Building a Successful MVP: Challenges, Mistakes, Testing, and Best Practices

Why MVP Matters

The main goal of any MVP (Minimum Viable Product) is to launch a solution to the market quickly, reach a wide audience, and attract early customers. The key factor here is speed to market — the faster you validate your idea, the faster you can iterate or pivot.

But an MVP still needs to be high-quality. Users visiting your website shouldn’t see broken layouts, typos, or critical bugs. First impressions are everything — if the experience is poor, most users won’t return.

This applies equally to internal systems. A user should be able to complete all core actions without friction:

• Register an account

• Log in

• View listings or find a specialist

• See CRM integration in action

• Receive an SMS or push notification

• etc.

All core user flows must work smoothly — especially when it comes to payments. A payment-related bug in an MVP can be devastating. It undermines trust and instantly kills conversions.

Of course, some imperfections or manual processes can be tolerated in the early stages. But functionality must be reliable.

That’s why an MVP should focus on a minimal set of essential, viable features. You’re delivering a lean but polished product that allows the business to test hypotheses or grab a slice of the market — without burning out the team with 120-hour workweeks.

💡 According to CB Insights, 35% of startups fail because there's no market need. An MVP helps avoid this by validating demand early with minimal investment.

⚙️ Companies like Dropbox and Airbnb started as MVPs. Their first versions were far from perfect, but they worked — and more importantly, they proved that real users wanted the solution.

Challenges Your Team Will Face When Building an MVP

1. Unclear Requirements at the Start

At the beginning of most projects, clients usually don’t have a clear understanding of how the product should function. And this isn’t about the design — it’s about business logic. How will payments work? What sections are essential? Which parts of the initial scope actually matter?

Almost always, important nuances are missed in the early stages, and as a result, the final scope expands significantly.

This happens because the business evolves. Initial ideas often don’t make it to the final product unchanged — they grow, shift in priority, or get replaced entirely. Sometimes a feature the team spent a full week developing becomes obsolete. And sometimes a previously “non-essential” feature becomes critical for launch.

2. Mistakes in Integration and Data Modeling

Decisions made early in the project tend to stay with it for a long time. Changing the object model later is difficult — there’s often no time to rewrite every service that depends on it.

The same goes for system architecture. If you make a mistake at this level, you're likely stuck with it until delivery.

3. Hidden Infrastructure Pitfalls

There are countless non-obvious issues that can derail development:

• The database you chose may not comply with company policies

• Sanctions might prevent using certain tools or platforms

• The server infrastructure may be too weak for your needs

Or, like in one of our Kazakhstan-based projects, the cloud provider didn’t support Kubernetes — so we had to help them test and deploy it manually. That cost us two extra weeks.

These kinds of issues often lead to architectural compromises that hurt the system in the long run.

4. Architecture as a Critical Risk Area

Architecture might be the single most important factor in your project’s success. It affects everything:

• What database you use

• Who the message broker is

• How services will communicate

• How your modules are structured

Any mistake at this level multiplies the number of hours you’ll spend later debugging and refactoring.

Especially when it comes to the core of your system — like MDM (Master Data Management), IDM (Identity Management), or other shared services. These are foundational, deeply integrated parts of your product. Bugs in core modules can cause cascading failures across the entire system.

5. Developers Should Care, Too

Many of these problems sound like issues for managers, team leads, or architects. And while that’s partially true, they directly impact developers down the line.

That’s why it’s crucial for every team member — not just the leadership — to understand how early decisions shape future complexity.

🔍 Fact: According to the Standish Group, only 29% of software projects are considered successful. Poor initial planning and overcomplexity are among the top reasons projects fail.

🧠 Pro Tip: Developer feedback in early planning is invaluable — it often helps reduce scope without compromising value.

Common Mistakes When Building an MVP

1. Poor Scope Planning

Here’s a real example from our practice: At the beginning of a project, we agreed with the client that the feedback feature wasn't critical at launch — the platform wasn’t expected to get much traffic early on. So we kept it simple: a form to rate a session and leave a comment.

