The 10 Best LLM Gateways in 2027

Curated by Kory White · Fractional CRO, CRO Syndicate

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📅 Published Jun 27, 2026 · Updated Jun 27, 2026 · 7 min read

The 10 Best LLM Gateways in 2027

An LLM gateway is a proxy that sits between your applications and the language models they call, centralizing routing, authentication, rate limiting, cost tracking, caching, logging, and safety into one governed layer. As organizations adopt multiple models from multiple providers, a gateway becomes the control plane that keeps spend visible, guardrails consistent, and provider switching painless.

This ranking covers the ten LLM gateways production teams rely on in 2027, from open-source proxies like LiteLLM to enterprise gateways built on mature API-management platforms.

Direct Answer

LiteLLM is the best overall LLM gateway for most teams because it is open source, exposes a unified OpenAI-compatible API across a hundred-plus model providers, and includes routing, fallback, caching, rate limiting, and spend tracking out of the box. It is also the best value given its free open-source core.

The right gateway depends on whether you want an open-source proxy, a managed observability-first gateway, or an enterprise gateway built into an existing API-management stack.

How We Ranked These

We evaluated each gateway on five criteria: unified API and provider coverage (one interface across many models), routing and resilience (load balancing, fallback, retries), cost and governance (token/spend tracking, quotas, per-team budgets), caching and performance (exact and semantic caching), and observability and safety (logging, tracing, guardrails).

Pricing for managed options varies and is described generically; confirm current rates and pilot on your traffic before committing.

1. LiteLLM 🏆 BEST OVERALL & 💎 BEST VALUE

LiteLLM is an open-source LLM gateway and Python SDK that provides a single OpenAI-compatible interface to a very large set of model providers — commercial APIs and self-hosted models alike. As a proxy server it adds load balancing and fallback across deployments, virtual keys and budgets per team, rate limiting, caching, and spend tracking, plus logging integrations to observability tools.

It is the default starting point for many teams precisely because it is free, broad, and production-capable.

Strengths: unified API across 100+ providers, routing/fallback, budgets and virtual keys, caching, open source. Best for: teams wanting a flexible, vendor-neutral gateway they can self-host. Pricing/availability: free and open source; an enterprise tier adds support and advanced features.

2. Portkey

Portkey is an AI gateway and control panel focused on reliability and observability, offering routing with fallbacks and retries, semantic caching, request logging and tracing, guardrails, and per-team budgets through a unified API. It is designed to be production-grade with strong analytics on cost, latency, and quality.

Strengths: reliability features, semantic caching, rich observability and analytics, guardrails. Best for: teams wanting a managed gateway with deep observability. Pricing/availability: managed SaaS with a free tier; open-source gateway core available.

3. Kong AI Gateway

Kong AI Gateway extends the mature Kong API gateway with AI-specific plugins: multi-provider routing, request/response transformation, semantic caching, prompt guarding, rate limiting, and observability — all on a battle-tested gateway platform. It suits enterprises that already run Kong for API management and want LLM traffic governed the same way.

Strengths: enterprise-grade platform, AI plugins on proven infrastructure, strong policy and security controls. Best for: enterprises standardized on Kong API management. Pricing/availability: open-source core; enterprise tiers add advanced features and support.

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4. Cloudflare AI Gateway

Cloudflare AI Gateway sits at the edge in front of your model providers, adding caching, rate limiting, request logging and analytics, and cost visibility with minimal setup — you change your endpoint and gain observability and control. Its edge presence makes it simple to adopt for teams already on Cloudflare.

Strengths: edge-based, trivial to adopt, caching and analytics, low operational overhead. Best for: teams wanting quick visibility and caching without running a proxy. Pricing/availability: managed service with usage-based tiers.

5. Apache APISIX (AI plugins)

Apache APISIX is an open-source API gateway with AI plugins for proxying and load-balancing LLM requests, prompt transformation, rate limiting, and observability. It brings LLM gateway capabilities to teams who want a fully open-source, high-performance gateway platform.

Strengths: open source, high performance, AI proxy and load-balancing plugins, extensible. Best for: teams wanting an open-source gateway platform with LLM support. Pricing/availability: open source; commercial support via API7.

