Best AI Gateways & LLM Routing Proxies

These tools address a critical operational challenge: as AI agents and applications grow in production, teams need to manage multiple LLM providers (Claude, GPT, Gemini, open-source models) without rewriting application code. Gateways solve several reliability concerns—if one provider experiences downtime or hits rate limits, requests can automatically failover to another, improving application resilience. For teams running production AI workloads at scale, cost visibility and budget controls are essential as API bills can accumulate quickly across providers and models.

How to Choose

Deployment model: If you need on-premises deployment, full infrastructure control, or want to avoid external cloud dependencies for gateway routing, LiteLLM's open-source foundation provides that flexibility. If you prefer a managed SaaS platform with hosted infrastructure and don't want operational overhead, Portkey's cloud-first approach handles everything.

Cost accountability and monitoring: Both track spend per provider and model. LiteLLM's open-source tier runs cost tracking internally—useful if you have compliance requirements around where billing data flows. Portkey's usage-based pricing model ($59.99 per 500,000 units) works well for teams with predictable or metered usage patterns; LiteLLM's Enterprise tier uses custom quotes, which suits organizations with variable, high-volume, or unpredictable workloads.

Governance and team structure: Portkey is designed for production use from day one, with built-in RBAC, budget enforcement per team, and audit logs—essential if you're managing multiple internal teams or customer workloads. LiteLLM's open-source tier is lighter on governance; Enterprise adds SSO, JWT Auth, and audit capabilities, but requires negotiation and is aimed at large infrastructure teams.

Integration scope: LiteLLM explicitly supports 100+ LLM providers, making it ideal if your stack uses niche or older models. Portkey focuses on mainstream providers but emphasizes observability—logging request latency, error rates, and cost per integration—valuable if debugging production issues is a priority.

Comparison