How to Build SaaS Application with Data Isolation but No Run-time Isolation?

As you have already considered SaaS implementation, we recommend choosing the right SaaS architecture type so that all the hardware and automation costs you bear are well optimized. In case you are considering SaaS type 3 architecture for your startup, you are at the right place to get started.

Type 3 SaaS architecture is the right fit for cases that require data isolation but no isolation. In this type, different data stores are placed for different customers; however, the application is shared by all. Type 3 SaaS architecture is considered in businesses like e-mail marketing, content management systems (CMS), health care applications, and so on.

For your understanding of the type 3 SaaS architecture, I will take you through the example of an innovation management platform that I worked on for a fast-growing startup. The platform enabled industry leaders to tap into the collective intelligence of employees, partners, and customers, find the best ideas as well as make the right decisions. This platform drove innovation through the following-

  1. Employee engagement: Making ideation a part of daily lives and creating a culture of innovation
  2. Continuous improvement: Supercharging project discovery by tapping into the employee bases
  3. Product development: Creating the next big thing with people who understand the business well
  4. Customer experience: Engaging a wider workforce and reduce customer churn

It also enabled enterprises to manage the entire idea lifecycle, right from coming up with an idea of delivering impact at scale. Now, you must be wondering why we chose SaaS for this platform? The platform had to be made available as a service to enterprises with an option of subscription for a limited period. Herein, hosting/licensing wasn’t a viable option, taking into consideration the cost of deployment, data privacy concerns, and the IT assistance involved. We picked SaaS Type 3 deployment model for this platform wherein we could keep data of each enterprise isolated from others, all the while retaining flexibility of application runtime being shared.

Fig 1- Saas Type 3 Architecture

How Our Decision Paid Off?

Having the right foresight and visualization is the key to good decision-making. That worked well in this case too, when we could rightly foresee the results of deploying SaaS type 3 on this platform. This decision helped us address the areas mentioned below-

  • Data isolation
  • Server utilization, wherein we kept application runtime shared to use the server capacity optimally
  • Separating application runtime to the high-end server for some high-paying customers

What are the Challenges We Overcame and How?

Isolating data for each customer by having separate databases, all the while sharing a common application runtime, was a critical challenge that we tackled. In other words, we got one application runtime capable of supporting multiple databases for customer-specific data management. Along with this, we also had to accelerate customer onboarding in less time. This implies the deployment process should be automated enough to handle database provisioning, disaster recovery, and rollout of new versions.

Supporting Multiple Database Connections

As explained earlier, we had one application runtime that supported multiple databases for the respective customers. In our case, we had built N-number of Tomcat web applications deployed in one server that shared the common application runtime. This way, every customer had access to an independent application, with every application having its connection pool to manage connections. However, a plan of merging these deployments to one application is underway, so that we don’t have to run duplicate processes.

Faster Customer Onboarding

We brought down the customer onboarding time by automating the database creation with templatized data using Chef scripts. Apart from the database creation, it was also essential to set up a backup-recovery process and failover & load balancing for the application, which we could achieve by using the cloud solutions and Chef scripts.

Effective Disaster Recovery

As the solution helps in innovation management, the data was highly critical to our customers. This implied that our system should be able to weather any unexpected disasters and unforeseen accidents. To handle this, we had deployed the application & database across multiple availability zones that ensured timely updation of application and copies of the database whenever the primary DS is down.

Automated Deployments

For a new version rollout, along with the application deployment, we had to deploy a new version of the database or upgrade the existing version for each customer. However, with one-click deployment automation that we had in place, we could safely upgrade all customer applications to the new version all the while ensuring the existence of a recent backup in case of a rollback.

Utilizing Hardware

As we had an isolated database for each tenant, we had to spin up multiple DB servers for each of them, and this was more of a requirement rather than a choice. But since the application runtime can be shared, we had options of hosting it in a single server depending on the usage. By grouping customers based on utilization, we could reduce the number of servers and, in turn, accelerate the usage.

How did we Ensure Security?

As stated earlier, we isolated data for each customer by having a separate database all the while sharing a common application runtime. This came with the additional baggage of securing the application runtime that would restrict the urge of end-users to access other end-users’ data points. How did we implement this? Here’s how-

  • Maintaining separate configuration keys for each customer and rotating them on every release
  • Preserving encryption keys of databases fields for each customer and rotating them on every release

Apart from that, there were many other security compliances we had to follow-

  • Our product was independently audited on an annual basis against a rigid set of SOC 2 controls
  • We have an open policy that allows our customers to perform penetration tests of our service
  • Our production environment is protected by a robust network infrastructure that provides a highly secured environment for all customer data
  • Data in transit is over HTTPS only and is encrypted with the TLS v1.2 protocol. User data, including login information, is always sent through encrypted channels
  • The hosting environment is a single isolated database and application components that ensure segregation, privacy, and security isolation in a multi-tenant physical hosting model. Instead of storing user data on backup media, we rely on full backups that are shipped to a physically different co-location site
  • Customer instances, including data, are hosted in geographically disparate data centers. Customers may choose the location to host their data based on the corporate location or user base location to minimize latency
  • We support Single Sign On (“SSO”), using the Security Assertion Markup Language (“SAML 2.0”). This allows network users to access our application without having to log in separately, with authentication federated from Active Directory
  • An automated process deletes customer data 30 days after the end of the customer’s term. Data can also be terminated immediately depending on the contract terms of the agreement.


Despite the above challenges, this model helped us live up to the promise made to the customer, i.e. ideas across enterprises remain isolated and high-security compliance remains ensured for every customer.

