Most mid-market technology leaders I talk to are caught in the same trap. The board wants an AI story by next quarter. The engineering team wants to build something real. And every vendor in the pipeline wants you to commit to their stack before you have validated a single assumption.

Ninety days is enough time to ship a credible AI MVP. It is not enough time to recover from a bad platform decision. The two goals—speed and optionality—are not in conflict if you sequence the work correctly. Here is the framework I use with CTOs and VP Engineering teams who need to move fast without mortgaging their architecture.

Why AI MVP in 90 Days Without Platform Lock-In Matters Now

Three forces are converging on mid-market engineering organizations:

  • Board-level urgency. AI initiatives are now a standing agenda item. "We're evaluating options" buys you one quarter, not two.
  • Vendor consolidation pressure. Cloud providers, model vendors, and orchestration platforms are all racing to become the default. Each offers generous credits and "accelerators" that quietly become dependencies.
  • Talent scarcity. You likely have one or two engineers who can credibly build AI systems. Their time is your scarcest resource, and it should not be spent on proprietary SDKs that only work in one ecosystem.

The cost of lock-in is not the license fee. It is the re-platforming project you fund eighteen months from now when the model you bet on gets deprecated, the pricing changes, or your use case outgrows the abstraction you bought into.

A 90-day MVP is a learning instrument. Its job is to answer specific questions: Does this use case produce measurable value? Can our data support it? What does production actually cost? Lock-in forecloses those answers because it makes switching expensive before you know whether you want to switch.

Decision Criteria and Trade-Offs

Before you evaluate vendors, define what "good" looks like for your organization. I use six criteria with clients:

  1. Model portability. Can you swap the underlying model (OpenAI, Anthropic, open-weight, or your own fine-tune) without rewriting application logic? If the answer requires a multi-week refactor, you are locked in.
  2. Data gravity and egress. Where does your data live, and what does it cost to move? Egress fees and proprietary data formats are the most common lock-in mechanisms.
  3. Evaluation and observability. Can you measure quality, latency, and cost independently of the vendor's dashboard? Your evals should be portable artifacts, not vendor-hosted configs.
  4. Total cost at scale. Pilot pricing rarely resembles production pricing. Model the cost at 10x and 100x your pilot volume before signing anything.
  5. Compliance posture. SOC 2, data residency, retention policies, and subprocessor lists. For mid-market, this often eliminates more vendors than technical criteria do.
  6. Exit cost. Concretely: if you had to leave in twelve months, how many engineer-weeks would it take? Write the number down.

The core trade-off is managed velocity versus architectural control. Fully managed platforms get you to a demo in two weeks. Portable architectures get you to a defensible system in twelve. For an MVP whose purpose is learning, the second option almost always wins—but only if you are disciplined about scope.

A Practical Evaluation Framework

Here is the framework I walk teams through, structured around the 90-day clock.

Days 1–15: Define the Decision, Not the Demo

Write a one-page decision memo answering:

  • What business decision does this MVP inform? (Fund the next phase? Kill the initiative? Choose a vendor?)
  • What is the single metric that determines success?
  • What would make us walk away?

If you cannot answer these, you are not ready to build. Most failed AI pilots fail here, not in the code.

Days 16–45: Build the Thinnest Vertical Slice

Architect for portability from day one:

  • Abstract the model behind an internal interface. A thin adapter layer (often under 200 lines) lets you route to different providers. Frameworks like LangChain or LiteLLM can help, but a hand-rolled interface is often simpler and more durable.
  • Keep prompts and eval sets in version control. They are source code, not configuration.
  • Store raw inputs and outputs in your own database. Vendor logs are for debugging, not for your analytics.
  • Use open formats. Parquet, JSONL, standard vector formats. Avoid proprietary embedding stores you cannot export.

Days 46–75: Instrument and Stress-Test

Run the MVP against real users or real data. Measure:

  • Quality against a held-out eval set you built yourself
  • Latency at p50 and p95
  • Cost per successful task, not per API call
  • Failure modes and their business impact

This is also when you test the swap. Route 10% of traffic to a second model provider. If it takes more than a day of engineering, your abstraction is leaking.

Days 76–90: Decide With Evidence

Produce a decision memo with three scenarios: scale, pivot, or stop. Include the exit-cost estimate from your evaluation criteria. Present it to stakeholders alongside the working system.

Implementation Risks and Mitigations

The risks that sink AI MVPs are predictable. Plan for them.

  • Scope creep into "platform." Teams start building internal AI infrastructure before they have a use case. Mitigation: freeze scope at the decision memo. Anything not serving the success metric goes to a backlog.
  • Hidden vendor coupling. SDKs that phone home, managed vector stores with no export, proprietary fine-tuning formats. Mitigation: require an export test as an acceptance criterion for every dependency.
  • Evaluation theater. Teams measure what is easy (token counts) instead of what matters (task success). Mitigation: build your eval set before you build the feature.
  • Cost surprises at scale. Pilot costs of $200/month become $40,000/month at production volume. Mitigation: model unit economics at 10x and 100x on day one.
  • Compliance discovered late. Legal blocks launch in week 11. Mitigation: bring security and legal into the day-15 review, not the day-85 one.
  • Single-engineer dependency. One person holds all the context. Mitigation: pair on the core abstraction layer and document decisions as ADRs.

None of these are exotic. They are the same failure modes that have plagued enterprise software for decades, accelerated by the pace of AI tooling churn.

Recommended Next Steps

If you are staring down a 90-day clock, here is what I would do this week:

  1. Write the decision memo. One page. No slides.
  2. Inventory your constraints. Data residency, compliance, existing cloud commitments, team capacity.
  3. Pick two model providers and one orchestration approach. Not five. You are testing portability, not running a bake-off.
  4. Define your eval set. Twenty to fifty examples is enough to start.
  5. Set the day-90 decision date on the calendar and tell your stakeholders what will be decided.

The teams that get this right treat the MVP as a reversible bet. They invest in the abstraction layer, keep their data portable, and make the platform decision after they have evidence—not before.

If you want a second set of eyes on your architecture or your 90-day plan, we run structured reviews for mid-market engineering teams.

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