Kong AI Gateway Alternatives: How to Pick a Different Layer When Kong Does Not Cover Your Workload

Kong AI Gateway is the AI-focused plugin family on the Kong data plane: multi-provider LLM routing, semantic caching, prompt templates, prompt guards, AI-aware rate limiting, and per-consumer token attribution. The product fits teams already running Kong as their HTTP data plane that want AI traffic management on the same operator surface. Teams that need different things from their LLM traffic layer (open-source observability, identity-bound policy enforcement, hosted multi-provider routing without operating Kong, regulatory audit records that survive EU AI Act Article 12 review) pick a different layer. I want to walk through the credible Kong AI Gateway alternatives, by use case, and where each one fits.

TL;DR

Kong AI Gateway covers the operational LLM gateway use case on top of a Kong-resident HTTP data plane. Alternatives by use case: Helicone or Langfuse for open-source observability without operating Kong, Portkey for a hosted multi-provider gateway with observability built in, LiteLLM for OpenAI-SDK-compatible multi-provider routing without Kong, MLflow AI Gateway for MLflow-anchored experimentation workflows, Databricks AI Gateway for Databricks-resident workloads, and DeepInspect for identity-bound policy enforcement and regulatory audit records on top of any LLM endpoint.

Use case 1: open-source observability without Kong

Teams that want application-side observability into LLM calls (latency, cost, custom property breakdowns, prompt versioning, evaluation scores) without operating the Kong data plane pick an observability-first product.

Helicone

Helicone is an open-source LLM observability platform with an async proxy and a self-hosted gateway. The dashboard exposes captured calls by user, model, route, custom property, latency, and cost. The async proxy mode does not require SDK changes for most applications. Caching, rate limiting, retries, and fallbacks ship as observability-adjacent features.

Langfuse

Langfuse is an open-source LLM observability platform that captures traces via in-process SDKs. The trace captures multi-step spans, prompt template versions, evaluation scores, and user feedback. The dashboard supports prompt experimentation workflows and side-by-side completion comparison.

The architectural distinction between Helicone and Langfuse for this use case: Helicone deploys as a proxy and captures the call data at the network layer; Langfuse deploys as an SDK inside the application code and captures the application-side trace. Teams that prefer not to add SDK calls inside the application code pick Helicone. Teams that want fine-grained application-side trace control pick Langfuse.

Use case 2: hosted multi-provider gateway with observability built in

Teams that want a hosted (or self-hosted enterprise) LLM gateway with multi-provider routing plus observability on the same control plane pick a closed-source platform.

Portkey

Portkey is an LLM gateway and observability platform with multi-provider routing across 200+ providers, retries, fallbacks, conditional routing, caching, load balancing, cost tracking, traces, evaluations, prompt management, and guardrails. The hosted tier covers small and medium deployments; the enterprise tier supports self-hosted deployment.

Portkey's architectural sweet spot is the platform team that wants one control plane for operational gateway features plus the engineering team's observability surface. The trade-off versus Kong AI Gateway is the data plane: Portkey is a managed (or self-hosted enterprise) product; Kong is an open-source data plane with a Kong-Konnect control plane option.

Use case 3: OpenAI-SDK-compatible multi-provider routing without Kong

Teams that want the OpenAI SDK as their single integration surface and multi-provider routing as a side effect, without running Kong, pick an open-source LLM proxy.

LiteLLM

LiteLLM is an open-source LLM proxy with an OpenAI-compatible API surface across 100+ providers. The proxy server handles routing, retries, fallbacks, basic key management, virtual keys with per-team budgets, and rate limits. Self-hosted deployment runs as a Python process.

The architectural distinction versus Kong AI Gateway is the data plane assumption. LiteLLM assumes the application speaks the OpenAI API and runs LiteLLM as the translation layer. Kong AI Gateway assumes the application speaks the OpenAI API and runs Kong as the data plane (with the AI plugins on top). Teams that already operate Kong pick Kong AI Gateway; teams that prefer a standalone Python proxy pick LiteLLM.

Use case 4: MLflow-anchored experimentation workflows

Teams running LLM evaluations, prompt experimentation, and offline batch inference inside MLflow pick an MLflow-anchored gateway.

MLflow AI Gateway

MLflow AI Gateway (formerly MLflow Deployments) is an open-source MLflow component that registers LLM provider endpoints under named routes that MLflow client code calls. The MLflow tracking integration captures the call inside an MLflow run for offline review and comparison.

