5 Best LLM Gateways

As AI applications move from prototypes into production, managing model access becomes a different kind of engineering problem.

At first, connecting an application to OpenAI or Anthropic is relatively straightforward. But as a project grows, teams may need to support multiple providers, control token spending, handle rate limits, recover from provider outages, and monitor how models are being used across different applications.

Building all of that independently can create unnecessary complexity.

This is where an LLM gateway becomes useful. It provides a central layer between applications and model providers, helping teams manage AI traffic without implementing the same infrastructure separately in every project.

I've been looking at how different gateways approach this problem, and one thing stands out: not every gateway is designed for the same environment. Some focus on model access and routing, while others emphasize enterprise governance, API management, or operational visibility.

In this article, I'll compare five LLM gateways worth considering in 2026: Bifrost, LiteLLM, Kong AI Gateway, Cloudflare AI Gateway, and OpenRouter.

TL;DR

• Bifrost: A strong option for enterprises that need low-overhead model routing, centralized governance, and integrated MCP capabilities. It also offers virtual keys and other controls for managing AI usage.
• LiteLLM: A flexible, open-source option for accessing multiple LLM providers through a common interface, with proxy and cost-management features.
• Kong AI Gateway: Best suited to organizations that want to extend existing API infrastructure and governance practices to AI workloads.
• Cloudflare AI Gateway: A practical choice for teams looking for AI traffic analytics, caching, rate limiting, and retries within the Cloudflare ecosystem.
• OpenRouter: Useful for developers who want a straightforward way to access models from different providers through a unified API.

The right choice depends on whether you prioritize enterprise controls, model flexibility, existing infrastructure, or a managed way to access a broad model catalog.

What Is an LLM Gateway?

An LLM gateway is an intermediary layer between an application and one or more large language model providers. Instead of having every application manage provider-specific endpoints, credentials, routing logic, and usage tracking independently, teams can centralize these responsibilities.

For example, an application might send requests to a gateway rather than directly to a model provider. The gateway then routes the request to the configured model, applies relevant policies, and returns the response.

Depending on the platform, this layer may also handle:

• Model routing and fallback: Direct requests to suitable providers and recover from certain failures.
• Cost management: Track usage, set budgets, and attribute spending to teams or applications.
• Authentication and governance: Manage credentials, access permissions, and rate limits.
• Observability: Monitor requests, token consumption, latency, and errors.
• Caching: Reuse eligible responses to reduce repeated model calls and costs.
• MCP and agent integrations: Manage access to tools and services used by AI agents, where supported.

These capabilities can simplify production operations, but the depth of implementation varies considerably between gateways.

How to Evaluate an LLM Gateway

Before comparing individual products, I would look at five practical considerations.

Evaluation criterion	What to look for	Why it matters
Provider support	Compatibility with the models and APIs you use	Reduces integration work and provider lock-in
Routing and reliability	Fallbacks, load balancing, and provider selection	Helps applications handle failures and changing workloads
Governance	Authentication, budgets, permissions, and rate limits	Makes AI usage easier to control across teams
Observability	Request logs, latency, errors, and cost reporting	Helps teams troubleshoot and optimize production systems
Deployment	Managed, self-hosted, or hybrid options	Determines how well the gateway fits security and infrastructure requirements

Performance also deserves attention, particularly when an application makes multiple sequential model calls. Published latency numbers are only useful when the benchmark methodology, hardware, workload, and request volume are understood.

1. Bifrost: Enterprise AI Routing and Governance

Bifrost is an AI gateway developed by Maxim AI that focuses on routing, governing, and observing AI traffic across multiple providers. It offers an OpenAI-compatible interface, allowing teams to integrate supported models without building a separate connection for each provider.

Its architecture is particularly relevant for teams that need an operational control layer rather than just a convenient way to switch models.

What stands out

Bifrost combines several capabilities in one platform:

• Multi-provider routing: Access supported providers through a common gateway and configure routing or fallback behavior.
• Performance focus: Bifrost is written in Go, and its maintainers publish benchmarks reporting low gateway overhead under specified test conditions. Teams can review the Bifrost benchmarking documentation to understand the testing approach.
• Enterprise governance: Virtual keys, access profiles, budgets, and rate limits help teams manage usage across consumers and applications. Its budget and limits controls provide additional options for managing spending and request volume.
• MCP integration: Its MCP gateway capabilities extend management beyond model requests to interactions with external tools.
• Operational visibility: Request logs, analytics, and administrative controls help teams understand how AI infrastructure is being used.

The combination is useful when a company wants model routing and governance in the same infrastructure layer. For organizations running AI agents, the ability to manage model requests and MCP tool access can also reduce the need to operate disconnected control systems.

