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Pick 2–5 published tools. Values come from our evidence-checked profiles — an em-dash means unknown, never “no”. Ranking rows reflect the current published methodology for the comparison context.
LiteLLM vs Kong AI Gateway
Context: LLM Gateways & Model Routing Platforms · 23 rows
| Attribute | LiteLLMBerriAI | Kong AI GatewayKong |
|---|---|---|
| Identity · Taxonomy | ||
| Documentation | https://docs.litellm.ai/docs/ | https://developer.konghq.com/ai-gateway/ |
| Tags | llm-gateway, proxy, open-source, routing | llm-gateway, api-gateway, kubernetes, mcp, microservices |
| Platform · Deployment | ||
| Delivery model | self-hosted | SaaS, self-hosted |
| Pricing · Licensing | ||
| Free plan | true | — |
| Free trial | true | — |
| License model | open-source | open-source |
| Pricing model | free, custom | custom |
| Source repository | https://github.com/BerriAI/litellm | https://github.com/Kong/kong |
| SPDX license IDs | MIT | Apache-2.0 |
| Trial length | 30 | — |
| Features · Product · Fit | ||
| Core capabilities | Unified OpenAI-compatible API to 140+ LLM providers, Virtual keys with per-key, team, and org budgets, Auto routing, load balancing, and caching, Guardrails including PII masking and prompt injection protection, Usage analytics, spend tracking, and chargeback, Unified access for LLMs, agents, and MCP servers | Multi-LLM routing with provider failover, Semantic caching and load balancing, PII sanitization and semantic prompt guards, Token quotas, chargeback, and spend tracking, MCP server governance and authentication, L7 observability with logging and tracing |
| Api · Integrations · Ecosystem | ||
| API available | true | true |
| CLI | true | — |
| Enterprise SSO | OIDC | — |
| Support · Docs · Services | ||
| Support channels | community, enterprise support | — |
| Audience · Use · Case | ||
| Best for | Platform and engineering teams that want to self-host one OpenAI-compatible gateway in front of many LLM providers, with budgets, routing, and guardrails. | Organizations with existing API gateway or Kubernetes infrastructure that want LLM, MCP, and agent traffic governed through one gateway platform. |
| Company-size fit | mid-market, enterprise | SMB, mid-market, enterprise |
| Primary use cases | Central LLM gateway for AI applications, Spend tracking and chargeback across teams, Model routing and load balancing | Governing LLM traffic in API infrastructure, MCP and agent-to-agent traffic governance, Provider switching and failover |
| Skill level | advanced | advanced |
| Lifecycle · Versioning | ||
| Changelog/release notes | https://docs.litellm.ai/release_notes | — |
| Current version | v1.100.0 | — |
| Maintenance status | active | active |
| Vendor · Maintainer | ||
| Vendor type | commercial | commercial |
“—” means unknown (we never treat missing evidence as a negative). Cells reflect each value's current evidence state; superseded or stale values are excluded. Ranking rows are editorial assessments under the cited methodology version — see the LLM Gateways & Model Routing Platforms category for definitions.