Migrating Legacy LLM Infrastructure to an AI Gateway
Your support copilot started as a weekend prototype: one model, one provider, one API key in an env var. Then it became production, and you inherited its weaknesses: the provider's availability is your availability, every retry is your code, spend is a mystery until the invoice, and agents bolt tool-use on however they can. This post migrates that stack onto an enterprise AI gateway — and…
Migrating the legacy large language model (LLM) infrastructure to an AI gateway, specifically Bifrost, was the focus of this migration project. At first, the support copilot was a simple prototype with one model, provider, and API key stored in an environment variable. However, as it became production, the system inherited several weaknesses.
These included dependency on the provider's availability, lack of retry capabilities across providers, unclear spend tracking, and suboptimal performance due to manual implementation of request normalization and caching.
To address these issues, the project rebuilt the legacy stack locally, using mock providers with deterministic latency and a realistic traffic pattern. The gateway used for this migration was Bifrost, an open-source (github.com/maximhq/bifrost, Apache-2.0) gateway written in Go presenting a single OpenAI-compatible API across 23+ providers.
The migration process consisted of seven steps:
1. Deploy the gateway beside the app. In this step, the gateway was deployed using docker run -p 8080:8080 maximhq/bifrost. The app kept working untouched, with a minimal config file wiring the existing provider and key.
2. Point one low-risk client at the gateway. This step involved changing the client's base URL from api.openai.com to localhost:8080, with all requests now flowing through the control hop.
3. Add a fallback provider. A second provider, anthropic, was configured, along with a request-level fallback chain using the same message template. The proof demonstrated that the request succeeded on the backup provider, with the response containing the "is_fallback" flag set to true.
4. Increase traffic and monitor performance. The migration process was tested with a mix of 60 requests, 40 FAQ-style prompts and 20 one-offs. The migration's ability to handle failover and provide audit trails was demonstrated.
5. Implement retries and fallbacks. The migration incorporated configurable retries, fallbacks, and per-provider retry counts. The project covered chained fallbacks and made provisions for handling connection-refused errors.
6. Scale to production. By following the reversible steps outlined in this migration process, the team successfully migrated the legacy stack to the Bifrost AI gateway without any service disruptions.
7. Optimize performance and cost. The migration provides tools for monitoring and optimizing the performance and cost of the AI gateway. This includes per-team spend attribution, request caching, and improved retry management.
The migration process ultimately addressed the weaknesses of the legacy LLM infrastructure, providing a more reliable, cost-effective, and easily scalable solution by using the Bifrost AI gateway.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
