The moment that crystallised this question for me was sitting across from a vendor demo for a content workflow tool. The product was polished, the interface was clean, and the demo covered roughly 70% of what I needed. The problem was that the 70% it handled was not my 70%. The specific capabilities that mattered most, combining voice transcription with document intelligence, generating multiple formats from a single input, and layering a customisable knowledge base over the whole workflow, were either absent or sitting on a roadmap labelled 'coming in Q3.' The subscription for the 70% I did not need was north of $200 a month. That was the moment I decided to build Avenote instead.

The build vs. buy decision is one of the oldest questions in enterprise technology. For most of its history, the answer leaned heavily toward buy: building was expensive, required a technical team, took months, and left you responsible for maintenance forever. A good SaaS product was faster, cheaper, and someone else's problem to keep running. That calculus made sense for two decades. It is increasingly wrong in an AI-assisted development environment and most organisations have not updated their assumptions.

The Question Underneath The Question

Before getting to the decision framework, it is worth being precise about what build vs. buy actually means in 2026. It is not a binary choice between writing code from scratch and subscribing to a SaaS platform. The real spectrum runs from: fully custom build (you own everything), to composable build (you assemble from APIs and open-source components), to configured SaaS (you use a platform with meaningful customisation), to out-of-the-box SaaS (you use it as designed, with minimal configuration).

Each point on that spectrum has different cost, control, and capability implications and the optimal choice depends on factors that are specific to your organisation, your use case, and crucially, whether the capability you are evaluating is a commodity or a competitive advantage.

When Buying Is Still The Right Answer

Buying makes sense when requirements are well-defined, relatively stable, and not where your competitive differentiation sits. If you need email automation for standard marketing campaigns, there is no good reason to build that. If you need a CRM with a mobile app and offline capability, building it would be architectural hubris. If you need a reliable transcription API for a specific workflow, Whisper or a commercial equivalent is the right call integrating rather than replicating is the correct instinct.

The buy case is also strong when the niche is narrow. I have recommended buying to clients when their requirements were genuinely straightforward: a specific operational workflow with no meaningful customisation needed, handled competently by an existing product at a price that makes the build case weak. If a SaaS tool solves 95% of your actual need (not 70% of a generic version of it), the remaining 5% rarely justifies the overhead of ownership.

The mistake most organisations make is not buying when they should; it is buying prematurely, before they have defined what their actual requirements are precisely enough to know whether a standard solution covers them. A vendor demo is not a requirements exercise. A polished product covering someone else's use case can look very compelling until you map it against what you specifically need.

Why The Build Case Has Strengthened Significantly

The fundamental shift is in the cost of building. For most of the last two decades, building a robust, scalable software product required a technical team of developers, a QA process, infrastructure management, ongoing maintenance etc. The realistic cost for a reasonably complex product was measured in tens of thousands at minimum, often significantly more. That cost made the buy decision rational for most organisations most of the time.

AI-assisted development has changed that equation materially. A practitioner with a clear product vision and the right AI development tools can now build, iterate, and deploy solutions that would previously have required a team, at a fraction of the time and cost. Avenote was built without a technical team. The cost of building it was a fraction of what commissioning it externally would have required. Avenote is a live product with real users. It exists because the cost of building it fell below the threshold where buying became the obvious alternative.

The second shift is in the space itself. The AI landscape is evolving fast enough that vendor lock-in carries a cost that is easy to underestimate at the point of purchase. Platforms that were leading products 18 months ago have been disrupted by new entrants.

Pricing models have changed as platforms scaled or pivoted. Capabilities that were enterprise-only have become commodity. When you build on a platform, the pace of that platform's evolution and the stability of its pricing are risks you are absorbing. When you build your own solution, even if it uses third-party APIs at the component level, you can swap those components as the landscape evolves.

Three Builds, Three Different Reasons

The three AI products I have built each reflect a different dimension of the build case, and it is worth being specific about each.

