Turning Ideas into Products Faster: The Minimum Viable Architecture Approach

In today's fast-paced business environment and evolving technology landscape, companies must deliver digital products and services to market quickly if they want to stay ahead of the competition. In our experience working with large enterprises across a variety of industries, the traditional solution-architecture stage of product development can often be a bottleneck, slowing down the entire process from the start.

This article explores the concept of Minimum Viable Architecture (MVA), an approach inspired by the Minimum Viable Product (MVP) methodology. This alternative to traditional architecture can help organizations streamline their product-development process and get new ideas into the hands of users faster.

The Problem: Why Traditional Architecture Slows You Down

The goal of the traditional solution- or enterprise architecture-approval stage is to create a near-perfect architecture that can scale to meet all future needs. While this approach has its merits, the extensive planning and upfront design it often requires can lead to several problems for large enterprises, including:

• Slow Time to Market: Its lengthy design and approval processes can significantly delay the launch of new products, giving competitors a chance to gain a foothold in the market.
• Wasted Resources: Investing significant time and assets into designing and implementing an overly complex architecture (that may not even be successful) can waste valuable resources.
• Reduced Agility: The rigid nature of traditional architecture can make it difficult to adapt to changing market conditions, customer feedback, or technology innovations, thus hindering the ability to iterate and improve products quickly.

The Solution: Minimum Viable Architecture

The Minimum Viable Product concept focuses on delivering a product with just enough features to be usable, meet basic business needs, and gather user feedback. Similarly, Minimum Viable Architecture focuses on creating an architecture with just enough structure to serve as a stepping stone to a vision state or north star that follows sound software architecture principles — and that aligns with engineering and DevOps best practices. The intent is for the MVA to enable and support all requirements of the initial product launch.

Here are seven key principles to help guide your team through the MVA implementation process:

• Patterns and principles before platforms: Ground your architecture strategy in enduring principles and patterns, rather than in specific platform technologies or tooling. This practice cultivates an environment in which the choice of technology supports a broad architectural vision — one that fosters the design of systems that are both robust against the test of time and agile in the face of new challenges.
  
  • For example, the default deployment model today is container based. At scale, the management and operations of containers is quite complex, requiring an orchestration platform such as Kubernetes — as well as a team to manage the platform. In our experience however (and especially when working with teams that are in the early stages in their product development or cloud-native journey), a crawl-first strategy can be more effective.
    In practice, this strategy involves gaining local development experience first and then, on the deployment side, focusing on shipping to PaaS or FaaS capabilities such as Azure App Services or AWS Lambda. The crawl-first strategy does not overcomplicate the process, expedites time to market, enables teams to adhere to best practices and principles and, ultimately, positions the platform for continued evolution and scaling.
• Start simple and iterate: Focus on building a basic architecture that meets the core needs of the product at launch. You can always add complexity and features as the product evolves and scales. Don’t be encumbered by designing a more-difficult-to-implement but overly scalable solution, especially if you are building something for internal use or for a limited number of users. To increase development speed, lean on proven programming languages and frameworks your team is familiar with. Modern databases like PostgreSQL (JSONB for NoSQL, PGVector for vector-based data models enabling AI solutions) offer a great deal of flexibility, allowing you to start with a simple model that can grow and adapt over time. To further enhance agility, utilize tools that facilitate rapid changes and easy updates. Technologies like GraphQL for APIs can dramatically simplify data access and integration, enabling more flexible UI development.
• Embrace "good enough" over perfect: Don't get bogged down in the pursuit of a perfect, all-encompassing architecture (even though Kubernetes seems to be one of the first tools that teams reach for, it can often be overkill). Aim for an architecture that is good enough to support the MVP and can be easily adapted to future needs. And think about the developer experience too Will your engineering team be able to execute what you’re designing? Does it meet them at their experience level? To speed up the delivery of functional prototypes for user feedback, consider low-code platforms for UI creation (e.g. Retool, Mendix), especially for internal tools.
• Place "smart bets" on tools and infrastructure: Leverage existing tools and infrastructure whenever possible to avoid reinventing the wheel. This can save time and resources and allow you to focus on the core functionality of your product. Don’t be afraid of new product licensing costs; they are often dwarfed by savings in development time and future maintenance.
  
  • For example, the default architecture mindset for an API that needs to connect multiple services and data sources is to build one from scratch. But there are many tools out there such as Hasura that can minimize or even eliminate parts of the API-build effort. Even with the cost of an enterprise license at the highest support tier, you will save significant time and development cost over a custom API solution.
• Data as the foundation: Focus on data integrity and structure. While simplifying the UI and API layers is important, making sure that your data platform is robust, user-friendly, and secure is the best way to support the iterative development of your application.
• Documentation and continuous improvement: Document your decisions by creating a log of your architectural decisions. Deciding exactly what this document looks like is less important than just creating it. But if you need inspiration, please take a look at Architecture Decisions Records (ADRs). Creating a decision log not only facilitates knowledge sharing within the team, but also helps the team assess when to undertake architectural revisions as your product scales.
• Adapting architecture over time: Embrace modular designs. Yours could even be a “modular monolith” approach in which you build services within the context of your product — without necessarily taking the time to build a pure microservices approach that could be immediately reusable. Use shared libraries and make incremental changes to keep your architecture flexible and responsive to business needs. Prioritize high-ROI features and tackle hard problems early to maintain momentum and ensure long-term success.

Moving from Perfect to Most Effective

Implementing a Minimum Viable Architecture approach to product-development is not about cutting corners, although it can help your organization cut quite a few. Instead, it is about making strategic choices that align with your business goals, your teams’ capabilities, and market demands. By focusing on what is essential, companies can accelerate product development, reduce overhead, and foster a culture of innovation and agility.

Amid rapid technological evolution, taking the MVA path shifts the conversation from building perfect solutions to creating adaptable, effective architectures that drive growth and innovation — and help you get your new ideas to market first.