I Compared the 5 Best Open-Source LLM Gateways for Enterprise AI

Your AI stack works fine with one model. The moment you add a second provider, a fallback, a rate limit, or a cost ceiling, everything breaks. Routing logic bleeds into application code, API keys get hardcoded, teams duplicate retry logic across services, and suddenly your “enterprise AI” is just a pile of half-working integrations held together by environment variables. That’s exactly the problem open-source LLM gateways solve, and in 2026, the options have matured enough to pick one seriously.

What an LLM Gateway Actually Does (and Why You Need One)

An LLM gateway sits between your application and every model provider OpenAI, Anthropic, Azure, Cohere, Mistral, local models, whatever. It normalizes the API surface, handles routing, enforces rate limits, logs requests, manages keys, and gives you failover without touching application code.

Without one, you’re solving the same problems in every service: retry logic, model fallback, token tracking, cost controls. With one, you solve it once at the infrastructure layer.

The four capabilities that matter at enterprise scale:

• Unified API surface — one endpoint regardless of which model is behind it
• Load balancing and fallback — route to backup providers when primary fails or rate-limits
• Observability — token usage, latency, cost, and errors in one place
• Access control — centralized API key management, team-level quotas, audit logs

Open-Source LLM Gateways Worth Evaluating in 2026

1. Helicone

Helicone is the one that shows up when teams need serious observability baked into the gateway layer, not wired in afterward through a separate logging pipeline. It’s built as a proxy you can drop in front of any OpenAI-compatible endpoint, and monitoring starts immediately, with no instrumentation changes in the application code.

What separates Helicone from most gateways is the depth of the request-level data it captures by default: token counts, cost per request, latency, user-level attribution, and custom properties you tag at call time. That makes it practical for teams that need to answer “which feature is driving our AI spend” rather than “how many tokens did we use this month.”

Key capabilities:

• One-line proxy setup changes the base URL; observability starts immediately.
• Request logging with cost tracking, latency, and custom metadata per request
• User-level and session-level attribution out of the box
• Prompt management and versioning with A/B testing support
• Caching layer to reduce duplicate calls and control spend
• Self-hostable or managed cloud deployment

Best for: Teams that prioritize observability and cost attribution above everything else. Particularly useful when multiple teams or product features share the same LLM budget, and you need to break down spend by owner.

Trade-offs: Helicone is observability-first, and fallback logic is less of a focus compared to LiteLLM or Kong. If your primary problem is multi-provider failover rather than usage visibility, you’ll likely combine Helicone with another routing layer or use a different tool.

2. LiteLLM

LiteLLM is the most widely adopted open-source LLM proxy in 2026, and for good reason: it supports more model providers than any other and has an enormous community contributing to it.

It exposes a single OpenAI-compatible endpoint to 100+ models. You point your app at LiteLLM, configure your providers in a YAML file, and the gateway handles translation, retries, and fallback.

Key capabilities:

• Broadest provider support: OpenAI, Anthropic, Azure, Bedrock, Cohere, Mistral, Ollama, and dozens more
• OpenAI-compatible API (minimal code changes to adopt)
• Budget controls per key, per team, per model
• Built-in spend tracking and cost dashboards
• Proxy server with Redis-backed caching

Best for: Teams that need maximum provider coverage and a battle-tested community project. If you’re standardizing across a heterogeneous model portfolio, LiteLLM has the widest support.

Trade-offs: The breadth comes with configuration complexity. At large scale, self-hosting LiteLLM requires careful tuning; it can become a performance bottleneck if not provisioned correctly. Observability is functional but not as deep as Helicone’s native offering.

3. Kong AI Gateway

Kong built its reputation on API gateways for microservices, and Kong AI Gateway extends that into LLM traffic. If your organization already runs Kong for API management, this is a natural addition; it plugs into the same control plane, uses the same plugin architecture, and fits into existing GitOps workflows.

Key capabilities:

• Semantic caching to reduce duplicate LLM calls (and cost)
• AI prompt engineering plugins (injecting system prompts at the gateway level)
• Rate limiting, authentication, and logging via existing Kong plugins
• Multi-cloud support with declarative configuration
• Enterprise tier available with dedicated support

Best for: Enterprises already on Kong for API management, or teams that need gateway-level prompt manipulation without application changes.

Trade-offs: If you’re not already in the Kong ecosystem, the setup overhead is real. It’s a heavier deployment compared to purpose-built LLM gateways. The AI-specific features are also more recent, so they’re not as mature as Kong’s core gateway capabilities.

4. Apache APISIX

APISIX is a high-performance API gateway that added LLM routing support in recent versions. It’s CNCF-hosted, runs on NGINX under the hood, and is built for teams that need raw throughput at scale.

The LLM plugin layer handles provider routing, load balancing, and basic observability. What APISIX brings is performance; it consistently outperforms other gateways in requests-per-second benchmarks at high concurrency, which matters when you’re processing thousands of simultaneous LLM calls.

