LiteLLM vs Kong AI Gateway: Which One to Self-Host in 2026

LiteLLM is a lightweight, MIT-licensed proxy you self-host with a small Postgres database, putting 100+ LLM providers behind one OpenAI-compatible API; Kong AI Gateway is a mature, plugin-based API-management platform, built on Apache-2.0-licensed Kong Gateway, that adds LLM, MCP, and agent-to-agent (A2A) traffic governance on top of infrastructure many enterprises already run (Source: LiteLLM GitHub, Source: Kong AI Gateway). Both projects expose the same OpenAI-compatible surface, so switching between them is close to a base-URL swap. Inference: this follows from both vendors documenting standard-endpoint compatibility, not a migration either vendor benchmarks directly. The real decision is operational: LiteLLM asks you to run one container and a database; Kong asks you to adopt, or already own, a Kong Gateway deployment before its AI plugins do anything. If governance like PII redaction and SSO justifies Kong's enterprise license, Kong wins. If a fully open, single-binary proxy covers what you need, LiteLLM wins. The rest of this piece breaks down exactly where that line falls.

Key takeaways

• LiteLLM is MIT-licensed end to end; the proxy and SDK cost nothing beyond your own infrastructure.
• Kong AI Gateway's core ai-proxy plugin ships inside Apache-2.0 Kong Gateway (v3.6+), but PII sanitization, semantic caching, and advanced rate limiting are Enterprise/Konnect-only (Source: Kong ai-proxy Plugin Docs).
• LiteLLM tracks spend through Postgres-backed virtual keys with per-org/team/project budgets; Kong meters usage through its plugin pipeline and Konnect analytics.
• Neither gateway runs models. Both route to 100+ providers behind one API, so the choice is about who operates the routing layer, not which one is "smarter."

What LiteLLM and Kong AI Gateway actually are

An LLM gateway is middleware that sits between an application and multiple model providers, unifying the API surface and centralizing failover, cost tracking, and access control. LiteLLM is an open-source gateway maintained by BerriAI: a Rust-core proxy with a Python SDK that you deploy via Docker, Helm, or Terraform, backed by a managed Postgres instance for spend tracking and virtual keys (Source: LiteLLM GitHub). Kong AI Gateway is not a separate product so much as an AI-specific plugin suite (ai-proxy, ai-rate-limiting-advanced, semantic caching, prompt guards) layered onto Kong Gateway, the same platform many teams already run for REST and GraphQL traffic; Kong positions it as governing "all generative AI and agentic AI traffic with a single gateway" (Source: Kong AI Gateway). Neither product picks or hosts a model. Both decide how your calls to OpenAI, Anthropic, Bedrock, or a local model reach their destination.

LiteLLM vs Kong AI Gateway at a glance

Is LiteLLM open source?

Yes. The current release on PyPI, 1.93.0, lists its license as MIT with no dual-licensing (Source: LiteLLM PyPI). That covers the proxy server, the Python SDK, and the routing logic; there is no separate "core vs enterprise" split for the parts most self-hosters touch first. Some LiteLLM deployments do add a paid Enterprise tier for SSO, SCIM, RBAC, and audit logs, but that tier sits on top of a fully functional open proxy rather than gating basic routing or provider access. For a self-hoster comparing licenses, LiteLLM is the more straightforwardly open of the two: everything you need to run a working gateway is MIT from the start.

Can you self-host Kong AI Gateway?

Yes, with a caveat. The ai-proxy plugin that does the actual LLM routing ships inside Kong Gateway, Kong's Apache-2.0 open-source core, and requires Gateway version 3.6 or later; it deploys on-prem or through Konnect (Source: Kong ai-proxy Plugin Docs). That means the basic self-hosted path exists and costs nothing in licensing. The caveat is that Kong's more distinctive AI features, semantic prompt guards, PII sanitization, ai-rate-limiting-advanced, and Konnect's LLM analytics, sit behind an Enterprise or Konnect license, a pattern independently reported across three vendor comparisons (Source: Kong AI Gateway). So "self-hosting Kong AI Gateway" can mean two very different things: a free ai-proxy plugin on open-source Kong Gateway, or the full governance suite, which is where Kong's enterprise pricing enters the picture.

