AI Gateway & API Management
One API, many models. Unified routing, cost control, fallback chains, and observability for your entire LLM infrastructure.
Why a Gateway
Without a gateway, every service talks directly to LLM providers. That creates four critical problems that compound as you scale.
Vendor Lock-In
Problem
Every provider has a different API schema, auth method, and response format. Switching from OpenAI to Anthropic means rewriting every integration.
Solution
One unified API across all providers. Swap models with a config change, not a code change.
No Fallback
Problem
When OpenAI goes down, your entire product goes down. No automatic failover, no degraded mode, no fallback to a secondary provider.
Solution
Automatic fallback chains with configurable priority, timeout thresholds, and health checks per provider.
Hidden Costs
Problem
No visibility into per-request, per-user, or per-feature token spend. Costs spike silently until the monthly invoice arrives — and by then, the budget is blown.
Solution
Real-time cost tracking per request with budget enforcement, alerts, and per-project spending limits.
No Observability
Problem
You can't see latency distributions, error rates, quality drift, or token usage patterns. Debugging production LLM issues is guesswork.
Solution
Structured traces for every call — latency, tokens, cost, model version, quality scores — with dashboards and alerting.
Gateway Architecture
Eight stages from request to response. Each stage adds a layer of intelligence, control, and observability to every LLM call.
Request
Client sends a standard API request — model, messages, temperature, max_tokens. The gateway normalizes this into a provider-agnostic internal format regardless of origin.
Core Features
Six capabilities that transform a simple proxy into a production-grade AI infrastructure layer.
Unified API
One interface for OpenAI, Anthropic, Google, Mistral, Cohere, and self-hosted models. Same request format, same response schema, same SDK. Swap providers with a single parameter change.
Model Routing
Route requests based on cost, latency, or quality targets. Send simple queries to fast cheap models, complex reasoning to frontier models. A/B test models on live traffic with percentage-based splits.
Fallback Chains
Automatic failover when a provider is down, slow, or returning errors. Define priority chains — OpenAI → Anthropic → Meta (self-hosted) — with configurable timeout and error thresholds per link.
Semantic Caching
Deduplicate similar queries using embedding-based similarity matching. When a user asks a semantically equivalent question, serve the cached response instantly — saving both latency and cost.
Cost Controls
Set per-project, per-user, and per-model spending budgets. Real-time enforcement rejects requests when limits are hit. Alerts fire at 80%, 90%, and 100% thresholds. Daily and monthly budget cycles.
Observability
Structured traces for every request — latency (TTFT, total), token counts (input/output), cost, model, cache status, and quality scores. OpenTelemetry-compatible with built-in dashboards.
Gateway Solutions
The right gateway depends on your scale, team, and infrastructure. Here's how the leading options compare.
LiteLLM
Open-source Python proxy supporting 100+ LLM providers. Lightweight, easy to self-host, with built-in spend tracking and virtual keys.
Strengths
Trade-offs
Portkey
Production-grade AI gateway with advanced routing, caching, guardrails, and observability. Managed or self-hosted with enterprise SSO.
Strengths
Trade-offs
Helicone
Observability-first gateway that captures every LLM interaction. Strong analytics, cost tracking, and prompt versioning with minimal integration effort.
Strengths
Trade-offs
Kong AI Gateway
Enterprise API gateway with AI plugins. Leverages existing Kong infrastructure for auth, rate limiting, and traffic management with AI-specific extensions.
Strengths
Trade-offs
Custom-Built
Build your own gateway when you need deep integration with existing infrastructure, custom routing logic, or proprietary caching strategies.
Strengths
Trade-offs
Cost Impact
A well-architected gateway pays for itself within weeks. These are measured outcomes from production deployments.
Cost Reduction
from semantic caching
Uptime
from fallback chains
Developer Velocity
from unified API
Before vs After Gateway
Without Gateway
- ×Direct provider calls from every service
- ×API keys scattered across codebases
- ×No centralized logging or cost tracking
- ×Provider outage = product outage
With Gateway
- ✓Single entry point with unified API
- ✓Centralized key management and rotation
- ✓Full observability with per-request cost
- ✓Automatic failover maintains 99.9% uptime
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Unify your AI infrastructure.
Describe your model providers and usage patterns. We'll architect the gateway, routing rules, and cost controls.