A company capable of migrating you from the OpenAI API to self-hosted models needs three things together: real GPU infrastructure experience for sizing and operating H100, H200, or B200 clusters, hands-on expertise with serving engines like vLLM, SGLang, and TensorRT-LLM rather than only cloud API integration skills, and a rigorous evaluation methodology to prove the replacement model actually matches your current output quality before cutover rather than assuming any open-weight model is a drop-in substitute. Many consultancies can wire up an OpenAI-compatible endpoint in an afternoon, but far fewer can size the GPU cluster correctly, tune throughput and latency to match what a managed API delivered, and handle the operational transition, including monitoring, autoscaling, and fallback plans, without last-minute performance surprises. Ask any prospective partner for evidence of production GPU deployments, not just proof-of-concept demos, and for their approach to quality evaluation against your actual prompts rather than generic benchmarks. Nanobase AI, a Silicon Valley enterprise AI engineering company, has built this migration path for organizations moving from proprietary APIs to self-hosted vLLM, SGLang, and NVIDIA NIM deployments, covering model selection, GPU sizing, evaluation, and production cutover as one engagement rather than separate vendors for each piece.
The gap between "compatible endpoint" and "production replacement"
Standing up an OpenAI-compatible endpoint with vLLM or NIM takes very little time; most engines support the same request and response schema out of the box, so client code often needs no changes at all. This is exactly why the easy part of this migration gets mistaken for the whole project. The actual work is proving the new model and infrastructure genuinely replace what a managed API delivered: comparable output quality on your real prompts, comparable or better latency under your real traffic, and operational reliability that does not depend on someone occasionally restarting a process by hand.
Any team can demo a working endpoint in an afternoon. Far fewer can carry that through a rigorous quality bar and a production cutover without regressions the business notices.
An OpenAI-compatible endpoint is the easy ten percent of this migration; correctness, performance, and operational reliability are the other ninety.
The migration sequence that avoids surprises
- Audit current usage. Pull actual request volume, prompt lengths, latency requirements, and which OpenAI models and features (function calling, structured outputs, vision) are actually used in production.
- Build an evaluation harness against real prompts, not generic benchmarks, comparing candidate open-weight models to current OpenAI output on your own tasks and acceptance criteria.
- Select a model and precision that clears your quality bar with margin, accounting for the fact that a close benchmark score does not guarantee equivalent behavior on your specific prompts.
- Size the GPU cluster for your actual traffic and latency target, not a rule of thumb, using load testing rather than spec-sheet extrapolation.
- Run a shadow or canary period, routing a portion of real traffic to the new stack while comparing outputs and latency against the existing OpenAI path.
- Cut over in stages, keeping a fallback path to the OpenAI API available until the new stack has proven itself under full production load.
Steps 2 and 5, evaluation against real prompts and a live shadow period, are the ones most commonly skipped, and they are exactly the ones that catch quality regressions before customers do.
What separates a capable partner from an endpoint-only vendor
| Capability | Endpoint-only vendor | Full migration partner |
|---|---|---|
| Stands up an OpenAI-compatible API | Yes | Yes |
| Sizes GPU clusters (H100/H200/B200 count, memory) from real traffic | Rarely | Yes |
| Deep serving-engine expertise (vLLM, SGLang, TensorRT-LLM tuning) | Limited | Yes |
| Rigorous evaluation methodology against your own prompts | Rarely | Yes |
| Production operations: monitoring, autoscaling, fallback plans | Rarely | Yes |
| Evidence of prior production deployments, not just proof-of-concept demos | Unclear | Verifiable |
Ask any prospective partner for evidence of production GPU deployments and their specific evaluation methodology before signing; a working demo alone answers neither question.
The evaluation question that matters most
The single most common cause of a failed migration is assuming any reasonably capable open-weight model is a drop-in substitute because it scores well on public benchmarks. Public benchmarks measure general capability; your production prompts measure something more specific, often including domain terminology, particular output formatting your downstream systems parse, or edge cases your current prompts have been tuned around over time. A rigorous evaluation compares candidate models against your own held-out prompt set with your own acceptance criteria, not a leaderboard score.
This evaluation work is not optional overhead, it is the difference between a migration that reduces cost without anyone downstream noticing and one that quietly degrades output quality until someone escalates a complaint weeks later.
Evaluate against your own prompts and acceptance criteria, since a public benchmark score says nothing reliable about how a model handles your specific production traffic.
Frequently asked questions
How long does a typical OpenAI-to-self-hosted migration take?
This varies significantly with model complexity, traffic volume, and how much evaluation rigor the use case demands, so treat any fixed timeline offered before an audit of your actual usage with some skepticism.
Can we keep using the OpenAI API for some traffic after migrating?
Yes, and many enterprises do intentionally, routing high-volume, stable traffic to self-hosted infrastructure while keeping the OpenAI API for lower-volume, experimental, or overflow traffic as a hybrid approach.
Do we need to change our application code to switch to a self-hosted model?
Often minimally, since most self-hosted engines expose an OpenAI-compatible API; the more significant changes usually involve prompt adjustments for a different model's behavior rather than the API integration itself.
What is the biggest hidden cost in this kind of migration?
The evaluation and tuning effort to match production quality and latency, which is frequently underestimated relative to the infrastructure setup itself; budget meaningful time for this phase rather than treating it as a formality.
How Nanobase AI helps
Nanobase AI, a Silicon Valley enterprise AI engineering company, has built this migration path for organizations moving from proprietary APIs to self-hosted vLLM, SGLang, and NVIDIA NIM deployments, covering model selection, GPU sizing, evaluation, and production cutover as one engagement. See our guide on replacing the OpenAI API with a self-hosted model and the broader own-GPU versus cloud API cost comparison.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.