The architectural distinction versus Kong AI Gateway is the workflow assumption. MLflow AI Gateway assumes the call is part of an MLflow run, with tracking captured in MLflow's experiment surface. Kong AI Gateway assumes the call is operational LLM traffic from production applications, with the operational concerns handled by Kong plugins. Teams running offline experimentation pick MLflow AI Gateway; teams running production LLM traffic pick Kong AI Gateway.

Use case 5: Databricks-resident workloads

Teams running LLM inference primarily inside Databricks Model Serving pick the Databricks-native control surface.

Databricks AI Gateway

Databricks AI Gateway, part of Mosaic AI Gateway, sits inside Databricks Model Serving. The control plane attributes usage to Unity Catalog principals, applies AI guardrails (keyword filters, PII detection), and writes payload tables to Delta tables in Unity Catalog. The gateway covers both Databricks Foundation Model APIs and external model endpoints that Databricks brokers.

The architectural distinction versus Kong AI Gateway is the identity boundary. Databricks AI Gateway assumes the caller is a Unity Catalog principal and the model endpoint is a Databricks model serving endpoint. Kong AI Gateway is agnostic to the identity model and runs in front of any LLM endpoint. Teams whose LLM workload lives inside Databricks pick Databricks AI Gateway; teams whose workload spans Databricks and non-Databricks endpoints pick Kong (or compose Databricks AI Gateway with a cross-endpoint enforcement layer).

Use case 6: identity-bound enforcement and regulatory audit records

Teams subject to EU AI Act Article 12, Fannie Mae LL-2026-04, HIPAA, DORA, FedRAMP, ISO 42001, or any sector regime that requires identity-bound per-decision audit records pick an enforcement-first product.

DeepInspect

DeepInspect sits at the HTTP request boundary as a separate enforcement layer. It evaluates identity-bound policy on every request, classifies prompt data against the regulated data types the organization recognizes, and commits a per-decision audit record with cryptographic integrity. The decisions are deterministic, fail-closed, and independent of the model's behavior.

The architectural distinction versus Kong AI Gateway is the audit format. Kong's operational logs satisfy the existence requirement of an audit. DeepInspect's per-decision audit records satisfy the traceability requirement that Article 12 and the Fannie Mae LL-2026-04 review apply. The record carries the natural-person identity (not the API key alone), the policy version active at decision time, the data classification outcome, the policy decision outcome, and the cryptographic integrity signature.

DeepInspect composes with Kong AI Gateway by sitting in front of it for regulated workloads that also need Kong's operational features. The composition pattern preserves Kong's plugin-based operational layer and adds the regulatory audit layer above it.

Picking between the alternatives

The right alternative depends on what the team needs from the LLM traffic layer.

• Open-source observability without Kong: Helicone (proxy) or Langfuse (SDK).
• Hosted multi-provider gateway: Portkey.
• OpenAI-SDK-compatible routing without Kong: LiteLLM.
• MLflow-anchored experimentation: MLflow AI Gateway.
• Databricks-resident workload: Databricks AI Gateway.
• Identity-bound policy enforcement and regulatory audit records: DeepInspect.
• Operational gateway plus regulatory audit: Kong AI Gateway plus DeepInspect (composed).

Most production deployments end up with two layers: an operational gateway (Kong, Portkey, LiteLLM, MLflow, Databricks) and a regulatory audit layer (DeepInspect). The two compose without overlap because the operational concerns and the regulatory audit obligation are different responsibilities.

DeepInspect

DeepInspect sits between calling applications and any LLM endpoint over HTTP. It evaluates identity-bound policy on every request, classifies prompt data against the regulated data types the organization recognizes, commits per-decision audit records with cryptographic integrity, and produces the record format that EU AI Act Article 12 and Fannie Mae LL-2026-04 reviewers accept. The architecture composes with any of the alternatives above by sitting at the request boundary for the regulatory audit layer.

The composition gives organizations the operational features they prefer from Kong, Portkey, LiteLLM, MLflow, or Databricks and the per-decision audit records they need for the workload to survive regulatory review. The audit pipeline consumes one record format regardless of which operational gateway selected the upstream provider for any given request.

If you are running Kong AI Gateway today and the EU AI Act August 2 deadline applies to the workload, let's talk.