Avenote was a capability gap decision. There was no all-in-one AI content studio that combined voice transcription, document intelligence, multi-format output generation, and a knowledge layer into a single coherent workflow. What existed were tools that handled pieces of it: a transcription tool here, a summariser there, a content generator somewhere else. Stitching those together manually defeats the purpose. The build case was clear: the integrated capability did not exist at a price point that justified subscription, and the cost of building it had fallen to the point where a solo practitioner could do it without a team. Beyond the product itself, I wanted first-hand evidence that I could build something robust and scalable at that level, which is a different kind of return on investment than a subscription can provide.

The AI Solution Advisor was a moat decision. The core of the product is a knowledge base: a carefully curated database of over 200 AI tools and platforms, each evaluated across more than 20 attributes like scalability, robustness, technical capability required in-house, ROI characteristics, time to implement, pricing model, and suitability by company size and sector. That database is not something you can buy. It is accumulated, structured intelligence and the kind of knowledge asset that takes sustained effort to build and cannot be replicated by subscribing to a platform. The software wrapping that knowledge base is almost secondary to the knowledge itself. Building was the only viable path because the thing that makes the product valuable is something that does not exist for purchase.

The Multi-Agent Customer Support system was a specificity decision. The business logic behind it, the routing agent, specialist agents, escalation protocol, knowledge base architecture, and integration with existing communication channels, was specific enough to the client context that no off-the-shelf product could have handled it without compromising the design. When requirements are that precise, the customisation overhead on a SaaS platform often approaches the cost of building, while leaving you with a product that is still constrained by someone else's architecture. Building gave full control over the system design and the ability to evolve it as the use case matured.

The Real Cost Comparison

The cost comparison most organisations do at the point of a build vs. buy decision is incomplete. They compare the upfront build cost against the first-year subscription. What that comparison misses is the total cost of ownership over three to five years, which for SaaS includes: annual subscription increases (commonly 10-20% per year as platforms mature), the cost of workarounds for the capabilities the platform does not cover, the switching cost if you later decide to move, and the opportunity cost of capabilities you could not build because your team was locked into a vendor's architecture.

Against that, the three-year cost of an AI-assisted build includes the initial development investment (now significantly lower than it was), ongoing maintenance (proportional to the complexity of the system), and the cost of evolving it as requirements change. For many use cases, particularly those involving meaningful customisation or a rapidly evolving domain, the three-year build TCO is now comparable to or lower than the equivalent SaaS subscription with significantly more control and flexibility.

A Practical Framework Before The Decision

Before making a build vs. buy call, four questions are worth answering precisely.

Is this capability your competitive advantage, or is it infrastructure? If it is what makes your product or service differentiated, the case for building and owning is strong. If it is operational infrastructure that every competitor also needs, buy it and focus your build energy elsewhere.

Does the integration need to be seamless, or can best-of-breed tools work independently? If the value is in the combination of different data sources, different AI capabilities, different output formats working together as a single system, then building the integration layer often costs less than fighting a SaaS platform's architecture to achieve the same result.

What is the realistic three-year cost on each path, not just year one? Do the full calculation. SaaS costs compound. Build costs front-load but then flatten. The crossover point has moved in building's favour over the last two years.

How stable are your requirements? Well-defined, stable, narrow requirements favour buying. Evolving requirements, a fast-moving domain, or a use case that you expect to significantly expand as these favour building, because a platform you own can be redesigned while a platform you rent requires the vendor's agreement.

The Default Is Shifting

My default has changed. The starting position is now to look for a build path, and to choose buy when there is a specific reason that justifies it and not the other way around. That shift is driven by the change in build costs, the volatility of the AI vendor landscape, and direct experience of what it is possible to build without a technical team in 2026. The organisations still operating on the assumption that building is always the expensive, slow option are making decisions with outdated inputs. The cost of building has fallen. The cost of the wrong vendor relationship has not.