Portkey AI Gateway
Production LLM gateway with routing, guardrails, MCP support, and observability.
Open source
Best when production agents need a unified API gateway across 200+ LLM providers with built-in load balancing, fallbacks, caching, guardrails, and cost tracking.
Selection advice
Choose Portkey AI Gateway when you need production-grade routing and guardrails across providers. It adds reliability layers that prompt-level logic cannot provide.
Quick comparison
Portkey targets production routing with guardrails and MCP-aware reliability layers. Helicone and LiteLLM are common alternatives when observability-first proxying or Python-native provider abstraction is the priority.
| Portkey AI Gateway | Helicone | LiteLLM | |
|---|---|---|---|
| Best for | Multi-provider routing with guardrails and fallbacks | Fast proxy-based LLM observability and caching | Python-centric unified LLM API across providers |
| Reliability features | Load balancing, fallback chains, guardrails | Gateway caching and failover via proxy | Router fallbacks and budget limits |
| Observability | Unified gateway logs and cost tracking | Request-level dashboards and sessions | Logging hooks; often paired with Langfuse |
| Tradeoff | More platform surface than a thin proxy | Less opinionated about guardrail policy | Strong dev ergonomics; less managed policy layer |
Best for
- LLM routing
- provider fallbacks
- cost control
- production gateway
- MCP integration
Not ideal for
- teams using a single provider only
- projects that don't need multi-provider routing
Core concepts
gatewayroutingguardrailsMCPfallbacks
Minimal implementation shape
Configure multiple LLM providers through Portkey, define fallback chains and guardrail checks, then route all agent traffic through a single API endpoint with unified observability.