Comparisons anchored on LiteLLM, the MIT-licensed OpenAI-compatible LLM gateway with virtual keys and budgets.
Choosing a self-hosted LLM gateway: LiteLLM's MIT proxy with virtual keys and budgets against Kong's AI plugins, where load balancing and semantic routing sit in the enterprise tier, judged by whether you already run Kong and what governance you need.
Whether to call models through the LiteLLM Python SDK inside each application or run the LiteLLM Proxy Server as a shared AI gateway for the organization. Covers what each offers (router, fallbacks, virtual keys, budgets, admin UI), the PostgreSQL and Redis the proxy needs in production, and the sizing the docs give.
Whether a self-hosted LiteLLM gateway can stay on the MIT edition or needs an Enterprise license key: what open source already covers for virtual keys, budgets and guardrails, and the SSO beyond five users, audit logs, RBAC, secret managers, per-team logging and licensed guardrail integrations that Enterprise unlocks.
Two MIT-licensed LLM gateways compared for self-hosting: LiteLLM's proxy with virtual keys, budgets and spend tracking in your own PostgreSQL, against Portkey's lightweight gateway with configs for retries, fallbacks and guardrails, whose README recommends Portkey Cloud. Covers what runs fully on your infrastructure, Portkey's acquisition by Palo Alto Networks in May 2026, and Portkey's documented LiteLLM migration path.
A Python proxy with virtual keys and spend tracking against an Envoy-based gateway configured through Kubernetes custom resources: provider coverage, rate limiting and quotas, the two-tier pattern for self-hosted model clusters, MCP routing, licenses and which team (application or platform) each suits.
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