Key capabilities:

• High-throughput architecture (Lua-based plugins, NGINX core)
• Dynamic routing without reloads
• Plugin ecosystem covering auth, rate limiting, observability, and transforms.
• Strong Kubernetes integration
• Active Apache Software Foundation governance

Best for: Teams where raw gateway performance is the primary constraint, high-volume inference pipelines, real-time applications where latency margins are tight.

Trade-offs: LLM-specific features (semantic caching, cost tracking, model fallback chains) are less mature than those of purpose-built LLM gateways. You’ll likely need to build or integrate additional tooling for production-grade LLM observability.

5. Envoy AI Gateway

Envoy Proxy is the infrastructure backbone for many service meshes (Istio, for one), and Envoy AI Gateway extends it with LLM-specific routing capabilities. The value proposition is clear if you’re already running Envoy: you add AI routing to an existing, hardened proxy layer rather than operating a separate gateway.

Key capabilities:

• Built on battle-tested Envoy infrastructure
• Token-based rate limiting (not just request-based, critical for LLM cost control)
• Header-based routing and model selection
• Strong observability via existing Envoy metrics pipeline
• Native integration with service mesh deployments

Best for: Teams running Istio or other Envoy-based service meshes who want to add LLM routing to existing infrastructure. Also strong for teams with strict compliance requirements that need request-level auditability.

Trade-offs: Envoy AI Gateway is earlier-stage than the others. Configuration is verbose (Envoy’s xDS API is powerful but not quick to get right). If you don’t already know Envoy, the learning curve is steep.

Side-by-Side Comparison

Feature	Helicone	LiteLLM	Kong AI Gateway	Apache APISIX	Envoy AI Gateway
Provider Support	OpenAI-compatible endpoints	100+ (broadest)	Major providers	Major providers	Major providers
API Compatibility	OpenAI-compatible	OpenAI-compatible	OpenAI-compatible	Plugin-based	xDS / HTTP
Fallback / Failover	Limited (observability focus)	✅	✅	✅	✅
Semantic Caching	✅	Partial	✅	Limited	❌
Native Observability	✅ Deep (core feature)	✅ Basic	✅ Via plugins	✅ Via plugins	✅ Via Envoy metrics
Token-Level Rate Limiting	✅	✅	✅	Request-level	✅
Cost Tracking	✅ Per request + per user	✅ Budget controls	Partial	Limited	❌ Native
Kubernetes Native	✅	✅	✅	✅	✅
Setup Complexity	Low	Low	Medium–High	Medium–High	High
Maturity	Mature	Mature	Mature (AI layer newer)	Mature (AI layer newer)	Early-stage
Best Fit	Observability + cost attribution	Max provider coverage	Kong-ecosystem teams	High-throughput pipelines	Envoy/service mesh teams

How to Pick One Without Overthinking It

The gateway decision usually comes down to three questions:

1. What’s already in your infrastructure?

If you run Kong, use Kong AI Gateway. If you run Envoy/Istio, evaluate Envoy AI Gateway. If you’re greenfield, pick based on capabilities.

2. How important is deep observability?

If you need cost attribution per team, per project, or per feature, and you don't want to wire up five separate tools, Helicone’s native approach wins. The proxy setup is a single base URL change; request-level tracing, cost tracking, and user attribution work immediately with no additional instrumentation.

3. How many providers do you need to support?

If your answer is “all of them” or close to it, LiteLLM has the broadest support and the most community-contributed integrations.

What “Enterprise Scale” Actually Breaks

A gateway that works in development often fails in production for one of three reasons:

• Stateless routing at high concurrency: Most gateways handle this fine, but verify your chosen gateway can handle thousands of simultaneous streaming connections without memory bloat. Envoy and APISIX have the strongest track record here.
• Fallback logic for partial provider failures: If a provider is degraded (slow responses rather than hard failures), naive retry logic can make things worse. Look for gateways that support circuit breakers and timeout-based fallback, not just error-code-based fallback.
• Cost attribution at the team level: Token usage without team-level attribution means your finance team can’t audit spend, and engineering teams have no incentive to optimize. This is a gap across several gateways: Helicone and LiteLLM handle it natively, while APISIX requires external tooling.

Conclusion

Running enterprise AI at scale isn’t a model problem; it’s an infrastructure problem. The model is the easy part. Routing, fallback, cost controls, observability, and access management are where teams consistently underestimate complexity.

The right gateway is the one that fits your existing stack and solves your actual bottleneck, whether that’s provider breadth, raw throughput, observability depth, or the need to integrate with an existing Kong or Envoy deployment. None of these projects is a wrong choice; they have different priorities.

What matters is picking one and centralizing your LLM traffic through it before that routing logic spreads across every service in your stack and becomes impossible to audit or change.