Spend tracking and cost attribution

LiteLLM tracks spend natively through its Postgres-backed virtual keys, with budgets settable per organization, team, project, key, or tag, so a single proxy instance can enforce a hard ceiling for one team while leaving another uncapped (Source: LiteLLM GitHub). Kong takes a different path: usage metering runs through its plugin pipeline, and Kong markets detailed cost monitoring as an "L7 observability and cost monitoring" capability tied to its Konnect analytics layer rather than a bare Gateway feature (Source: Kong AI Gateway). For a self-hoster who wants spend attribution out of the box without an add-on subscription, LiteLLM's Postgres-native budgeting is the more direct answer; Kong's equivalent visibility largely assumes you have, or will buy, Konnect.

Operator note: reading both config formats side by side

Operator note (first-hand): rather than trust either vendor's feature list, I read through the two projects' actual declarative config shapes. A minimal LiteLLM proxy is a single config.yaml:

model_list:
- model_name: gpt-4o
litellm_params:
model: openai/gpt-4o
api_key: os.environ/OPENAI_API_KEY
general_settings:
database_url: os.environ/DATABASE_URL

Running litellm --config config.yaml --port 4000 starts the whole proxy, and that database_url line is the entire Postgres dependency; there is no separate control plane to stand up first (Source: LiteLLM GitHub). Kong's equivalent is a plugin attached to an existing route inside kong.yml:

plugins:
- name: ai-proxy
route: my-llm-route
config:
route_type: llm/v1/chat
model:
provider: openai
name: gpt-4o

That block only does anything once a Kong Gateway instance (v3.6+) and its route and service objects already exist (Source: Kong ai-proxy Plugin Docs); you are configuring one plugin inside a platform, not standing up a standalone proxy from zero. Neither snippet is a benchmark; I did not run a live load test against either stack, so any latency claim would be a guess and I am not making one. But the shape of the two files is the real tradeoff in miniature: LiteLLM's file is the entire deployment, and Kong's is one plugin inside infrastructure you must already operate.

Which should you pick

Decision rule: pick based on whether you already run Kong for non-AI traffic, not on which gateway has more features on paper.

Most self-hosters land firmly on one side once they answer a single question: is there already a Kong deployment in the building. If yes, extending it is usually cheaper than standing up a second, unrelated proxy. If no, LiteLLM's minimal footprint rarely loses on cost or setup time.

FAQ

Is LiteLLM better than Kong AI Gateway?

Neither is universally better; they solve the self-host decision differently. LiteLLM is better for teams that want a fully open, minimal-footprint proxy with native spend tracking. Kong AI Gateway is better for teams that already run Kong and want LLM, MCP, and A2A governance unified with their existing API infrastructure and IAM.

Can you self-host Kong AI Gateway?

Yes. The ai-proxy plugin ships inside open-source, Apache-2.0 Kong Gateway starting at version 3.6, deployable on-prem (Source: Kong ai-proxy Plugin Docs). The free path covers basic LLM routing; PII sanitization, semantic caching, and advanced rate limiting require an Enterprise or Konnect license on top of that self-hosted core.

Is LiteLLM open source?

Yes. LiteLLM's proxy and SDK are MIT-licensed, confirmed on the current PyPI release, 1.93.0 (Source: LiteLLM PyPI). There is no dual license gating basic routing or provider access; an optional paid Enterprise tier adds SSO, SCIM, RBAC, and audit logs on top of a fully functional open core.

Does Kong AI Gateway support 100+ providers?

Kong AI Gateway routes to multiple LLM providers through its ai-proxy plugin configuration, but Kong does not publish a fixed "100+" provider count the way LiteLLM does. LiteLLM explicitly advertises 100+ providers behind one OpenAI-compatible API (Source: LiteLLM GitHub); Kong's provider support is broad but configured per plugin instance rather than marketed as a headline number.

LiteLLM vs Kong for spend tracking?

LiteLLM tracks spend natively with Postgres-backed virtual keys and budgets settable per organization, team, project, key, or tag, no add-on required. Kong meters usage through its plugin pipeline, but granular per-model cost breakdowns are part of Konnect analytics, a feature that sits outside the free, self-hosted core.

Related coverage

• LiteLLM vs OpenRouter: Self-Hosted Proxy or Managed Gateway
• LiteLLM vs Portkey: Which AI Gateway to Self-Host
• OWASP MCP Top 10: How to Fix All 10 Risks in 2026
• MCP tool poisoning: audit any server before installing

References

• Kong AI Gateway - https://konghq.com/products/kong-ai-gateway
• Kong ai-proxy Plugin Docs - https://developer.konghq.com/plugins/ai-proxy/
• Kong GitHub - https://github.com/Kong/kong
• LiteLLM GitHub - https://github.com/BerriAI/litellm
• LiteLLM PyPI - https://pypi.org/project/litellm/