6. Helicone

Helicone is an open-source LLM observability and gateway tool that proxies your model calls to capture cost, latency, usage, and traces, with caching, rate limiting, and request management. Its observability-first design makes it a quick way to gain visibility and basic gateway controls over LLM API usage.

Strengths: open source, fast to adopt, cost and usage analytics, caching and rate limiting. Best for: teams prioritizing visibility into LLM spend and usage. Pricing/availability: open source self-hosted; managed cloud with free and paid tiers.

7. OpenRouter

OpenRouter is a hosted unified API that routes requests across many model providers through one endpoint and one billing relationship, with automatic fallback and a marketplace of models. It is less a self-hosted gateway and more a managed routing layer that simplifies multi-provider access.

Strengths: one API and one bill across many providers, automatic fallback, broad model marketplace. Best for: teams wanting easy multi-provider access without operating a gateway. Pricing/availability: managed service; pay per token with a routing margin.

8. TrueFoundry / Gateway platforms

TrueFoundry and similar AI-platform vendors offer an enterprise LLM gateway with unified API access, routing, rate limiting, budgets, observability, and guardrails as part of a broader AI deployment platform. These suit organizations wanting the gateway integrated with model deployment and governance.

Strengths: enterprise gateway within a deployment platform, governance and budgets, observability. Best for: enterprises wanting gateway plus deployment in one platform. Pricing/availability: managed enterprise pricing.

9. Gloo AI Gateway (Solo.io)

Gloo AI Gateway brings LLM traffic management to Kubernetes-native, Envoy-based gateways, offering routing across providers, prompt guards, rate limiting, and observability with the policy controls of a service-mesh-grade platform. It fits cloud-native enterprises managing AI traffic alongside microservices.

Strengths: Kubernetes/Envoy-native, strong policy and security, provider routing and guards. Best for: cloud-native enterprises governing AI alongside microservices. Pricing/availability: open-source core; enterprise tiers available.

10. Cloud-native gateways (Bedrock / Vertex / Azure AI)

The major clouds offer gateway-like capabilities around their model services — unified access to multiple foundation models, logging, guardrails, and governance within the provider. For teams committed to one cloud, these reduce the need for a separate gateway while keeping traffic inside the provider's ecosystem.

Strengths: integrated with cloud foundation models, native governance and logging, no extra system. Best for: teams standardized on one cloud's model services. Pricing/availability: usage-based within the cloud provider.

How to Choose

flowchart TD A[Need an LLM gateway] --> B{Want open source / self-host?} B -- Yes --> C{Priority?} C -- Broad provider unification --> D[LiteLLM] C -- On existing API gateway --> E[Kong or Apache APISIX] C -- Observability-first --> F[Helicone] C -- Kubernetes / Envoy --> G[Gloo AI Gateway] B -- No, managed --> H{Priority?} H -- Reliability + analytics --> I[Portkey] H -- Edge + simple --> J[Cloudflare AI Gateway] H -- One API + one bill --> K[OpenRouter] H -- Cloud-native --> L[Bedrock / Vertex / Azure]

Why a gateway becomes essential

A single app calling one model does not need a gateway. The need appears when several teams each call providers directly: spend becomes invisible, guardrails are inconsistent, outages and rate limits surface as user errors, and switching providers means touching every codebase. A gateway centralizes these cross-cutting concerns into one controllable chokepoint — one place to meter cost, enforce safety, route and fall back across providers, and observe every request.

This is the same architectural logic that produced API gateways, now applied to model calls, and it is why most organizations adopt one soon after LLM features reach production.

Frequently Asked Questions

What is the difference between an LLM gateway and an AI gateway? The terms are used interchangeably. Both describe a control-plane proxy in front of language models that handles routing, auth, rate limiting, cost tracking, caching, observability, and guardrails. Some products emphasize observability, others routing or enterprise policy, but the core role is the same.

Why is LiteLLM so widely used? Because it is open source, exposes one OpenAI-compatible API across a hundred-plus providers, and bundles routing, fallback, caching, budgets, and spend tracking. Teams can start free and self-hosted, then layer on observability and enterprise features as needs grow.

