A unified API for every model and tool.
One endpoint for chat, completions, embeddings, tool calls, and evals — with routing, caching, rate limits and observability included. Give your engineering team a single, typed platform for AI features — one API, many models, managed integrations, streaming, evaluations and observability that meet enterprise SLAs.
Overview
A single home for ai api.
AI API is the platform layer that lets your engineers build AI features the same way they build the rest of your product: with typed contracts, testable behavior, first-class observability and predictable operational cost.
It abstracts the moving parts — model routing across OpenAI, Anthropic, Google and self-hosted stacks, OAuth for connectors, streaming transport, evaluations and telemetry — behind clean SDKs. Teams swap models, add integrations and scale to production traffic without rewriting application code.
50%
fewer tools
4×
faster time to value
99.99%
uptime
SOC
2 · HIPAA
Problems it solves
What breaks without AI API.
The pain points developer platform teams tell us they hit before adopting AI API — and how the platform removes each one.
Too many models, too many APIs
Each provider ships a different SDK, streaming format and error model — every switch is a rewrite.
Prompt drift and regressions
Changes to prompts or models silently degrade production quality with no eval harness to catch it.
Secrets and OAuth sprawl
Every integration reinvents refresh, revocation and least-privilege scopes.
Unbounded inference cost
Without quotas, semantic caching and routing, spend scales faster than usage.
Painful observability
Traces, logs and metrics for AI calls sit outside your existing APM, making incidents hard to root-cause.
Handoff between prototypes and production
The team that prototypes AI and the team that runs it in production use different tools.
Capabilities
Everything you need to ship.
Model routing
Route to OpenAI, Anthropic, Google, or self-hosted per workspace.
Caching & rate limits
Semantic cache, TTLs and org-level quotas out of the box.
Streaming
SSE and websocket streaming with backpressure handling.
Technology
Built on production-grade primitives.
AI API is engineered for teams that need to run AI in production — with the reliability, observability and portability that regulated, high-volume workloads demand.
- TypeScript, Python and Go SDKs with generated types
- SSE and websocket streaming with backpressure handling
- OAuth 2.0 managed refresh, revocation and scoped tokens
- Semantic caching, retry with backoff and dead-letter queues
- OpenTelemetry-native tracing, metrics and logs
- Signed webhooks with HMAC verification and replay
- Flyps CLI with preview environments and secret management
Why teams love AI API
Outcomes, not outputs.
The measurable benefits developer platform leaders report after standardizing on AI API.
One API, many models
AI API abstracts provider-specific SDKs behind a typed interface — swap models without touching application code.
Typed contracts everywhere
SDKs, tool schemas and prompt inputs are typed and validated, catching regressions before they ship.
Managed integrations
OAuth refresh, revocation and least-privilege scopes are handled for you across 300+ connectors.
First-class observability
Streaming-aware traces, logs and metrics export cleanly to Datadog, Grafana, Honeycomb or any OpenTelemetry collector.
Predictable operational cost
Semantic caching, quotas, model routing and DLQs keep costs inside SLOs without brittle rate-limit code.
Local-first developer loop
CLI-driven previews, secret management and typed generators keep the inner loop fast.
Framework-agnostic
Works with Next.js, Nuxt, Rails, Django, Go, or a bare Lambda — no lock-in on your web stack.
How we deliver
From setup to signal in weeks.
Every AI API rollout follows the same repeatable process — designed with your engineering, product and compliance teams in the loop from day one.
1
Provision
Create a workspace, connect providers and set quotas — infrastructure is production-ready from day one.
2
Integrate
Wire the SDK into your service; AI API handles typing, retries and streaming so your code stays clean.
3
Evaluate
Run offline evaluation suites in CI to catch regressions before they hit production.
4
Monitor & scale
Traces, cost dashboards and DLQs give on-call the signal to scale confidently.
Expected results
Business impact you can measure.
Typical outcomes teams report within the first two quarters on AI API — instrumented against the metrics your leadership already tracks.
-70%
time-to-integration for new AI features
-35%
inference spend from routing and caching
99.99%
API SLA across regions
<100ms
cache-hit latency on typed calls
Use cases
Where teams use AI API.
In-product AI features
Copilots
RAG
Batch inference
Integrations
Plays well with your stack.
First-party connectors — plus 300+ more across the Flyps platform.
Recommended solutions
See AI API in your context.
FAQ
Answers about AI API.
The questions engineering, product and compliance leaders ask most often before rolling AI API out — with detailed answers on implementation, technology and security.