Build reliable AI systems
AI Application Architecture
A production AI application is more than a prompt and a model endpoint. It needs stable contracts, controlled access, asynchronous workflows, cost limits, failure handling, and evidence about what happened. This guide connects those decisions into one architecture.
Decisions this guide helps you make
- Where model-provider details should stop and your application contract should begin.
- When a synchronous request should become an asynchronous job.
- How to authenticate users, services, agents, and third-party integrations.
- How to contain provider outages, retries, duplicate work, and runaway cost.
Design the application boundary
Treat model calls as one dependency inside an application—not as the application itself. Define stable contracts around changing providers and models.
AI API design best practices
Design request, response, streaming, tool-call, and job contracts that clients can rely on.
Read guide → BuildBuild an AI gateway
Route multiple providers behind one authenticated, observable interface.
Read guide →API authentication for AI apps
Choose between API keys, OAuth, JWTs, and service identities.
Read guide →TypeScript for AI applications
Type SDKs, streams, tool calls, MCP clients, and structured model responses.
Read guide →Express vs Fastify vs Hono
Choose a Node or edge runtime for AI APIs, gateways, and streaming.
Read guide →Make asynchronous work reliable
Generation, extraction, and agent runs may outlive a browser request. Use jobs, events, and idempotent state transitions instead of hoping every request finishes.
Webhook architecture patterns
Retries, signatures, idempotency, ordering, and delivery guarantees for AI jobs.
Read guide →Idempotency in APIs
Prevent duplicate charges, generations, and agent actions when clients retry.
Read guide →Handle AI API failures
Classify provider errors, retry safely, and degrade without hiding failure.
Read guide →Handle invalid model JSON
Validate structured outputs and recover safely from malformed responses.
Read guide →Monorepos for AI products
Organize apps, agents, gateways, workers, schemas, prompts, and evaluations.
Read guide →Control capacity, cost, and failure
Production reliability means controlling concurrent work and spend as carefully as latency.
How rate limiting works
Translate provider RPM and token limits into fair per-user application limits.
Read guide →Rate-limit AI API requests
Apply practical queues, backoff, and concurrency controls around model calls.
Read guide →AI application deployment checklist
Check runtime, state, secrets, observability, and rollback before launch.
Read guide → Quality layerAI testing and evaluation
Test API contracts, streaming, structured outputs, tools, and asynchronous model workflows.
Read guide →