Later, we made the second mistake.

2. Failing to Lock the Scope

Despite being marked as low priority, the feedback system was expanded before the MVP was even released. It grew into a fully-featured module with multiple feedback options, checkboxes for different issues, and more.

Each small task seemed “quick” — But let’s break it down:

• Dev: 2 hours

• QA: 30 minutes

• Frontend tweaks: 1–1.5 hours

• Team lead validation: another 1–1.5 hours

That “30-minute task” consumed 4–6 hours in reality. Multiply that by 30 similar “quick” features, and suddenly you’ve lost weeks of development time.

3. Bad Architecture Decisions

Choosing the wrong architecture for an MVP can be catastrophic. Overengineering leads to unnecessary complexity, while underengineering results in poor scalability and maintainability. Either way, you lose.

🧠 Reminder: MVP ≠ Hackathon. The system must be simple, but still structurally sound.

4. Wasting Time on the Wrong Type of Specs

Some teams spend days writing detailed specifications for a simple feature — wasting time. Others skip specs altogether — even when a feature affects critical workflows in other services.

Both extremes are harmful. Inconsistent data and misunderstandings are common outcomes.

What You Can Influence as a Developer Out of all these risks, the final scope and its clarity are the two areas you can directly impact.

Every developer is a high-level thinker, not just a code machine. Your input matters.

Don’t be afraid to tell your team lead that some services can be dropped from the MVP

Or that it’s okay to ship something simpler now and refactor later

Because what truly matters is:

✅ Build something that works ✅ Ship it fast 🛠 Then improve

📊 Fact: A study by CB Insights found that 42% of startups fail because they build a product no one actually needs. MVPs are meant to test assumptions — not to be perfect from day one.

How We Build Automated Tests in a Microservice Architecture At AGIMA, we strive to instill a culture of automated testing in every developer. Nearly all of our Python projects have test coverage — especially on the backend.

For Django, we use pytest-django, which provides excellent tooling for working with databases. It integrates deeply with Django’s ORM and allows for easy handling of transactions and running tests in parallel using xdist.

But when it comes to FastAPI, Flask, or aiohttp, things are not that straightforward. These frameworks don’t impose any architecture or tools, so we have to build everything from scratch. Inspired by Django’s experience, we created our own test framework with the following features:

• Automatic creation of a test database

• Migration management (applying and rolling back)

• Transaction handling per test

• Database cloning per xdist worker

Handy JWT token generators with customizable roles and permissions

Building such a framework took time, but the benefits were huge: writing tests became much faster, TDD became more realistic, and onboarding junior developers got easier. Even less experienced engineers can write quality tests just by following examples.

Plus, debugging got way faster, since reproducing bugs in a test is often easier than doing it manually via Postman.

How We Test Microservices Our core testing strategies:

🧪 Unit Tests for Core Functions and Classes These ensure that small changes won’t break the entire system, especially if they touch critical modules.

🌐 API Endpoint Tests By testing endpoints, we’re effectively testing multiple layers of the application: routing, validation, business logic, and database access.

This is especially helpful when there are no resources to write unit tests for each individual layer.

🔁 Deterministic Input Data We aim for fully controlled input values so that tests are consistent and easy to reproduce.

✅ Comparison with Expected Output We compare the HTTP status and the full JSON response. Even minor changes in the structure or content will cause the test to fail — which is exactly what we want.

About Mocks We prefer to mock external service responses, not internal functions. This helps verify how our clients behave under various conditions — timeouts, 500 errors, malformed data, etc.

A typical test looks like this:

• Load fixtures

• Mock all external calls

• Simulate a request (e.g., creating a contract)

• Compare the response with a golden reference

Reusable Tests We Copy Between Projects We have a set of standardized tests we reuse across services:

• Healthcheck endpoint tests

• Debug info endpoint tests

• Special endpoints that always fail (to test Sentry integration)

• Migration step tests (inspired by Alexander Vasin from Yandex): apply one migration, roll it back, apply two, roll back, etc.

