AI Inference Gateway
Proof of-conceptUnified API gateway routing inference requests to optimal providers — HuggingFace, OpenAI, or self-hosted vLLM.
Overview
The AI Inference Gateway provides a single OpenAI-compatible API endpoint that intelligently routes requests to the best available inference provider based on cost, latency, model capability, and rate-limit headroom. It supports chat completions, embeddings, text classification, and structured extraction — with automatic failover, request caching, and per-user token management.
Technologies Used
Key Features
- OpenAI-compatible /v1/chat/completions endpoint
- Multi-provider routing with automatic failover
- Request caching via Redis for repeated queries
- Per-user API token issuance and rate limiting
- Streaming response support (SSE)
- Cost tracking and usage analytics dashboard
Challenges & Solutions
Challenge:
Normalising response formats across different inference providers
Solution:
Built adapter layers that transform provider-specific responses into a unified OpenAI-compatible schema
Challenge:
Managing cold-start latency on free-tier HuggingFace models
Solution:
Implemented a warm-up scheduler and fallback routing to pre-warmed alternative models
Challenge:
Preventing abuse while keeping the API accessible
Solution:
Designed a tiered token system with configurable rate limits, usage quotas, and automatic throttling
Outcome
Serving internal demos and research workloads; public beta planned for Q2 2026