Distributed transactions are Not Micro-services

(A Quick Note for the Readers- This is purely an opinion-based article distilled out of my experiences)

I’ve been a part of many Architecture-based discussions, reviews, and implementations, and have shipped many microservices’ based systems to the production. I pretty much agree with the ‘Monolith first’ approach of Martin Fowler. However, I’ve seen many people go in the opposite direction and justifying the pre-mature optimization, which can lead to an unstable and chaotic system.

It’s highly important to understand if you are building microservices just for the purpose of distributed transactions, you’re going to land onto great trouble.

What is a Distributed System?

Let’s go by an example, in an Ecommerce app this will be the order flow in a monolithic version

In Microservice version, the same thing will be like this

In this version, the transaction is dived into two separate transactions by two services and now the atomicity needs to be managed by the API controller.

You need to avoid distributed transactions while building microservices. If you’re spawning your transactions in multiple microservices or calling multiple rest APIs or PUB/SUB, which can be easily done with in-process single service and a single database, then there’s a high chance that you’re doing it the wrong way.

Challenges in Using Microservices to Implement Distributed Transactions

  1. Chaotic testing, as compared to the ones in in-process transactions. It’s really hard to stabilize features written in a distributed fashion, as you not only test happy cases but also cases like service down, timeout, and error handlings of rest APIs.
  2. Unstable and intermittent bugs, which you will start seeing in production.
  3. Sequencing, in real word everyone needs some kind of sequencing when it comes to transactions, but it’s not easy to stabilize a system that is asynchronous (like node.js) and distributed.
  4. Performance, which is a big one and is a by-product of premature optimization. Initially, your transactions might not handle big jsons, but might appear later, and in-process where the same memory is accessible to subsequent codes and transactions, in microservice world where a transaction is distributed it could be painful (now every microservices will load data, serialize and deserialize or same large Db calls multiple time).
  5. Refactoring, every time you make changes in the design level, you will end up having new problems (1-3), which leads to engineering team a mod “resistant to change”
  6. Slow features, the whole concept behind microservices is to “build and deploy features independently and fast” but now you may need to build, test, stabilize, and deploy bunch of services and it will slow down
  7. Unoptimized hardware utilization, there is a high chance that most of the hardware will be under utilized and you might be start shipping many services in same container or same VMs, resulting in high I/O. Suddenly if some big request comes into the system, it could make it go hyper utilized, which will then make you separate that component out, further making the system under-utilized if these kind of requests are not coming anymore, and now there will be a team to handle this infinite oscillation that could have been avoided.

Do’s & Dont’s for Building Microservices from Scratch

  1. Don’t think of microservices as an exercise similar to refactoration of code in different directories. If some code files seem to be logically separated, it’s always a good idea to separate them in one package, however, to create a microservice herein is nothing but premature optimization.
  2. If you need to call rest APIs to complete a request, think twice about it (I would rather recommend to avoid it completely). Same goes for a messaging-based system before creating new producers and consumers, try not to have them at all.
  3. Always focus on different user experiences and their diverse scaling requirements, like for e-commerce vendors APIs are bulky and transactional, as compared to consumer API, it’s a good way of identifying components
  4. Avoid integration tests (yes, you just heard it right ). If you create 10 services and write hundreds of integration tests, you’re creating A chaotic situation altogether. Instead, start with 2-4 services, write hundreds of unit tests, and write 5 integration tests, which I’m sure you won’t regret later.
  5. Consider batch processing, as this design would turn out to be good in performance and less chaotic. For instance, let’s say in e-commerce, you have products in both vendor and consumer databases. Herein, instead of writing distributed transactions to make new products in both the DBs, you can first write only in the vendor DB and run batch processes to pick 100 new products and insert them into consumer DB.
  6. Consider setup auditor or create your own, so that you’ll easily be able to debug and fix an atomic operation when it fails instead of looking into different databases. In case you wish to reduce your late-night intermittent bug fixes, set this early on and use in all the places. So, the solution could be like this 
  7. I would recommend to overlook synchronizing. I have seen many people trying to use this as a way to stabilize the ecosystem, but it introduces new problems (like time outs) then fixing. In the end, services should remain scalable.
  8. Don’t partition your database early, if possible every microservice should have its own database but not all of them need databases. You should create persistent microservices first, and then try to use them inside other microservices. If your most/all microservices are connecting to Databases then it’s a design smell, scale the persistent microservices horizontally with more instances
  9. Don’t create a new git repository for new microservices, first create well unit tested core components, reuse (don’t copy) them in high level components, and from a single repository you might be able to spawn many microservices. Every time you need same code in another repository don’t copy them, rather move it to core component, write super quick unit test, and reuse in all microservices.
  10. Async programming , this can be a real problem if transactions are written in proper sequence handling . there might be some fire and forget scenario could have come which might not impact in normal scenario but in regress or heavy load these fire and forget might not even exected ) lead to inconsistent scenarios.

Check above example here developer thought calling sendOTP Service don’t need to synchronize and did classic “fire and forget”, now in normal testing and low load OTP will be send always but in heavy load sometime sendOTP would not get chance to execute .

