Kong AI Gateway Alternatives: Helicone, LiteLLM, Portkey, and More

Kong AI Gateway alternatives fall into six distinct buyer paths. Helicone and Langfuse supply observability. Portkey and LiteLLM handle multi-provider routing. MLflow and Databricks fit platform-specific work. DeepInspect supplies identity-bound policy with request evidence. Kong's AI-focused plugin family provides multi-provider LLM routing, semantic caching, prompt templates, prompt guards, rate limiting, and per-consumer token attribution on the Kong data plane. I want to map each alternative to the control layer it actually supplies.

TL;DR

Kong AI Gateway covers operational LLM traffic management on a Kong-resident HTTP data plane. Helicone and Langfuse supply observability. Portkey and LiteLLM focus on multi-provider routing. MLflow AI Gateway and Databricks AI Gateway fit their respective platforms. DeepInspect evaluates identity-bound policy and writes request evidence for routed LLM traffic.

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 and fallback handling ship as observability-adjacent features. For a broader observability shortlist, see LLM observability tools.

Langfuse

Langfuse is an open-source LLM observability platform that captures traces via in-process SDKs. The trace captures multi-step spans and prompt versions, plus evaluation results 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 and retry behavior, basic credential management, virtual keys with per-team budgets, and rate limits. Self-hosted deployment runs as a Python process. LiteLLM alternatives examines the routing choices in more detail.

The architectural distinction is the data-plane assumption. LiteLLM assumes the application speaks the OpenAI API and runs LiteLLM as the translation layer. Kong runs that same API pattern on its data plane with AI plugins. Teams already operating Kong can retain that surface; teams that prefer a standalone Python proxy can select LiteLLM.

Use case 4: MLflow-anchored experimentation workflows

Teams doing offline evaluation work inside MLflow, including prompt experiments and batch inference, can use 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 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 handles operational LLM traffic from production applications through its plugins. MLflow suits offline experimentation; Kong suits production request management.

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. Read Databricks AI Gateway controls for the platform-specific review.

The architectural distinction 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 remains agnostic to the identity model and can run in front of any LLM endpoint. Teams whose workload lives inside Databricks can use its gateway. Cross-platform deployments can retain Kong or add an enforcement layer above the Databricks route.

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.

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