Does a gateway add latency? A well-built gateway adds only small overhead, and its caching and routing often reduce net latency by serving repeated queries instantly and avoiding throttled providers. Measure the overhead on your traffic, but for most teams the control and savings outweigh it.

How does a gateway control LLM cost? It meters tokens per request, attributes spend to teams and projects, enforces budgets and quotas, and caches repeated or rephrased queries so you stop paying for duplicate answers — turning opaque LLM spend into a governed, observable line item.

Can a gateway route across both APIs and self-hosted models? Yes. Gateways like LiteLLM, Kong, and Portkey front commercial APIs and self-hosted models (served by vLLM or TGI) behind one interface, so you can mix providers and shift traffic by changing configuration rather than code.

Should I use a managed gateway or run my own? Open-source gateways (LiteLLM, Kong, APISIX, Helicone) give control and avoid per-request fees if you can operate them. Managed gateways (Portkey, Cloudflare, OpenRouter) remove operational work and add hosted analytics. Choose based on your ops capacity and whether you want analytics and support included.

Sources

LiteLLM project documentation (GitHub)
Portkey documentation
Kong AI Gateway documentation
Cloudflare AI Gateway documentation
Apache APISIX AI plugins documentation
Helicone and OpenRouter documentation
Solo.io Gloo AI Gateway documentation
Cloud provider model-service governance documentation

Keep reading

![The 10 Best LLM Gateways in 2027](https://www.devopsconsulting.in/blog/wp-content/uploads/2026/03/ChatGPT-Image-Mar-14-2026-03_47_02-PM.png)

# The 10 Best LLM Gateways in 2027

An LLM gateway is a proxy that sits between your applications and the language models they call, centralizing routing, authentication, rate limiting, cost tracking, caching, logging, and safety into one governed layer. As organizations adopt multiple models from multiple providers, a gateway becomes the control plane that keeps spend visible, guardrails consistent, and provider switching painless. This ranking covers the ten LLM gateways production teams rely on in 2027, from open-source proxies like LiteLLM to enterprise gateways built on mature API-management platforms.

### Direct Answer
**LiteLLM** is the best overall LLM gateway for most teams because it is open source, exposes a unified OpenAI-compatible API across a hundred-plus model providers, and includes routing, fallback, caching, rate limiting, and spend tracking out of the box. It is also the best value given its free open-source core. The right gateway depends on whether you want an open-source proxy, a managed observability-first gateway, or an enterprise gateway built into an existing API-management stack.

## How We Ranked These
We evaluated each gateway on five criteria: **unified API and provider coverage** (one interface across many models), **routing and resilience** (load balancing, fallback, retries), **cost and governance** (token/spend tracking, quotas, per-team budgets), **caching and performance** (exact and semantic caching), and **observability and safety** (logging, tracing, guardrails). Pricing for managed options varies and is described generically; confirm current rates and pilot on your traffic before committing.

## 1. LiteLLM 🏆 BEST OVERALL & 💎 BEST VALUE
**LiteLLM** is an open-source LLM gateway and Python SDK that provides a single **OpenAI-compatible** interface to a very large set of model providers — commercial APIs and self-hosted models alike. As a proxy server it adds **load balancing and fallback** across deployments, **virtual keys** and **budgets** per team, **rate limiting**, **caching**, and **spend tracking**, plus logging integrations to observability tools. It is the default starting point for many teams precisely because it is free, broad, and production-capable.

**Strengths:** unified API across 100+ providers, routing/fallback, budgets and virtual keys, caching, open source. **Best for:** teams wanting a flexible, vendor-neutral gateway they can self-host. **Pricing/availability:** free and open source; an enterprise tier adds support and advanced features.

## 2. Portkey
**Portkey** is an AI gateway and control panel focused on reliability and observability, offering routing with **fallbacks and retries**, **semantic caching**, request logging and tracing, guardrails, and per-team budgets through a unified API. It is designed to be production-grade with strong analytics on cost, latency, and quality.