Microservices Out of Monolithic: A Cheatsheet

  1. 1-5 of the above-mentioned are applicable
  2. Forget big-bang, you have a stable production system (might not be scalable though)and have to still use 50-70% of existing system in new one.
  3. Start collecting data and figuring out pain points in the system, like tables, non-scalable APIs, performance bottlenecks, intermittent performance issues, and load testing results.
  4. Make a call over scaling by adding hardware vs optimization, however, there’s cost involved in both the cases and you’ll have to decide which is lower. Many a time it’s easier to add more nodes and solve a problem (optimizing the system might involve development and testing cost which might be way higher than just addng nodes).
  5. Consider using the incremental approach. For example, let’s say I’ve an ecommerce app that is monolith (vendor and consumer both), and I come to know that we will be scaling with more new vendors in the coming six months. The first intuition would be to re-architect, however, in case of incremental approach you will determine that your biggest request hit will be from consumer side and product search. The product catalogue will need to be refactored, so you will not change anything in the existing app and it will work as it is for all vendors APIs and consumer transactions. Only for the new problems you will be creating another microservice and another db, replicate the data using batch processing from primary DB, and redirect all search and product catalogue APIs to new microservice.
  6. Optimization, you’ll have to shift your key area of focus on optimizing problematic components (scaling with adding more hardware might not work here).
  7. Partition of your DB to fix problems (don’t ignore this). Many people out there might not agree to this but you need to fix the core design problems instead of adding a counter mechanism like caching.
  8. Don’t rush into new techs and tools, you should be using when you have enough expertise and readiness in your team. Always pick stable opensource small projects instead of the new, trendy library or framework promising too many things.

Still Distributed Transactions in Microservices? Here’s the Way Forward

  1. Compositions, if you think you should merge couple of microservices or integrate transactions in one service, it’s never late to do this exercise.
  2. Build consistent and useful audit for transactions, and make sure you always capture audits even your service gets timed out. A simple example of setting up elk stack, structured logs with transaction ids, entity ids and ability to define policies that will enable you to trace your failed transactions and fix them by data operation teams (this is supercritical). You need to enable them to fix these, if it comes to engineering team then your audit setup is failed)
  3. Redesign your process for chaos testing. Don’t test with hypothetical scenarios (like killing a service then see how other components behave), instead try to produce the situation or data or sequences which can kill or time out a service and then see how resiliency/retry works in other services.
  4. For new requirements, always do estimates, impact analysis, and build an action plan based on your testing time and not development time (since now you will spend most of the time testing).
  5. Integrate a circuit breaker in your ecosystem, so that you’ll be able to check whether all services- the ones going to participate in these transactions- are live and healthy. This way you can avoid half-cooked transactions big time even before starting the transactions.
  6. Adopt batch process, wherein you convert some of critical transactions in batch and offline to make the system more stable and consistent. For example, for the e-commerce example mentioned above, you can use the following-

Here you will still get scaling, isolation, and independent deployment but batch process will make it far more consistent.

  1. Don’t try to build two-phase commit, instead go for an arbitrator pattern which essentially supports resiliency, retry, error handling, timeout handling, and rollback. This is applicable for PUB-SUB as well, with this you don’t need to make every service robust and just have to ensure that arbitrator is capable of handling most of the scenarios.
  2. For performance, you can use IPC, memory sharing across processes, and TCP, if there are chatty microservices check for gRPC or websockets as an alternative of rest APIs.
  3. Configurations can become real nightmares if not handled properly. If your apps fail in production due to missing configuration and you are busy rolling back, fixing and redeploying, you would require something else here. It’s very hard to make every microservice configuration savy and you can never figure out all missing configurations before shipping to productions. So, follow this

Hard code à config files à Data bases à api à discovery

  1. Enable service discovery, in case if you haven’t.


You can use microservices but must also have the pitfalls in the back of your mind. Avoid premature optimizations, and your target should be building stable and scalable products instead of building microservices. Monolith is never bad, however, SOA is versatile and capable of measuring everything. You don’t require a system where everything is essentially microservices, rather a well-built system with combination of monoliths, microsevices, and SOAs can fly really high.

How to Choose the Right SaaS Architecture for Your Startup?

Having worked as a solution architect and designed multiple SaaS applications over the years, I could see most startups struggling to choose the right SaaS architecture for their product offering.  

In this article, I’ve compiled all my learnings into a cheat sheet to help startup founders, who’re looking to build SaaS applications,  make a pragmatic decision backed by proven facts and data 

How does SaaS Architecture Impact Pricing and Profitability? 

Customers are increasingly choosing the ‘pay as you go’ pricing modelas this model offers flexibility as compared to one-time pricing model. In order to enable ‘pay as you go’ for your customers, you need the right architecture to support it. When we say the right architecture, it should allow your startup to track usage of services and offer customers the flexibility of managing infrastructure as per their requirements.  

A poorly designed architecture creates limitations in setting the right pricing strategy for the offeringsthereby impacting the acquisition of new customers. On the other hand, a good architecture not only helps in setting the right pricing model but also accommodating special architecture-design requirements, such as scalability and customizability. For having a clear idea of pricing model before setting up SaaS architecture, a startup needs to get answers to these questions-  

  • How would your customers pay? 
  • For what services (computation and values) would the customers pay? 
  • How will the usage be measured and invoices be created for the customers? 

In a SaaS setup, costs incurred in managing operations impact the profitability to a large extent. The optimization of operational expenses involved in managing the SaaS model depends on three crucial factors – infrastructure cost, IT administration cost, and licensing cost. 

However, the bigger question is- how do you ensure that these costs are well-optimized and priced correctly? I’ve listed below a few examples to demonstrate the same 

Salesforce Online- Salesforce provides a lead management system for sales and marketing teams for enterprises. The online version uses cloud (so none of their customers needs to worry about hardware and IT procurement) and chargecustomers based on the size of sales and marketing teams (so that they don’t have to pay one-time high license cost). 