• Permission tests to validate access control across endpoints

Access Control Testing — A Must-Have We built custom modules that introspect Django/FastAPI applications and extract all existing endpoints and HTTP methods.

For each endpoint, we test for:

Missing JWT (should return 401)

Incorrect auth header format

Expired or invalid JWT (wrong signature, etc.)

Missing permissions (should return 403)

How We Build Automated Tests in a Microservice Architecture

At UnciaSoft, we strive to instill a culture of automated testing in every developer. Nearly all of our Python projects have test coverage — especially on the backend.

For Django, we use pytest-django, which provides excellent tooling for working with databases. It integrates deeply with Django’s ORM and allows for easy handling of transactions and running tests in parallel using xdist.

But when it comes to FastAPI, Flask, or aiohttp, things are not that straightforward. These frameworks don’t impose any architecture or tools, so we have to build everything from scratch. Inspired by Django’s experience, we created our own test framework with the following features:

Automatic creation of a test database

Migration management (applying and rolling back)

Transaction handling per test

Database cloning per xdist worker

Handy JWT token generators with customizable roles and permissions

Building such a framework took time, but the benefits were huge: writing tests became much faster, TDD became more realistic, and onboarding junior developers got easier. Even less experienced engineers can write quality tests just by following examples.

Plus, debugging got way faster, since reproducing bugs in a test is often easier than doing it manually via Postman.

How We Test Microservices

Our core testing strategies:

🧪 Unit Tests for Core Functions and Classes

These ensure that small changes won’t break the entire system, especially if they touch critical modules.

🌐 API Endpoint Tests

By testing endpoints, we’re effectively testing multiple layers of the application: routing, validation, business logic, and database access.

This is especially helpful when there are no resources to write unit tests for each individual layer.

🔁 Deterministic Input Data

We aim for fully controlled input values so that tests are consistent and easy to reproduce.

✅ Comparison with Expected Output

We compare the HTTP status and the full JSON response. Even minor changes in the structure or content will cause the test to fail — which is exactly what we want.

About Mocks

We prefer to mock external service responses, not internal functions. This helps verify how our clients behave under various conditions — timeouts, 500 errors, malformed data, etc.

A typical test looks like this:

Load fixtures

Mock all external calls

Simulate a request (e.g., creating a contract)

Compare the response with a golden reference

Reusable Tests We Copy Between Projects

We have a set of standardized tests we reuse across services:

Healthcheck endpoint tests

Debug info endpoint tests

Special endpoints that always fail (to test Sentry integration)

Migration step tests (inspired by Alexander Vasin from Yandex): apply one migration, roll it back, apply two, roll back, etc.

Permission tests to validate access control across endpoints

Access Control Testing — A Must-Have

We built custom modules that introspect Django/FastAPI applications and extract all existing endpoints and HTTP methods.

For each endpoint, we test for:

Missing JWT (should return 401)

Incorrect auth header format

Expired or invalid JWT (wrong signature, etc.)

Missing permissions (should return 403)

Small Thoughts

A solid test framework is an investment. It takes time to build, but without it, developers either avoid testing altogether or suffer through it. Once it’s in place, testing becomes a natural part of the process — and the product benefits from better speed, reliability, and confidence.

🧠 Fun fact: According to JetBrains research, over 60% of Python developers use Pytest — and for good reason: it’s flexible, fast, and scales well across projects.

Asynchronous Tasks

For inter-service asynchronous communication, we used RabbitMQ. In our case, both Celery and Dramatiq were viable options for background task processing. Initially, we settled on Celery. However, after noticing Dramatiq, we experimented with it for a while.

Key Differences Between Dramatiq and Celery

Windows Support: Dramatiq works on Windows.

Middleware: Dramatiq allows you to create custom middleware.

Code Clarity: Subjectively, the source code of Dramatiq is more understandable than that of Celery.

Code Reloading: Dramatiq supports reloading when the code changes.

