When you're building an AI product, the first vendor decision founders face is whether to hire an AI consultancy or an AI MVP studio. Both claim to help you build AI products. The outcome you get from each is dramatically different.
What AI consultancies actually deliver. Traditional AI consultancies — including the AI arms of large management consulting firms — deliver strategy documents, roadmaps, architecture recommendations, vendor evaluations, and proof-of-concept demos. They are optimized for selling advice to enterprises that have months to evaluate options. Engagements typically start at $50,000–$300,000 for strategy phases alone. The deliverable is a deck. Sometimes a prototype. Rarely production code.
What an AI MVP studio delivers. An AI MVP studio like SpeedMVPs starts where consultancies stop: production code. The deliverable is a working, deployed application — not a recommendation for one. Studios specialize in execution velocity. SpeedMVPs ships production AI MVPs in 2–3 weeks, with full source code ownership transferred to the client at close.
Key differences at a glance. Consultancies charge for time-and-materials at senior rates ($250–$500/hr). Studios charge a fixed price for a defined deliverable. Consultancies employ ex-McKinsey generalists who understand AI strategy. Studios employ AI engineers who build with LLMs, vector DBs, and agents every day. Consultancies measure success by client satisfaction with the strategy. Studios measure success by whether the product ships on time.
When to choose an AI consultancy. You are a large enterprise allocating a multi-year AI budget. You need board-level narrative and risk analysis before any code is written. You have internal engineering teams that will execute the strategy. You need vendor-neutral evaluation of AI platforms (Azure OpenAI vs AWS Bedrock vs Google Vertex). Regulation requires documented architecture review before implementation.
When to choose an AI MVP studio. You are a startup or innovation team that needs working software, not strategy slides. You have a clear product hypothesis and need it validated with real users in weeks. You want fixed-price delivery with defined scope. Your goal is traction — signups, demos, revenue — not internal alignment. You need the code to own, deploy, and modify independently.
The hidden cost of consultancy-first. Many founders hire a consultancy, spend 3–4 months getting the strategy right, then discover they need an entirely different vendor to build it. The strategy was optimized for the consultancy's framework, not for fast execution. The 'handoff' rarely works cleanly. Timelines slip from Q2 to Q4. By then, competitors have shipped. The consultancy model is designed for enterprises that can absorb this timeline. Startups cannot.
The SpeedMVPs model. We operate as an AI MVP studio: fixed scope, fixed price, 2–3 week delivery, 100% code ownership. We do light discovery (1–2 days) to scope the MVP correctly, then build. We have opinions about architecture because we've built 18+ AI products — but we save them for decisions that affect delivery speed and maintainability, not 40-page strategy documents.
Verdict. If you are pre-seed or seed stage and need a working AI product to show investors, partners, or customers, choose an AI MVP studio. If you are Series B+ and need to justify a multi-million AI platform investment to a board, a consultancy may be the right first step. Most founders reading this guide are in the first category.
What You'll Get
Model Comparison Matrix
Side-by-side breakdown of consultancy vs studio
Stage-Fit Decision Tree
Which model fits your company stage
Budget Allocation Guide
How to spend your AI budget at each stage
