AI infrastructure gem
Rails AI Gateway
Mount one OpenAI-compatible endpoint inside Rails with ordered model fallbacks, encrypted provider keys, streaming, and built-in usage tracking.
Outcome
Provides chat completions, embeddings, model routes, and usage tracking inside the host Rails application.
My role
Open-Source Developer
How I helped
- Built and published the open-source project.
Product
The workflow in context
Generated product visualization, not a live screenshot.
The solution
Mount one OpenAI-compatible endpoint inside Rails with ordered model fallbacks, encrypted provider keys, streaming, and built-in usage tracking.
What changed
Provides chat completions, embeddings, model routes, and usage tracking inside the host Rails application.
Source
AI system implementation
The gem is an OpenAI-compatible routing layer, not a model. Rails mounts chat completion, embedding, and model-list endpoints; ordered routes choose provider fallbacks. SSE forwards streaming responses. ActiveRecord encryption protects provider keys and metadata records status, latency, attempts, and reported token usage without storing prompts or responses. Native provider APIs and semantic caching are outside the documented scope.
Could a similar approach help your business?
Tell me where work gets stuck. We can identify a practical first improvement and what success should look like.
Discuss your challenge