SQL Azure- SQL server is the industry leader when it comes to RDBMSand provides a hosted solution wherein customer needs to pay high license cost, hire a DBA for regulating backup, geographical replication, and disaster recovery (important for databases). But, Azure SQL is a cloud-based system that’s accessible online where you only pay for storage and IOPS, with rest is taken care of by Azure (cloud provider). 

WordPress – Every enterprise needs a content management system, and WordPress has been at the forefront of this. WordPress provides an online platform with white-labelled solutions, customization, and multiple integrations for their customers. WordPress collects customer usage data and charges on the basis of it. 

Why is it Important to Pick the Right SaaS Type 

This might a common question popping up in your mind. Let me explain with two different examples-  

Example 1- Let’s consider an instance where a startup introduces isolated application VMs (Virtual Machines) for all its customers. In the majority of cases, these boxes will remain under-utilized. With customers paying only for utilization, the startup could end up with huge losses.  

Example 2- Consider a second instance where all customers of the startup share the database servers & application servers and paying only for utilization. This is a fair game for the startup as all of the hardware and automation are being properly utilizedHowever, in case of sudden increase in the utilization of these servers by one of the customers, other customers might have face performance issues and unexpected breakdowns.  

When a startup starts building a SaaS application, it essentially bears the hardware and automation costs. Hence, it’s crucial to ensure that all of the above-mentioned costs are well optimized by picking the right SaaS architecture based on your offerings. Damage control is still possible in the above-mentioned examples but you will definitely lose a big share of time and opportunities 

What are Different Architecture Types of SaaS Applications? 

Type of SaaS Architecture


Type 4 (Doesn’t require data & runtime isolation) 

This is the most basic type of SaaS application. In this type, you assume all of your customers to grow uniformly, and accordingly, the customer ID is created. These customer IDs are added to all of the tables/collection and all customers share the database and application hardware.  

Type 3 (Requires data isolation but no runtime isolation) 

This is one-step advanced as compared to type 4. In this type, different data stores are put in place for different customers, however, the application is shared by all the customers.  

Type 2 (Requires data & runtime isolation on the cloud) 

This type involves separate applications and separate data stores for all customers. In this case, the cost of isolation is typically passed on to the customers. 

Type 1 (Requires data & runtime isolation, but not on the cloud) 

This type is a version of type 2 wherein customer wants data to be stored on their own network and not on the cloud. Herein, the customer still opts for pay as you go or pricing model that’s based on users/featureas per on-boarding.  

How to Pick the Right SaaS Type for your Startup? 

Depending on the type of industry and nature of data, a customer’s requirement for security and shareability varies. A startup can try to understand the needs of multiple customers, and refer to the flowchart given below to select the right SaaS architecture type- 

On a Final Note 

As the startup growsit isn’t easy to mold the existing architecture to accommodate demands from the growing user base. Hence, it’s always good to choose the right SaaS architecture at the start, so that you don’t lose out on business because of rigid architecture. 

5 Notions That Could Spoil Your Great Product Idea

You know you have an incredible idea with which you could disrupt the market. But to do that, you need first to transform it into a software product. And you’re possibly wondering what the starting point is, and at what stage you need to take to the market. You’re worried because you know of people who did not make an impact despite their great idea. These thoughts are but natural and every entrepreneur goes through them.

I’ve had the fortune of working with multiple founders at various stages of their product, from bootstrap to scale and even through rounds of Venture Capital Funding. And through the years of building products for entrepreneurs and conducting Ideation Workshops, I’ve come across some typical misconceptions about Product Bootstrapping I’d like to share. These notions could potentially spoil your fantastic product idea.

“I’ll marry two products to create a new one.” 

People tend to use multiple applications to address their different needs. And, you feel that your product would give them the best of two or more worlds. It seems quite apparent that people would gravitate towards your unified solution instead of multiple products to fulfil their needs. This is not a wrong notion. It has worked remarkably well in the space of bill payments and bookings. However, you must not underestimate the products you are trying to marry.

For example, a platform that combines Facebook and TikTok to form a third application might sound like a great idea theoretically. But users are already accustomed to these features that have evolved to perfection over years, therefore you need to set aside that kind of time and money for your application. If you take a step back, you might realize that this is not how your idea first took shape. It all possibly began by assessing a specific user need and providing a solution for that. Simply put, it’s essential to do one thing and do it well rather than offering a suite of features that individually pack no punch.

“I’ll launch only when my product is highly feature-rich.”

At every stage of product development, it might feel like what has been built is not enough, “We need more power, Scotty!”. Also, the realization that all funds have been exhausted in the process of developing the product and its several features is the worst nightmare for any founder. This is where the power of a Minimum Viable Product (MVP) kicks in. It is crucial to scope your MVP and take it to the market on time.

Here’s a quick cheat-sheet to channel your MVP scoping:

  • Identify the type of users that will use your system – the user personas (keep this to the bare minimum).
  • Assess what each persona’s most critical needs are.
  • Drive the features and user-flows based on the personas and their needs.
  • Decide if it needs to be a Mobile Application or a Responsive Web Application.
  • Get this roughly estimated as a function of UX and Engineering.

Now, here’s the hard part – you’ll realize that this is too much in terms of time and effort. You will have to go back and trim the scope. “Trimming” doesn’t necessarily mean cutting out features, but just a matter of simplifying the process. For example- as an admin, you could try and do a few backend tasks manually at the beginning instead of having a fully automated system in place. Once you have your well-scoped MVP that has your core value proposition at the heart of it, you can easily plan to take it to the market predictably.

