Checklist: migrating off a public AI API to self-hosted
Moving off a public AI API isn't a single decision, it's four separate questions: does an open model actually match the quality you're getting now, does the cost math work at your volume, how much integration work does the switch take, and what happens if it doesn't go well. Each one has a concrete way to answer it.
1. Check model quality parity before committing to anything
The first question isn't "is there an open model", it's "is there one that's good enough for this specific task at this specific size". That depends heavily on what you're using the API for: a coding assistant, a support triage tool, and a document summarizer have different quality bars and different open models that fit them. GPUwerk's models documentation covers which open models fit in 128 GB of unified memory on a Spark, with measured throughput figures rather than vendor claims, so you can check whether a candidate model is fast enough before evaluating whether it's good enough. Run your actual workload, real prompts, real edge cases, against the candidate model before deciding anything else on this list. A cost or infrastructure win doesn't matter if the output quality drops below what your product or team needs.
2. Estimate your token volume and find the cost crossover
A public API's per-token pricing is cheap at low volume and expensive at high volume; a dedicated box is the opposite, a fixed cost regardless of how much you push through it. The question is where your actual usage sits relative to that crossover. Pull your last one to three months of API billing, or your token usage from the vendor's dashboard if billing doesn't break it down, and compare that spend against a Spark's flat rent-vs-buy cost. If you're evaluating specifically against Azure OpenAI or AWS Bedrock, GPUwerk has direct comparisons for both: vs Azure OpenAI and vs AWS Bedrock. The crossover point depends entirely on your volume and how continuously the workload runs, there's no universal answer, only your own numbers against the current published rates on both sides.
3. Plan for API-shape differences before you migrate, not after
Not every provider's API is a drop-in replacement for another's, even when both claim OpenAI compatibility: function-calling formats, streaming behavior, and system-prompt handling all vary in small ways that break integrations built against one specific vendor's quirks. Rather than rewriting every client that calls your current API, put LiteLLM in front of your self-hosted model as a gateway. It gives every client a single stable, OpenAI-compatible endpoint, so the migration becomes a matter of pointing the gateway at your new model rather than auditing every call site in your codebase for provider-specific assumptions.
4. Have a rollback plan before you cut over
Don't migrate a production workload in one step with no way back. Keep the public API integration in place and working while the self-hosted path runs in parallel, routed to a subset of traffic or a non-critical workflow first. LiteLLM's fallback routing can point traffic back to the cloud model automatically if the self-hosted endpoint is unavailable or underperforming, which gives you a live rollback path rather than a manual one you have to execute under pressure during an incident. Only widen the self-hosted path to full production traffic once it's held up under real load for long enough that you trust it, and keep the old integration code around, not necessarily paying for API usage, but ready to re-enable, until you're confident the migration is done.
Putting it together
These four steps aren't strictly sequential in practice, quality testing and cost estimation usually happen in parallel, but the order matters in one sense: don't finalize the cost case until quality is confirmed, and don't cut over production traffic until the gateway and rollback path are both proven. A migration done in that order is slower than flipping a switch, but it's the version where nothing breaks in front of a customer.