LiteLLM vs Kong AI Gateway (2026): AI Gateways Compared — and Where Kosmoy Fits

Three shapes on the same ten axes. LiteLLM (violet) and Kong AI Gateway (orange) both peak on Gateway & Policy Control and cluster low on the governance axes — that is the gateway category's signature. Kong reaches a little further on guardrails and MCP/agent traffic; LiteLLM a little further on FinOps depth and open-source coverage. Kosmoy (blue) trades some raw gateway breadth for reach across inventory, compliance and agent containment. Read it as area: the two gateways compete on one spoke; the suite covers the web.

Cost and open-source coverage. LiteLLM's core is free and MIT-licensed, and its FinOps and MCP gateway ship in that core; Kong gates its advanced AI plugins (semantic prompt guard, PII sanitizer, LLM analytics, ai-rate-limiting-advanced) behind Enterprise/Konnect tiers.

FinOps depth. Spend attribution and budgets per org/team/project/key/tag, soft-budget alerts, spend reports, Prometheus and OpenTelemetry v2 metrics, and Logs v2 with tool-call tracing make LiteLLM a deep cost-governance layer for LLM traffic.

Velocity and provider breadth. Weekly releases, 100+ providers behind one OpenAI-compatible API, and a Rust migration targeting sub-1ms overhead give it a cadence a plugin roadmap does not match.

Traffic breadth. Kong markets AI Gateway 3.14 as covering LLM, MCP and agent-to-agent (A2A) traffic in one runtime — "the most comprehensive AI gateway for the agentic era", by its own claim.

MCP governance depth. MCP Tool ACLs, OAuth2 scope-based tool filtering and RFC 8693 token exchange, plus an MCP Registry (tech preview), give per-tool authorization LiteLLM's per-server controls do not match.

Guardrails in the data path and a battle-tested core. Native semantic prompt guard and PII sanitizer plus partner integrations (Lakera Guard, AWS Guardrails, Azure Content Safety, Model Armor) sit on a mature, ~43.8k-star Apache-2.0 gateway engine.

Both LiteLLM and Kong answer “how do we route and govern model, MCP and agent traffic?” Neither answers “what AI are we running across the organization, is it compliant, and what happens when an agent misbehaves?” Those are different questions, and in a regulated enterprise they arrive together.

Kosmoy includes the gateway both products are — one OpenAI-compatible policy point with guardrails, RBAC, budgets and logging — but wraps it in the three layers a gateway leaves out: a risk-tiered inventory of every model, MCP server and agent (including a master agent registry that pulls from Foundry, Bedrock, Vertex, Salesforce and ServiceNow); EU AI Act, ISO 42001 (aligned) and NIST AI RMF evidence built from registry state plus gateway logs; and kernel-enforced Action Capsule containment for agents that act.

So the honest framing is not “Kosmoy beats LiteLLM and Kong at being a gateway” — they are capable gateways, and Kong in particular covers MCP and agent traffic well. It is that a gateway is one spoke. If the requirement is the whole web — inventory, gateway, compliance and containment in one self-hosted platform — that is a suite decision, not a gateway decision.

For a team whose problem genuinely is model, MCP and agent traffic, pick on the axis that matters: LiteLLM to own an OSS-first proxy with deep FinOps, Kong to extend an existing API-gateway estate across all three AI traffic patterns. Both self-host, so a later move is largely a configuration exercise.

For an enterprise that has to prove control over all of its AI — not just route it — the choice is not between these two gateways but between a point tool and a suite. Kosmoy can also sit in front of either: some teams keep an open-source gateway for experimentation while Kosmoy holds the inventory, compliance evidence and containment for what reaches production.