“The market is large enough, and I don’t need a differentiator.”

Many a time, I’ve heard very compelling thoughts around the key numbers associated with a product – the user base. The math around this would start with a pretty vast target market, followed by something like – “even if we just had 1% of that as our market size, we’d be sorted”. These numbers might seem accurate, but users seldom start using a product just because it exists.

As I mentioned earlier, everything begins with a need. A product that has been built to address that need uniquely is the differentiator that will drive customer adoption. This differentiator will also go a long way in adding value to your sales and marketing efforts. Though your target market might be large, your product could end up having the effect of a pebble thrown into the sea. The differentiator is everything.

“I must have AI and ML handle most operations.”

Everyone is excited about Artificial Intelligence and Machine Learning. They are here to stay, no doubt. You know that the roadmap of your product includes automated capabilities that are beyond a simple rule-based engine. Isn’t it best to build that capability from day 1, so that when you hit the market, you arrive in style? Well, there are two scenarios, and you will have to think hard about which bucket you fall into.

The first bucket is a product that can provide the solution to the user’s need through a rule-based engine. However, over time, it would be better for the operations to scale if AI and ML programming is in place. An example of this would be a sophisticated decisioning engine. The second bucket is where your product’s core value proposition is based on AI and ML. For example, a system that uses advanced facial recognition to cross-reference a vast database for identifying crime suspects.

Did you spot the difference here? In one case, without AI and ML, there is no product, but in the other, you can build the MVP without AI and ML and, over time, introduce that capability. Remember, Machine Learning is experimental and is going to need a lot of data and time to get to production. You must prepare yourself from a cost and time perspective to include it in the get-go.

“I must pick a VC-Friendly tech-stack.”

It’s essential to ensure that you don’t eventually face a VC with an un-cool technology stack, right? Well, every programming paradigm is meant to be used in a particular way. Each one has its own advantages and disadvantages, and more importantly, a purpose. But there is nothing called un-cool tech, only an un-cool notion of looking down upontechnologies because they maybe a little old. Remember, with age comes maturity. It would be best to choose your tech-stack based on the nature of the platform.

For example, you’d need to look at the kind of client-server chatter you’re expecting, the nature of data – structured or unstructured, the intensity of server-side processing & IO operations, and the sort of activity you expect on the UI, to name a few. A great example of a “Tech-pick for the VC” is the Blockchain. Blockchain is an excellent distributed, immutable, and secure network and is advantageous in a host of use-cases. But it can’t be crammed in a product where it does not belong with claims of replacing the database. I know you’re smiling, but I’ve actually heard that one.

On a Final Note… 

I’d like to conclude with an altered dialogue from the film For Vendetta. “Beneath the hood of your software product, there is more than just technology and frameworks. Beneath the hood, there is an idea. And ideas are bullet-proof.” I hope this article was helpful in re-addressing and fortifying your core idea. I wish you all the very best in transforming your idea into a successful product.

Does Team Ramp Up Guarantee Increased Feature Velocity?

When startups move from early to the growth stage, their priorities change by a significant degree. Among all others, feature velocity stands out as a priority and plays a crucial role in their pursuit of growth.

The founder of a seed-funded startup that I was working with, which recently raised its series A round, asked me to double the engineering team to release new functionalities in six months. However, is that the only relevant factor in reducing cycle time? To answer, I decided to address the ignored aspects behind this prevalent ‘misconception’.

Though resource capacity is one of the critical factors driving frequent releases to production, this alone can’t guarantee reduced cycle time. While building products for more than 16 startups, I’ve witnessed the transition from early to growth stage multiple times. Out of this experience, here are some crucial factors that I am sharing for startup founders to consider-

Pass On Clarity: The Top-Down Essential

One of the key essentials while ramping up teams to increase release velocity is the transfer of clarity from product teams to sprint teams. When a sprint starts with ambiguity or doesn’t have enough stories to start with, the sprint delivery ratio gets adversely affected. Vaguely-defined stories or mid-sprint inclusion of tasks slow down the pace, as a result, the sprint teams fail to deliver as planned.

A smooth sprint execution is the outcome of notable contributions coming from both the product and sprint teams. The product team can play its part by preparing and sharing the roadmap to the sprint team at least for the quarter so that the team can plan its deliverables accordingly. On the other hand, sprint team should keep pushing the product team to get a product backlog and groom it on a weekly/ biweekly basis to ensure focused delivery.

Automate to Release ‘Bug-Free’

“Efficiency is doing things right; effectiveness is doing the right things.” – Peter Drucker

When you think of automation, it is an example of the latter category. The moment feature development picks up speed, it is highly likely that production will break down unless the right processes are in place. If the product isn’t stable enough to handle new feature developments, your team spends more time fixing issues than building new features. Consequently, your engineering velocity comes down.

This is where CI/CD (continuous integration and continuous deployment) comes into the picture. Herein, exhaustive unit, integration, and automation test coverage ensure that whatever is shipped doesn’t break the system.

Don’t Just Build More, Else You Break More

Rework is a big productivity killer and could be a result of various factors such as vaguely-defined stories, lack of dev testing, lack of test coverage, and so on. Rework can eat up productivity as it would consume the time and efforts of QA engineers in testing and regressing, developers in debugging, and release managers in re-releasing. Slowing down a bit can help your team deliver faster and add value, as the effective speed is always highly rewarding than just the speed.