Despite these advantages, in our environment neither tool performed particularly well. The main issue was that both tools don’t natively support asyncio. This becomes a significant problem when your code is written to work in an asynchronous manner but you have to run it from synchronous code.

While it’s possible to run them, we started encountering various elusive bugs when working with the database—sporadic transaction issues, phantom connection closures, etc. Additionally, it turned out that integrating logs in the required format with Celery wasn’t straightforward. Setting up proper error alerting in Sentry according to our business logic was also challenging. We didn’t want to send business exceptions to Sentry, only unexpected Python errors. Moreover, the constructs for running asynchronous code from synchronous contexts looked terribly convoluted.

Given all of these issues, our task code became monstrous, filled with hacks and hard-to-debug bugs. Consequently, we developed our own producer-consumer implementation using the Aiopika library. This new solution removed the hacks needed for running asynchronous code and introduced the ability to add custom middleware for the worker.

Now, since we can work natively with Python’s asynchronous features, our worker is capable of processing multiple tasks concurrently—much like what you’d expect from Celery or Dramatiq.

Evolving Our Communication Strategy

During development, we also reconsidered our approach to inter-service communication via the message broker. In the early stages of our MVP, producer services were aware of consumer services and directly sent events into each other’s queues. Initially, this worked well, but as the system grew, producers ended up knowing too much about consumers, and part of the business logic was inappropriately shifted to the moment when messages were sent.

We then adopted a new approach: events produced by the services became simply broadcasts. This means that producers no longer need to know about their consumers. Instead, consumer services decide for themselves whether they should subscribe to and process a given event based on their own business rules. Since one event can trigger many actions, we additionally implemented simple job classes to handle these triggers.

Recommendations

Covering every issue that arises in an MVP in a single article is impossible. However, here are a few key points you should pay special attention to.

1. Security Testing

Security issues are always a major concern. You need to continuously scan your code to prevent critical vulnerabilities from creeping in.

2. Load Testing

Load testing is absolutely essential. It’s hard to predict in advance how much load your system can handle. Often, issues only become apparent once the system exceeds a certain number of requests per second (RPS).

3. S3 Storage

In monolithic architectures, you can always manage files within a dedicated module. Typically, you need to check:

• File size limits

• Allowed file extensions

• Whether the file is executable

• Limits on the number of uploads

• Ensure files don’t become stale by cleaning the storage regularly

In a microservices environment, file management is handled by a dedicated microservice. This means you must aggregate all file-handling logic into that service, which adds overhead. You need to know which service uploaded the file, its metadata, etc. The file is directly uploaded to the S3 service, so the microservice that initiated the upload (e.g., for photos, DOCX, Excel files, etc.) only knows the metadata.

Thus, you’ll need a separate asynchronous data synchronization process: informing the microservice where the file is stored (its URL), its identifier, whether it’s all good, and so on.

4. Auto-Generated Documentation

Always use auto-generated documentation. FastAPI provides this out of the box, while Django has tools like Django-yasg. Ready-made auto-docs save a tremendous amount of time for front-end and mobile teams.

5. Don’t Neglect Type Checking

Using type hints and static analysis is a great way to ensure you’re using packages and classes correctly, and to avoid simple mistakes.

6. Write Automated Tests

In environments with heavy inter-service communication, automated tests are indispensable. They protect you from unnecessary bugs and are a fantastic development tool.

7. Ask for Help When Stuck

Don’t hesitate to ask your colleagues for help if you’re stuck on a particular issue. It’s not a sign of weakness—it’s essential. Brainstorming together can help you complete tasks faster, avoid missed deadlines, and save you from unnecessary frustration.

8. Set Up Sentry

Sentry is a simple yet powerful tool that can be quickly set up in standalone mode. It’s easy to integrate with any framework, and setting it up in your project takes no more than 30 minutes.

9. Pin Library Versions

Our projects use Poetry by default. Dependency managers like Poetry allow you to specify minimum library versions. However, there’s a nuance: usually, only the minimum version is specified. This approach can lead to package conflicts, especially with popular libraries. Consider pinning versions more strictly to avoid such issues.