Good fit when you need
- A stable inference contract for multiple client apps
- Model routing without rewriting every product screen
- Fallback when a vendor endpoint is slow or unavailable
Track A · AI Products
Custom AI Microservices & APIs
Low-latency Python FastAPI and Node.js inference endpoints, with model routing and fallback cascades.
Overview
Inference as a product surface—not a notebook
We wrap models behind authenticated APIs your web, mobile, and ERP systems can call with timeouts, quotas, and structured JSON. Routing can send cheap tasks to one model and high-stakes tasks to another, with a fallback if an upstream provider fails.
These services sit next to Agentic AI and Domain RAG: agents and search call your APIs; the APIs own latency, keys, and logging.
Capabilities
What we implement
FastAPI / Node endpoints
Typed request/response schemas, auth, and p95 latency budgets you can measure.
Model routing
Send traffic by task type, tenant, or cost—via LiteLLM-style gateways where they fit.
Fallback cascades
Retry and degrade to a secondary model or cached result instead of a hard fail.
Key & tenancy isolation
Service identities per environment—not a shared key in a mobile binary.
Telemetry
Token, latency, and error traces that feed evaluation and cost reviews.
Container delivery
Docker-friendly packaging that slots into your CI/CD and Kubernetes path.
Benefits
Why teams choose this path
- Product teams call one API instead of four vendors
- You can swap models without rewriting Flutter or web clients
- Latency and cost become measurable SLOs
- Fits the Zettai Framework: lock the contract before traffic grows
Process
How We Deliver
01
Contract
Define payloads, SLOs, and which models may be used.
02
Build
Stand up FastAPI or Node routes with auth and logging.
03
Route
Add routing, fallbacks, and rate limits.
04
Load test
Prove p95 under a realistic mix of calls.
05
Operate
Hand over runbooks and cost dashboards.
Technologies
Tools We Work With
APIs
PythonFastAPINode.jsExpressREST/GraphQL
Routing
LiteLLMAzure OpenAIAWS Bedrock
Delivery
DockerCI/CDEvaluation
FAQ
Common Questions
Often we front managed endpoints (Azure OpenAI, Bedrock, Vertex). Self-hosted vLLM or similar is scoped when you already have GPU capacity and ops ownership.
Yes. The point of the microservice is a stable HTTP contract those stacks already speak.
Need inference behind a real API?
We’ll sketch the contract, routing, and latency budget before a line of production traffic.