The philosophy of ‘first-time-right (FTR)’ helps all team members to align to a common goal- delivering robust and stable code in the first time itself. It’s always healthy to spend some extra time to determine the quality of code, rather than hurrying, and then getting stuck with rework. Some of the tried-and-tested ways to improve FTR ratio are regular backlog grooming, reiteration of stories, regular demos to product manager. Instead of just gathering requirements, the sprint team should be more focused on elucidating them to improve the FTR ratio.

Structure Your Team for Parallel Sprints

When your startup has a small product team, everyone mostly works on one or two features at the same time (generally applicable for a team of 4-6 members). However, as the expectation goes up to deliver multiple features, it’s highly recommended that you form multiple sub-teams having distinct focus areas. In this manner, every sub-team gets to run its sprint and define its roadmap.

As compared to one big team, smaller teams born out of a ‘logical separation’ framework are more effective and yield better results. Individual teams for microservices, different product lines, and various components are among a few examples of the ‘logical separation’ approach. During the restructuring, it’s always essential to include at least one member from the earlier core team in each of these sub-teams to maintain the DNA. Though cross-team coordination for deliveries entails an additional overhead, that’s a justified trade-off.

Track Feature Usage Along with Velocity

User experience is the most vital metric to measure the success of a new feature release. As you start delivering multiple features at speed, user experience often takes a backseat. When your product has limited features, the user interaction continues to be a smooth unbroken curve. However, when you start releasing new features, there is a high chance of users getting overwhelmed and their experience getting impacted.

To achieve better user adoption, tracking user engagement along with feature velocity continues to be the best way forward. While exhaustive user research is one proven way, other significant ones are rolling-out initially to selected users via feature flags, A-B testing, and tracking user journey (via amplitude or similar analytics tools) after every new release.

Don’t Lose Your Core Members

This might be a very commonly-ignored aspect yet stands firm as a very crucial one. Small teams don’t necessarily need processes and have a nimble structure, and everyone’s voice is heard. When these teams move up to a state where processes are set up and new engineering & functional members are added, healthy management is the only way to avoid chaos.

As your engineering team successfully scales up, a well-capable product team is essential to feed the engineering team continuously. A churn becomes inevitable when the team members have no significant work, but no startup would ever want to lose its core members. In this case, senior management holds the key to ensure good relationships with people and understand the dynamics well.

On a Final Note

The learnings that I’ve shared here are from the experience with multiple startups over the years. I expect it to be useful for startup founders, who already have a lot on their plate, in a way that they don’t end up reinventing the wheel.

How to Scale an Engineering Team in a Startup?

After working with startup founders closely across stages, I have realized that scaling is one aspect they are often concerned with. During this journey, leading engineering teams from the front led me to conclude that scaling an engineering team can become a bottleneck to growth.

I want to share my scaling experience through an example of a leading mobile advertising platforms where I had to ensure that engineering does not become a roadblock. We scaled the product to handle the transaction from 1 million to 80 billion impressions per day with the help of an engineering team that had to scale from 2 to over 100 members.

Translate business goals to technology deliverables

I was part of quarterly business planning meetings where we used to discuss quarterly roadmap and feedback from the customers. During one such meeting, one of the co-founders spoke about an idea to enable revenue stream through display ads in a non-internet environment.

Though the complete feature details were not frozen, I could see this feature as one of the critical elements in the next release. We anticipated the need to hire an additional Android and iOS developers required to deliver this functionality much in advance. Apart from identifying & acquiring new skill sets, it was essential to implement this change in the core product to handle such cases.

I feel that if you nail down the art of translating business needs to technology deliverables, you will get extra time for execution.

Polyglot engineers

As a precondition to close a deal with a premium publisher, we had to develop a feature to cap the frequency of advertising. The core product was written in PHP, and we didn’t have a dedicated PHP person at that time. We had no time to acquire it since it was an on-demand request. So, one of the senior Java developers with a fundamental understanding of PHP took it up as a challenge and delivered the feature on time which helped us in acquiring the premium publisher.

You can have the best recruiters on your side, but even they cannot salvage you in such situations.

Challenge team members

Our attrition rate for the team was Zero for 3-4 years. Beyond aligning team members to product vision & mission, keeping them challenged in their work helped us achieve this feat. There was a strong focus on quality and value addition to product for which one had to think beyond just the day-to-day deliverables. Staying on the top of technology helped us to provide innovative solutions to hard problems. This also kept team members engaged. Using Big Data technologies to solve analytics problem & real-time campaign pacing engine turned out to be game-changer for us. Often rotating people within the team helped in productivity and faster technological growth.

Top-down induction process

Often, organizations follow a bottom-up approach for induction. However, in our case, we followed a top-down approach where the leads are involved in explaining business goals and metrics to the new team members. They are closely involved in the induction process. Along with a top-down induction process, an evolving team structure is equally important. Initially, we started with two team members and gradually scaled to form sub-teams such as the engineering team and the customer focus team.

After scaling an engineering team for this startup, I was able to apply the same guidelines to scale multiple teams for other startups successfully.

PhD and Data Science: Challenges and Opportunities with Startups

This article attempts to summarize potential expectation mismatches graduates with a PhD degree experience while working in the software industry, specifically in startups having a Machine Learning and Artificial Intelligence (AI) focus. Some of the opinions expressed here are based on personal experiences and recent attempts to hire engineers with a PhD degree.