**Strengths:** reliability features, semantic caching, rich observability and analytics, guardrails. **Best for:** teams wanting a managed gateway with deep observability. **Pricing/availability:** managed SaaS with a free tier; open-source gateway core available.

## 3. Kong AI Gateway
**Kong AI Gateway** extends the mature **Kong** API gateway with AI-specific plugins: multi-provider routing, request/response transformation, semantic caching, prompt guarding, rate limiting, and observability — all on a battle-tested gateway platform. It suits enterprises that already run Kong for API management and want LLM traffic governed the same way.

**Strengths:** enterprise-grade platform, AI plugins on proven infrastructure, strong policy and security controls. **Best for:** enterprises standardized on Kong API management. **Pricing/availability:** open-source core; enterprise tiers add advanced features and support.


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## 4. Cloudflare AI Gateway
**Cloudflare AI Gateway** sits at the edge in front of your model providers, adding **caching**, **rate limiting**, **request logging and analytics**, and cost visibility with minimal setup — you change your endpoint and gain observability and control. Its edge presence makes it simple to adopt for teams already on Cloudflare.

**Strengths:** edge-based, trivial to adopt, caching and analytics, low operational overhead. **Best for:** teams wanting quick visibility and caching without running a proxy. **Pricing/availability:** managed service with usage-based tiers.

## 5. Apache APISIX (AI plugins)
**Apache APISIX** is an open-source API gateway with **AI plugins** for proxying and load-balancing LLM requests, prompt transformation, rate limiting, and observability. It brings LLM gateway capabilities to teams who want a fully open-source, high-performance gateway platform.

**Strengths:** open source, high performance, AI proxy and load-balancing plugins, extensible. **Best for:** teams wanting an open-source gateway platform with LLM support. **Pricing/availability:** open source; commercial support via API7.

## 6. Helicone
**Helicone** is an open-source LLM observability and gateway tool that proxies your model calls to capture **cost, latency, usage, and traces**, with caching, rate limiting, and request management. Its observability-first design makes it a quick way to gain visibility and basic gateway controls over LLM API usage.

**Strengths:** open source, fast to adopt, cost and usage analytics, caching and rate limiting. **Best for:** teams prioritizing visibility into LLM spend and usage. **Pricing/availability:** open source self-hosted; managed cloud with free and paid tiers.

## 7. OpenRouter
**OpenRouter** is a hosted unified API that routes requests across many model providers through one endpoint and one billing relationship, with automatic fallback and a marketplace of models. It is less a self-hosted gateway and more a managed routing layer that simplifies multi-provider access.

**Strengths:** one API and one bill across many providers, automatic fallback, broad model marketplace. **Best for:** teams wanting easy multi-provider access without operating a gateway. **Pricing/availability:** managed service; pay per token with a routing margin.

## 8. TrueFoundry / Gateway platforms
**TrueFoundry** and similar AI-platform vendors offer an enterprise LLM gateway with unified API access, routing, rate limiting, budgets, observability, and guardrails as part of a broader AI deployment platform. These suit organizations wanting the gateway integrated with model deployment and governance.

**Strengths:** enterprise gateway within a deployment platform, governance and budgets, observability. **Best for:** enterprises wanting gateway plus deployment in one platform. **Pricing/availability:** managed enterprise pricing.

## 9. Gloo AI Gateway (Solo.io)
**Gloo AI Gateway** brings LLM traffic management to Kubernetes-native, Envoy-based gateways, offering routing across providers, prompt guards, rate limiting, and observability with the policy controls of a service-mesh-grade platform. It fits cloud-native enterprises managing AI traffic alongside microservices.

**Strengths:** Kubernetes/Envoy-native, strong policy and security, provider routing and guards. **Best for:** cloud-native enterprises governing AI alongside microservices. **Pricing/availability:** open-source core; enterprise tiers available.

## 10. Cloud-native gateways (Bedrock / Vertex / Azure AI)
The major clouds offer **gateway-like** capabilities around their model services — unified access to multiple foundation models, logging, guardrails, and governance within the provider. For teams committed to one cloud, these reduce the need for a separate gateway while keeping traffic inside the provider's ecosystem.

