Single-provider AI setups are a liability waiting to bite you. When OpenAI had that massive outage last year, half the startup ecosystem learned that lesson the hard way. A new developer guide on DEV.to walks through building an "AIGateway" class in JavaScript that routes requests between OpenAI, Claude (via ofox.ai's OpenAI-compatible endpoint), and Google Geminiβ€”giving you redundancy without vendor lock-in.

The Architecture That Makes This Work

The pattern is straightforward: a middleware layer sits between your application and the provider APIs. The AIGateway class maintains configuration for each provider, exposes a unified complete() method, and handles the translation between different API formats behind the scenes. OpenAI and Claude use compatible chat completions endpoints, but Gemini requires its own handler since it uses a completely different request structure with "contents" arrays instead of "messages." The real value comes from the smart routing logic built on top. Rather than manually specifying providers, you can call completeSmart(prompt, taskType) and let the gateway decide. Code tasks route to Claude Sonnet 3.5 for its coding prowess, fast responses go to Gemini Flash for speed and cost savings, and deep analysis lands on Claude Opus for reasoning depth.

Fallback Logic Is Where Resilience Lives

The article includes a completeWithFallback() function that iterates through providers in orderβ€”defaulting to Claude first, then OpenAI, then Gemini. If one call fails, it catches the error, logs a warning, and moves to the next provider automatically. This means your users never see a broken AI feature; they just might wait an extra second while the system recovers.

The Cost Optimization Angle

Beyond reliability, multi-provider routing lets you play providers against each other on price. Gemini's pricing is dramatically lower than both competitorsβ€”$0.000000125 per input token versus $0.000003 for Claude and $0.000005 for OpenAI. A simple trackCost() helper calculates actual spend by provider using the usage data returned in API responses, so you can verify your cost assumptions and adjust routing accordingly.

Key Takeaways

  • Use an AI Gateway pattern to abstract provider differences behind a unified interface
  • Build fallback logic that cycles through providers automatically on failure
  • Route task types intelligently: Claude for code, Gemini Flash for speed/cost, Opus for analysis depth
  • Track actual token costs per provider to validate your routing strategy over time