Startups Embrace the Artificial Intelligence Revolution

In recent times, Startups have been churning out complex products involving techniques that have mathematical underpinnings. The industry loosely refers to these techniques as AI. Purists, however, would argue that prevalent techniques in natural language processing (NLP), computer vision, speech processing and image processing that depend on statistical algorithms and machine learning algorithms are not really AI.

A decade ago, a PhD with an AI background in optimization, machine learning, computer vision, NLP, or pattern recognition was hardly considered for employment in Startups. However, over the last few years, there has been a remarkable change in the way these PhDs are being perceived, given the huge demand for Data Scientists in most spheres. And for the same reason, a PhD in computer science with a sprinkling of mathematics in their course work or in their thesis, seem to have an edge for the role of a Data Scientist today.

Hiring Demand for PhDs Soars

Given that a lot of startups are throwing in AI into their vision, it is natural that the engineering VPs driving these products wish to hire people who understand these data-driven or machine learning or AI algorithms in the context of Data (images, speech, telemetry, text etc) and Domain (FinTech, Wireless Networking, BioMedical imaging etc). Most of the products do not lend to black-box machine learning. The ideas are so unique that one needs to dissect known machine learning algorithms, modify them, create newer techniques, and combine multiple algorithms in order to deliver the final result. As these algorithms have mathematical foundations, people with a PhD degree in mathematical subjects are in huge demand. They are expected to not only understand the inner working of these algorithms, but are also expected to create new algorithms in the context of business problem, data platform and available data. Further, exploratory work is expected to be completed within time limits with the end result being software that can go to production.

Despite the requirement for PhDs with mathematical backgrounds, the number hired in startups is still significantly lower as full-stack developers are preferred over those with a PhD degree. One of the reasons could be the skepticism in the minds of engineering VPs with regard to a PhD’s capability to deliver “productizable real-world solutions”. With so much open-source software available, decision makers pressed for time think that integrating available algorithms into the product is a practical option, and full-stack engineers are adept at it.

However, if open-source could solve an out-of-the box problem, then every startup would end up building a similar product there by cannibalizing each other. Thankfully, many startups have a clear vision to be market leaders and are solving problems where critical thinking and grass-roots innovation are required in order to build the product. Therein lies a huge opportunity for graduates with a PhD, provided they are hands-on.

What does being hands-on entail?

It is the ability to convert theory into working code that powers the product feature. However, to power a product feature there are many steps to be followed and engineering practices to be adopted. In Startups, the role of a PhD is not limited to finding the solution to a problem. The PhD is expected to (A) build the solution in a working form, (B) adapt the solution to the platform and architecture (C) work with other engineers to get the solution into production (D) follow programming best practices (E) determine the solution approach within a short time span (F) find a solution that can be incrementally improved upon.

Note to fresh Graduates

Many fresh graduates on completion of their PhD believe that their thesis work is what the industry is currently looking for. I believed the same many years ago. However, barring a few cases, the PhD thesis has no direct relevance to the immediate needs of the industry. What perhaps is relevant is the training that one undergoes during the PhD programme. One needs to look at the 5+ years spent on a PhD thesis as a journey involving (a) learning and building domain knowledge (b) exploration (c) taking risks (d) making mistakes, and changing course when one hits a roadblock. Organizations frequently look for experiences that demonstrate the following

  • ability to identify the heart of a complex problem
  • ability to dissect a problem into smaller parts
  • ability to practically implement a solution for parts that stitch themselves into the whole
  • ability to improvise on a solution
  • ability to use knowledge in one domain to a different domain
  • ability to build a minimum viable solution as quickly as possible
  • ability to re-skill oneself

PhD hires are expected to display aptitude for incremental product feature rollout pretty much like the product development engineers. Designing a solution for incremental rollout with improving quality and accuracy is as much a skill as it is an attitude. One needs to develop the skill to dissect a mathematical approach into parts such that an approximate solution can be built within the shortest time.


PhDs have to choose between two worlds. One is an engineering world requiring product development skills and an execution mindset. The other is an academic world that provides research opportunities and a career in teaching.

The expectations in these two worlds are very different. Those wishing to enter the engineering world of startups need to be aware of the expectations in a product development setting. PhDs who want to work as individual contributors have an excellent opportunity for a long career in startups, even if it means moving between startups. They also have an opportunity to grow with the startup and move into leadership/execution roles if interested.

However, caution is prudent. Not all startups can be successful, but working in startups leads to the development of varied skills that other startups prefer in one’s resume!


Is building a startup like making a movie?

I work at Talentica converting my start-up customer’s ideas into products. I am a Senior Development Manager and have the goodwill of working with highly functional teams and amazing clients to create these products. Also, I love films, and a few years back, a group of friends and I decided to try our hands and Video Production. We agreed as a team that we would work on every element of the process – storyboarding, scripting, directing, filming, editing, mixing, marketing … the works!


Like any two processes that lead to creating something, there are similarities on the surface between Product & Video creation. Both start with an initial idea, this idea is then grown to become a full-fledged concept, then comes planning and execution. Through the journey, multiple feedback from several people is continuously sought and factored back into the execution process. Once the end-product is near completion, marketing activities begin. Eventually, the Product or Video is presented to the end consumers.


I, for one, am a filmmaking noob but thankfully my friends were more adept. However, I am experienced with music production. We worked on a whole bunch of short videos. Eventually, we saw success with a video we called “Indian Nod: Explained”. Through the course of creating videos, we learned that several factors lead to success. Amongst many, one of them emerged to be extremely crucial – a unified vision.


