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Rescue
You built your system on one provider and now need to move, for cost, quality, compliance, or reliability reasons. The API calls are straightforward to swap. The prompts are not. Different models respond differently to the same instructions. Prompts written for GPT-4 don't always produce equivalent results on Claude or Gemini without adaptation.
We audit your prompt set, test replacements against your existing quality baseline, adapt prompts for the new model's behaviour, update your evals, and migrate traffic using an A/B routing layer so you can confirm quality before full cutover.
Tell us what you’re switching and why.
Four legitimate reasons to switch providers. If your reason isn't one of these, we'll tell you before you spend money on a migration.
Your provider updated the model, same name, different behaviour. Outputs that were reliable are now inconsistent. Switching to a provider whose model version policy better matches your stability requirements (or locking to a specific version) resolves this.
You've scaled past the point where the current provider's pricing makes sense. A move to a different provider, a smaller model for lower-complexity tasks, or a self-hosted model reduces per-token cost significantly. We evaluate the quality tradeoff before recommending any switch.
Your legal or compliance team has determined that data cannot leave your cloud environment, cannot go to a US provider, or must be processed by a model you host yourself. We migrate the system to a self-hosted model (Llama, Mistral, or similar) or a compliant regional provider.
Your current provider's API has reliability or rate limit constraints that affect your product. Some providers offer better enterprise SLAs, higher rate limits, or multi-region redundancy. We evaluate alternatives against your actual traffic patterns before recommending a switch.
Five phases. Each has specific outputs. We don't cut over until each phase passes its quality gate.
Step 1
Map every prompt in the system: system prompts, user message templates, few-shot examples, tool definitions. Document the expected output format and behaviour for each. This becomes the specification the new model must match.
Step 2
Run your existing prompt set against candidate providers. Score output quality against your current baseline on the same inputs. Identify which prompts need adaptation for the new model's instruction-following style. Some prompts transfer cleanly; others require significant rewriting.
Step 3
Rewrite prompts for the target model's behaviour. Different models respond differently to the same instructions. What worked as a GPT-4 system prompt may produce worse results on Claude or Gemini without adaptation. We tune each prompt against the target model, not just copy-paste.
Step 4
Update your evaluation harness to run against the new provider. Establish a new baseline. Run both providers in parallel on a sample of production traffic to confirm output quality before full cutover.
Step 5
Route a small percentage of production traffic to the new provider while maintaining the current provider as fallback. Monitor quality metrics, latency, and error rates. Increase the new provider's share as confidence builds. Full cutover only when the new provider is demonstrably performing at least as well as the old one.
We have no preferred provider. The right choice is the one that meets your quality, cost, and compliance requirements.
OpenAI (GPT-5, o1, o3)
From or to
Anthropic (Claude 3, Claude 4)
From or to
Google (Gemini 2.5, 2.0, 2.5)
From or to
Meta (Llama 4, self-hosted)
To (for self-hosted requirements)
Mistral (self-hosted or API)
To (for EU compliance or self-hosted)
Azure OpenAI
To (for enterprise compliance or region requirements)
Provider migrations have prerequisites. We'll tell you upfront if these apply.
Systems without any evaluation coverage
A provider switch without evals is a blind migration. You won't know if quality improved or degraded until users tell you. We won't do a provider migration without establishing evaluation coverage first. If your system has no evals, the first phase is building them.
Teams who want to switch and add new features simultaneously
Changing the provider while changing the system's behaviour makes it impossible to attribute quality changes to the provider switch versus the feature changes. We migrate first, then add features once the new provider baseline is established.