AI App OS

The AI App Platform for production

A modular AI App OS: routing, runtime, monetization, and enterprise governance — designed to scale.

12.4B
replay requests modeled
across benchmark profiles
18-35%
typical savings
from routing and cache
99.5%+
success rate target
with fallback policies
50+
models and providers
through one interface
Four modules, one production surface

Model routing, agents, monetization, and governance in one control plane

Architecture map

The path from app request to governed signal

Read architecture docs
Operating loop

Not a one-time proxy, a continuous improvement system

Step 1

Design the policy

Start with a routing goal, budget, fallback chain, and data boundary. Every policy is versioned and reviewable.

cost / latency / quality objective
regional and tenant constraints
rollout percentage
Step 2

Route live traffic

The gateway normalizes requests, scores candidates, checks cache, enforces budgets, and records the trace.

provider quota awareness
semantic cache lookup
fallback on typed failures
Step 3

Observe every decision

Trace spans connect model choice, token spend, tool calls, cache hits, policy versions, and end-user metadata.

tokens and effective price
cache and fallback reason
tool timeline
Step 4

Improve with evidence

Compare policies, prompts, and model pools against evals before the next rollout changes production behavior.

eval score deltas
A/B guardrails
rollback readiness
route-production.ts
import { SkyAI } from "@skyaiapp/sdk";

const skyai = new SkyAI({ apiKey: process.env.SKYAIAPP_API_KEY });

const answer = await skyai.route({
  input: messages,
  goal: "quality",
  strategy: "balanced",
  budget: { maxCostUsd: 0.08, maxLatencyMs: 1800 },
  fallback: ["claude-sonnet-4.6", "gpt-5.5", "gemini-3.1-pro"],
  cache: { semantic: true, ttlSeconds: 3600 },
  guardrails: {
    pii: "redact",
    region: "us",
    audit: true
  },
  metadata: {
    tenant: "acme",
    workflow: "support-copilot"
  }
});

console.log(answer.model);
console.log(answer.traceId);
console.log(answer.cost);
Governance surface

Platform controls mapped to the risks teams actually operate

Risk surfaceRouterRuntimeBusiness controls
Cost controlper-request budget capsrun timeoutsusage alerts and invoices
Reliabilityprovider failoverretry and replaySLA reporting
Securityregional routingscoped toolsRBAC and SSO
CompliancePII policy gatesaudit spansDPA, BAA, MSA
Rollout path

Four weeks from proxy migration to production governance

Week
01

Mirror existing calls

Send traffic through the OpenAI-compatible adapter while preserving your current model behavior.

Week
02

Turn on policy routing

Route low-risk flows by cost or latency, then compare trace output against your baseline.

Week
03

Add cache and fallback

Enable semantic cache, provider fallback, budget ceilings, and alerts for spend anomalies.

Week
04

Govern production

Lock SSO, RBAC, PII handling, retention, and audit export before expanding to higher-risk workloads.

Live experience

Try routing in real time

Pick a goal and strategy to see how policies, caching and fallback impact cost, latency, and reliability.

Live Routing Demo

6 providers • 50+ models

Supported Providers
Goal
Strategy

Selected strategy: Balanced.

✨ Supports 50+ models • Auto-fallback • Semantic cachingComing soon: Open-source models (Qwen, Yi, Baichuan)
Production standards from day one

Get started in minutes

SDKs and quickstart guide for Next.js, Python, and more.