Let’s take a glance at a famous scene from Tarantino’s movie Inglourious Basterds – the one where a group of Undercover Intelligence Corps ends up playing a drinking card game. For those that haven’t watched the film, the situation is this – the scene is set in a restaurant where the Undercover Intelligence Corps are plotting to assassinate Hitler. Because of their shaky German accent, they attract the attention of a Nazi German Sargent who joins the group and subtly coerces them to play the card game. What is fantastic about this sequence is that it is 15 minutes long, and purely dialogue driven. But the tension it arouses puts you at the edge of your seat biting whatever is left after a quarter hour of nail-biting. The viewers know it in their bones that it is not going to end well.


Like any film, this scene had a solid team behind it starting with the director and writers, the actors, and the on-set crew. Then the footage was worked upon by multiple groups – editing, sound, production, etc. Now, many of these tasks are highly technical. It needs a sound understanding of the technology and tools. A film producer can put together the best team for his movie, but the critical factor that brings about success is that each and every person must align with is the final outcome – that ‘tension’ the scene needs to create. If the end-result is understood only by the writers and directors, it will result in failure. I’m sure you’ve come across this – “…the concept was nice, but somehow the movie didn’t come together”. Across all functions of the movie-making process, every player is vital and needs to be wired to that outcome.


This is no different from the thing that leads to successful products. The founders alone can’t be the ones with the vision for their product. The end objective should be sharp and clear in the minds of everyone working to create the product, business and technical folks alike. The vision must cut through the various layers – founders, product owners, UX, developers, QA, dev-ops, sales, marketing, et al. A sound process that can enable this a vital catalyst that ensures a great concept can come together to create a world-class product.

How to prepare for GDPR Compliance

What is GDPR?

General Data Protection Regulation (GDPR) requires businesses to protect personal data of European Union (EU) countries’ citizens. Organizations that collect and/or process data from the EU region must comply with the regulation by May 25, 2018.

What data are we talking about?

GDPR is here to protect the privacy of Personally Identifiable Information (PII) such as:

  • Name and address of a user
  • Web information like location, IP address, cookie data
  • Biometrics
  • Political opinions
  • Device IDs like IDFA, AAID

Who is affected?

All organizations that store or process personal information about EU citizens (no matter where on the globe they are), even if they do not have a business presence within the EU must comply with GDPR.

What if an organization is not GDPR compliant?

If any organization is not GDPR compliant by May 25, 2018, then the regulation attracts a penalty of up to €20 million or 4% of your global annual turnover, whichever is higher.

How do you prepare for GDPR?

The following checklist can help you comply with GDPR:

  • Identify if your customer’s product deals with EU data.
    NOTE: The common approach that most businesses are taking across industries is to proactively reach out to their entire user base and ask for their users’ explicit consent. That is a much easier and the most recommended thing to do. But, if for some reason this is detrimental to your business directly or indirectly, identify your available data.
  • If EU data is involved, identify the parameters involved. For example device IDs, email IDs, IP address.
  • Assess whether the data is really required for business. If the data is not required, immediately delete all such data and update your product to stop receiving such information.
  • If any data is essential, find a way to replace the data. For example, replace essential PII data with unique tokens or encrypt the data such that the token or encryption cannot be reversed to identify the person.
  • If you have been storing any PII data, update the data retrospectively.
  • Ensure GDPR compliance of all the entities involved in your business workflow.
  • In case of data breach, notify your data regulators within 72 hours of the breach.
  • Have a provision in your product to facilitate easy capturing of user preferences. This lets the user decide if they want to know the information your product captures, how is it used, and if they want your product to delete or restrict processing of their information.

What has Talentica done for GDPR compliance?

We understand that our customers are from both B2B and B2C scenarios. It is important for us to create tools or techniques for our customers to achieve GDPR compliance. We have been working to ensure all our customers are GDPR compliant.

Production-deployed customers:

For customers whose products are already in production, we perform the following:

  • Record Consent: Before storing any personal user data, we seek the user’s consent via consent forms and APIs.
  • Compliance Audit: We identify whether any users are in the EU region and whether they have provided consent for data sharing.
  • Consent Record Logs: We store an audit trail of when, where and how the consent was given. The logs include the link, text and a screenshot of the consent form.
  • Data Pseudonymization: We associate unique pseudo tokens for key personal identifiers like email, name and contact information so that there is no direct association with any personal information.
  • Data Encryption: As an alternative to data psedonymization, we encrypt all the personal information. Encryption also helps in unfortunate situations like data breach or hacks and cannot be used to re-identify a person.
  • Data Export: Our customers’ users can request an export of all data related to them. We notify customers about such requests and extract the required information securely.
  • Data Deletion: If any of our customer’s users ask for their data be forgotten, we purge the related data. We also disassociate any data pseudonymization for that user.
    NOTE: GDPR Law states that you should finish processing such requests within 45 days.
  • Thirdparty Compliance: We ensure our customers’ third-party integrations providers are GDPR compliant and they send us their user’s consent forms. If not, we urge them to comply with GDPR. If nothing works, we disassociate ourselves from such providers.

Infrastructure management:

For customers whose infrastructure is maintained by us, we perform the following:

  • Network Isolation: We isolate the infrastructure for EU users on the basis of the GDPR guidelines and setup the servers in EU region itself.


GDPR is unavoidable for anyone working with data about EU citizens. Taking care of this personal data is a responsibility that we share with our customers. A lot of the tools described here are also good practices that we should all be following irrespective of GDPR. By taking good measures, we can all ensure that our customer’s data privacy is fool-proof.