**Strengths:** integrated with cloud foundation models, native governance and logging, no extra system. **Best for:** teams standardized on one cloud's model services. **Pricing/availability:** usage-based within the cloud provider.

## How to Choose

```mermaid
flowchart TD
    A[Need an LLM gateway] --> B{Want open source / self-host?}
    B -- Yes --> C{Priority?}
    C -- Broad provider unification --> D[LiteLLM]
    C -- On existing API gateway --> E[Kong or Apache APISIX]
    C -- Observability-first --> F[Helicone]
    C -- Kubernetes / Envoy --> G[Gloo AI Gateway]
    B -- No, managed --> H{Priority?}
    H -- Reliability + analytics --> I[Portkey]
    H -- Edge + simple --> J[Cloudflare AI Gateway]
    H -- One API + one bill --> K[OpenRouter]
    H -- Cloud-native --> L[Bedrock / Vertex / Azure]
```

## Why a gateway becomes essential

A single app calling one model does not need a gateway. The need appears when several teams each call providers directly: spend becomes invisible, guardrails are inconsistent, outages and rate limits surface as user errors, and switching providers means touching every codebase. A gateway centralizes these cross-cutting concerns into one controllable chokepoint — one place to meter cost, enforce safety, route and fall back across providers, and observe every request. This is the same architectural logic that produced API gateways, now applied to model calls, and it is why most organizations adopt one soon after LLM features reach production.

## Frequently Asked Questions

**What is the difference between an LLM gateway and an AI gateway?**
The terms are used interchangeably. Both describe a control-plane proxy in front of language models that handles routing, auth, rate limiting, cost tracking, caching, observability, and guardrails. Some products emphasize observability, others routing or enterprise policy, but the core role is the same.

**Why is LiteLLM so widely used?**
Because it is open source, exposes one OpenAI-compatible API across a hundred-plus providers, and bundles routing, fallback, caching, budgets, and spend tracking. Teams can start free and self-hosted, then layer on observability and enterprise features as needs grow.

**Does a gateway add latency?**
A well-built gateway adds only small overhead, and its caching and routing often reduce net latency by serving repeated queries instantly and avoiding throttled providers. Measure the overhead on your traffic, but for most teams the control and savings outweigh it.

**How does a gateway control LLM cost?**
It meters tokens per request, attributes spend to teams and projects, enforces budgets and quotas, and caches repeated or rephrased queries so you stop paying for duplicate answers — turning opaque LLM spend into a governed, observable line item.

**Can a gateway route across both APIs and self-hosted models?**
Yes. Gateways like LiteLLM, Kong, and Portkey front commercial APIs and self-hosted models (served by vLLM or TGI) behind one interface, so you can mix providers and shift traffic by changing configuration rather than code.

**Should I use a managed gateway or run my own?**
Open-source gateways (LiteLLM, Kong, APISIX, Helicone) give control and avoid per-request fees if you can operate them. Managed gateways (Portkey, Cloudflare, OpenRouter) remove operational work and add hosted analytics. Choose based on your ops capacity and whether you want analytics and support included.

## Sources
- LiteLLM project documentation (GitHub)
- Portkey documentation
- Kong AI Gateway documentation
- Cloudflare AI Gateway documentation
- Apache APISIX AI plugins documentation
- Helicone and OpenRouter documentation
- Solo.io Gloo AI Gateway documentation
- Cloud provider model-service governance documentation

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The 10 Best LLM Gateways in 2027

The 10 Best LLM Gateways in 2027

Direct Answer

How We Ranked These

1. LiteLLM 🏆 BEST OVERALL & 💎 BEST VALUE

2. Portkey

3. Kong AI Gateway

4. Cloudflare AI Gateway

5. Apache APISIX (AI plugins)

6. Helicone

7. OpenRouter

8. TrueFoundry / Gateway platforms

9. Gloo AI Gateway (Solo.io)

10. Cloud-native gateways (Bedrock / Vertex / Azure AI)

How to Choose

Why a gateway becomes essential

Frequently Asked Questions

Sources

What does the score mean?