LiteLLM vs Kong AI Gateway

If your organization already runs Kong as its API edge, the AI Proxy plugin is the stronger starting point, because LLM traffic becomes one more route governed by the same gateway that already handles authentication, rate limiting and traffic policy for your other services. The AI Proxy plugin ships in the open-source Kong Gateway repository and needs Kong Gateway 3.6 or later, so a team on a current 3.x line can evaluate it without a new product. The practical gain is one place to review policy, one set of credentials to rotate and one set of logs to forward to your SIEM, instead of a second gateway with its own configuration, access model and upgrade calendar.

That advantage shrinks in three situations. If Kong sits behind a cloud load balancer or another edge product and is only used for a few internal APIs, you gain less from consolidating AI traffic into it. If your team does not need Kong's plugin ecosystem, a dedicated gateway such as LiteLLM may be simpler to operate. And if your governance needs center on per-user virtual keys and spend budgets, confirm that Kong's open-source plugins cover those controls before assuming the existing estate is enough, because the load balancing, semantic routing and failover features sit in Kong's enterprise offering through AI Proxy Advanced, which needs 3.8 or later.

Before deciding, ask your Kong administrators which version and license tier are in production, and ask the vendor to confirm in writing which AI controls are included in your current agreement. Then map each governance requirement, such as who may call which model, how cost is attributed and where prompts and responses are logged, to a named plugin or to a LiteLLM feature. Run that mapping against a test environment with your real routes before committing. Performance is worth measuring on your own workload rather than assuming either gateway wins on latency.

Both gateways sit in the request path to every model provider, so implementation starts with where the gateway runs and who holds the provider keys. For LiteLLM, the typical scope covers a proxy deployment in your own cloud account or data center, provider credentials for each model endpoint, virtual keys issued per user, team or project, and budget and rate-limit settings tied to those keys. For Kong, the scope usually means adding the AI Proxy plugin to an existing or new Kong Gateway deployment, configuring provider credentials and model routes, and deciding which consumers and services the plugin applies to. In both cases, secrets belong in your own secret store rather than in configuration files, and the team should confirm how each product reads provider keys in your environment before work begins. Identity integration, audit logging and fallback testing are separate workstreams in each case and should be listed as such in the written scope.

The steps that most often run long are identity integration, budget policy design and retrofitting an existing Kong estate. SSO and SAML are listed as enterprise features for LiteLLM, so an organization that needs single sign-on for administrators or users should confirm the edition, the identity provider, and how group membership maps to teams before quoting. Budget policy is a governance decision, not a configuration task: someone has to agree how spend is allocated across teams, who can raise limits, and what happens when a budget is exhausted. Kong adds its own delay when the estate already runs many services, plugins and custom policies, because enterprise features such as AI Proxy Advanced need the enterprise offering and a minimum version, and upgrading shared gateways touches teams that were not part of the AI project. Audit log export to your SIEM and fallback testing against real provider failures also take longer than teams expect.

Timelines depend on the written scope, so the reader should ask for each workstream to be named and sized before agreeing a date. A fixed-scope engagement records the deployment target, the identity provider, the audit destinations, the number of provider endpoints, the fallback scenarios to test and the handover documents. Ask for the quote to separate one-time work (installation, hardening, integration, testing) from recurring work (upgrades, monitoring, retainers for ongoing support), and ask which items depend on your teams, such as identity administrators, security reviewers or Kong platform owners. Confirm the Kong version in use and the LiteLLM edition you need, since both change what is available. Any timeline quoted without these inputs should be treated as provisional.

DeployOpen is a Chainscore Labs company that plans, implements, integrates, hardens, backs up and documents self-hosted open-source platforms inside your own cloud accounts, data centers or air-gapped networks. For an LLM gateway, that work usually starts with a decision you have to make before any install: whether your organization already runs Kong, which would put Kong AI Gateway's plugins close to existing gateway operations, or whether you need LiteLLM's virtual keys, budgets and per-team controls as the governance layer. We help you map those requirements to specific configuration, such as how keys are issued per user, team or project, where budgets are enforced, and which identity provider signs in administrators. Engagements are fixed-scope, so the agreed scope, deliverables and timelines are written down before work begins, and a quote follows from that written scope.

DeployOpen is not an official partner, reseller or certified provider of LiteLLM or Kong, and we do not recommend either product by default. The right answer depends on your environment, your edition requirements and your governance model. Several capabilities that often drive this decision, including SSO/SAML and audit logs in LiteLLM and load balancing and semantic routing in Kong's enterprise tier, carry licensing conditions that you should confirm directly with each vendor before you commit. We can help you build the comparison against your own requirements and document the result. Where you need evidence for a compliance assessment, we can prepare systems, controls and records for an auditor to review, but we do not issue audit reports, attestations or certifications. Those come from independent auditors and certification bodies.