Top 5 AI Gateways 2026: Kong vs Portkey vs LiteLLM vs Cloudflare vs Helicone

Enterprise API Platform Integration

The AI Gateway runs as a Kong plugin set on the same gateway infrastructure that handles traditional API traffic. For organizations with existing Kong deployments, the operational model — admin API, declarative configuration, observability via Kong's stack — extends to AI traffic without parallel infrastructure. The integration value is the operational consistency, not raw AI-specific features.

Security Plugins for LLM Traffic

Built-in plugins handle prompt injection scanning, semantic caching with vector embeddings, request validation against expected schemas, cost budgets per consumer, and rate limiting per token volume. The security plugin set is the most enterprise-mature in the AI gateway category, fitting compliance-conscious deployments.

Routing and Fallback Chains

Portkey's configuration model supports declarative routing rules — primary provider, fallback chain on error or latency threshold, semantic caching with embedding-based lookup, request transformation between provider schemas. The configuration is the gateway's strongest production feature; failover patterns that would be application-layer code in alternative deployments become gateway configuration in Portkey.

Observability and Evaluation

The observability layer captures every prompt, completion, latency, token count, and cost per request. Built-in evaluations (groundedness, faithfulness, custom evaluators) score completions against quality criteria; the evaluation results feed into the routing decisions. For production teams running A/B tests across model providers, the integrated evaluation-and-routing loop reduces application-layer experiment infrastructure.

OpenAI-Compatible Universal Interface

LiteLLM exposes an OpenAI-compatible API surface for any backing provider — Anthropic Claude, Google Gemini, Cohere, Bedrock, Azure OpenAI, local models via Ollama or vLLM, dozens more. Existing application code using the OpenAI SDK works against LiteLLM as the backend without modification, which makes provider migration and multi-provider deployment materially easier than direct integration with each provider's SDK.

Self-Hosted Deployment Model

Production deployment is a Docker container plus a YAML configuration describing providers, routing rules, budgets, and rate limits. The deployment fits Kubernetes, ECS, plain Docker, or any container orchestration; the operational model is similar to any HTTP proxy. For organizations preferring self-hosted infrastructure over managed services, the deployment is materially simpler than running Kong or building equivalent capability.

Cloudflare Stack Integration

AI Gateway runs at Cloudflare's edge network alongside Workers, Pages, R2, D1, and the broader Cloudflare platform. For applications built on this stack, the integration is operationally tight — observability flows into Cloudflare's analytics, caching uses Cloudflare's KV or D1, and the gateway is configured through the same dashboard as the rest of the infrastructure. The value scales with Cloudflare commitment.

Edge Latency and Generous Free Tier

Running at the edge produces materially lower latency than going through a centralized gateway in one cloud region, particularly for globally-distributed user bases. The 100K-requests-per-day free tier is the most generous in the category; for moderate-volume applications, the gateway is functionally free.

LLM Observability Depth

Helicone's observability captures every LLM call with full prompt, completion, latency, token counts, and cost, indexed for searchable analysis. Production teams running thousands of LLM calls per minute use the dashboard to identify regression in prompt quality, latency spikes, cost outliers, and provider error patterns. The observability is the platform's primary value.

Proxy Mode Gateway Functionality

Routing requests through Helicone's proxy enables basic gateway functionality — caching with semantic similarity, rate limiting per user or key, basic provider fallback. For applications where observability is the primary need and gateway features are useful additions, the proxy mode delivers both in one deployment; for applications where the gateway is the primary need, dedicated alternatives are usually better fits.