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For a production AI gateway, shortlist products by the controls and telemetry they document—not by a universal “best” ranking. Kong AI Gateway documents routing, access control, usage analytics, audit logs, OpenTelemetry and governance features, with a Konnect-managed control plane and self-managed data planes. LiteLLM documents deployment options including on-premises and air-gapped environments, plus budgets, rate limits and cost visibility. The available documentation supports comparing these two candidates, but not ranking the wider 2026 market.
What should an AI gateway govern and make observable?
An AI gateway can put a shared layer between applications and upstream AI providers. Depending on the product and configuration, it can centralize model routing, credentials, access policy and usage visibility. Those functions are not automatic guarantees: verify what the gateway enforces, what it records, and which systems receive the data.
“Governance” is not one checkbox. Separate identity and access control from budgets and rate limits; distinguish audit records from safety policies; and assess personally identifiable information (PII) handling on its own. Likewise, observability should be evaluated as specific signals and workflows—not just the presence of a dashboard.
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- Traceability: Are audit records available, and can metrics or traces be exported to existing systems?
- Payload privacy: Are prompts and responses captured? Is capture optional, and where does the data go?
- Enforcement: Which access, budget, rate, safety and PII controls are enforced by the gateway itself, and which depend on integrations?
How do the documented candidates compare?
The table uses vendor documentation and marks a capability “not stated” when the cited page does not establish it. It is not a performance test or a complete market ranking.
| Evaluation area | Kong AI Gateway | LiteLLM |
|---|---|---|
| Routing and usage visibility | Documents routing and load balancing, plus request, token, error and latency analytics. Kong AI Gateway overview | Documents provider/model routing and per-key, team and organization cost visibility. Request, token, error and latency measures are not stated on the cited page. LiteLLM About |
| Access, budgets and rate limits | Documents access control; specific budgets and rate limits are not stated on the cited overview. Kong AI Gateway overview | Documents budgets and rate limits. The cited page also describes enterprise SSO/SCIM, OIDC/JWT and administrative roles. LiteLLM About |
| Audit, export and dashboards | Documents audit logs, metrics exporters, OpenTelemetry and Konnect dashboards. Kong AI Gateway overview | Enterprise audit logs are described. Trace/metric export, dashboards and retention/query details are not stated on the cited page. LiteLLM About |
| Safety and PII | Documents prompt guard policies and a PII sanitizer. It also documents integrations with external safety providers and Amazon Bedrock guardrails; those integrations should not be mistaken for Kong-owned safety services. Kong AI Gateway Data Governance | Safety guardrails and PII handling are not stated on the cited page. LiteLLM About |
| Deployment and control plane | Documents a Konnect-managed control plane with self-managed data-plane nodes. This described topology does not provide a fully self-hosted control plane. Kong AI Gateway architecture | Describes installation in cloud, on-premises, Kubernetes or air-gapped environments, and an enterprise multi-region control plane. The cited page does not specify equivalent control-plane/data-plane details. LiteLLM About |
| Prompt/response data path | Default telemetry includes usage, cost and latency metadata rather than request/response bodies; forwarding payloads is opt-in. The control plane is outside the user-data path. Kong AI Gateway architecture | Default prompt/response capture behavior and telemetry destinations are not stated on the cited page. LiteLLM About |
When is Kong AI Gateway a plausible fit?
Consider it when you need a shared policy and telemetry layer
Kong’s documentation describes coverage for LLM, MCP and A2A traffic alongside routing, access control, analytics and observability. That may suit an organization already using Kong, or one seeking a common layer across API, LLM and agent traffic. The fit depends on whether its documented Konnect control-plane arrangement works with your hosting and data-handling requirements. Kong AI Gateway overview
Check what “built in” means for safeguards
Kong documents prompt guard policies and a PII sanitizer, as well as integrations with external safety providers and Amazon Bedrock guardrails. Treat the latter as integrations: establish which party supplies, configures and operates the safeguard, and how failures are handled. Kong AI Gateway Data Governance
Rank #2
Validate the telemetry path, not only the dashboard
Kong says the control plane stays outside the user-data path and that default telemetry consists of usage, cost and latency metadata rather than request/response bodies; payload forwarding is opt-in. Confirm the data classes and destinations for your exact deployment and configuration, especially if enabling payload forwarding. Kong AI Gateway architecture
When is LiteLLM a plausible fit?
Consider it when deployment flexibility and spend controls are priorities
LiteLLM’s official page describes cloud, on-premises, Kubernetes and air-gapped installation options, as well as provider/model routing, per-key/team/org cost visibility, budgets and rate limits. Its enterprise feature list includes SSO/SCIM, OIDC/JWT, audit logs, secret management and key rotation, administrative roles, and a multi-region control plane. Confirm the relevant deployment and features for the edition and configuration you plan to use. LiteLLM About
Rank #3
Do not infer observability depth from cost visibility
The cited LiteLLM page establishes cost visibility and enterprise audit logs, but does not specify request/token/error/latency dashboards, trace or metric export, prompt/response capture defaults, or telemetry destinations. Ask for documentation of those exact behaviors before treating it as equivalent to a gateway with explicitly documented signals and export paths. LiteLLM About
How should a team make the shortlist decision?
- Write down the enforcement requirements. Specify identity sources, authorization boundaries, budget owners, rate-limit scope, audit needs, safety policies and PII handling. Ask which controls are native, which require integrations, and which are outside the gateway.
- Map every data class and destination. Include prompts, completions, metadata, credentials, audit events and traces. Record defaults, opt-in settings, retention, access and export destinations for the deployment you will actually run.
- Match deployment to your boundary. Establish where the control plane and data plane run, which components are managed, and whether the arrangement satisfies on-premises, VPC or air-gap requirements.
- Test your operational cases. Validate routing, streaming, provider/model compatibility, failure handling, key rotation and policy behavior with your actual applications. The cited pages do not provide equivalent independent performance or reliability evidence, so do not assume parity.
- Confirm edition, support and price directly. Feature packaging and pricing can change; the cited pages do not establish comparable current prices or a like-for-like tier matrix.
What is not established by the available documentation?
The cited material does not support a defensible overall winner across the 2026 AI gateway market, a complete market-wide shortlist, or a head-to-head performance conclusion. The LiteLLM page is vendor-authored and does not substantiate an independent comparison; its provider/model counts and usage claims should not be read as audited coverage or quality rankings. Nor do the cited pages establish a like-for-like current price comparison.
For a procurement decision, treat documented feature lists as a starting point. Require equivalent evidence for retention, queryability, control-plane dependencies, compatibility, support and operational reliability before comparing vendors on those dimensions.
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