Bifrost is open source, and its GitHub repository provides implementation details for teams evaluating the architecture or considering self-hosted deployment.

Deployment requirements are another consideration. Organizations with specific network restrictions can investigate in-VPC deployment or air-gapped deployment, depending on their infrastructure and security needs.

Best for

Bifrost is worth considering for enterprises that need centralized control over model access, spending, and AI tool interactions. It is especially relevant when performance, deployment control, and MCP governance are important alongside multi-provider routing.

Teams should still validate published performance claims against their own workloads and evaluate which enterprise capabilities they require.

2. LiteLLM: A Flexible Open-Source LLM Proxy

LiteLLM is an open-source project that provides a consistent interface for working with a wide range of LLM providers. It offers a Python SDK and a proxy server that teams can deploy as their own gateway.

This makes it useful for engineering teams that want to standardize model access without committing every application to a single provider. Developers can integrate through a common interface while managing provider connections and gateway configuration centrally.

What stands out

LiteLLM offers several features for teams building their own AI infrastructure:

• Unified provider interface: Use a consistent API across supported model providers.
• SDK and proxy options: Choose between integrating the Python SDK or running a gateway service.
• Authentication and virtual keys: Centralize credentials and control access to configured deployments.
• Usage and cost tracking: Monitor model usage and associate costs with different consumers.
• Routing and fallbacks: Configure how requests are distributed across supported deployments and how failures are handled.

One of LiteLLM's main advantages is flexibility. Teams can inspect the implementation, customize their configuration, and deploy the proxy in infrastructure they control.

That flexibility also means the team must plan for maintenance, monitoring, scaling, and security. The amount of operational work depends on the architecture and deployment.

Explore the LiteLLM documentation for its SDK, proxy configuration, and supported integrations.

Best for

LiteLLM is best suited for developers and engineering teams that want an open-source, customizable gateway with broad provider compatibility. It is particularly useful for teams that prefer to manage their own infrastructure and want centralized authentication, routing, and cost visibility.

3. Kong AI Gateway: Extending API Management to AI

Kong AI Gateway extends the broader Kong Gateway platform with AI-specific routing, security, and observability capabilities.

For organizations that already use Kong to manage APIs, this can be a natural way to bring AI traffic into an existing governance model instead of introducing an entirely separate infrastructure layer.

What stands out

Kong's approach builds on established API management concepts:

• Provider abstraction: Route requests to supported LLM providers through a common layer.
• Centralized credentials: Manage provider access rather than distributing credentials across applications.
• Routing and load balancing: Configure how AI requests are sent to upstream services.
• Security policies: Apply authentication and access controls through the gateway.
• AI observability: Track request activity and usage through the platform's monitoring capabilities.
• MCP support: Its AI Gateway documentation includes MCP server management and related controls.

A major advantage is that AI traffic can be managed alongside existing API infrastructure. Teams may already have deployment processes, security policies, and operational expertise around Kong, reducing the need to establish a separate platform.

However, organizations that do not already use Kong should assess the learning curve and infrastructure overhead. They should also check which AI-specific capabilities are available in their chosen edition.

Best for

Kong AI Gateway is particularly relevant to enterprises that already rely on Kong and want to extend their API management practices to LLMs, MCP servers, and AI agents. It is less compelling to adopt solely for AI routing if the organization has no existing need for the broader Kong platform.

4. Cloudflare AI Gateway: AI Traffic Management at the Edge

Cloudflare AI Gateway provides a managed layer for observing and controlling AI application traffic. It fits naturally into environments where Cloudflare is already part of the application's networking or security infrastructure.

Rather than requiring teams to build their own monitoring and traffic-management components, it offers a set of features designed to make AI requests easier to observe and control.

What stands out

Cloudflare AI Gateway includes capabilities such as:

• Analytics and logging: Inspect request activity, token usage, errors, and costs.
• Caching: Serve eligible repeated requests from cache.
• Rate limiting: Control request volume within configured limits.
• Retries and fallbacks: Improve resilience when requests fail, where configured and supported.
• Provider connectivity: Work with supported AI providers through the gateway.

Its integration with the wider Cloudflare ecosystem is a practical benefit for teams already using its infrastructure. The managed model also reduces the burden of operating a separate gateway service.

There are trade-offs. Teams must evaluate the service's deployment model, data handling, logging retention, and governance capabilities against their requirements. Organizations that need to operate the gateway entirely within their own infrastructure should pay particular attention to deployment constraints.

Best for

Cloudflare AI Gateway is a sensible option for teams already using Cloudflare that want visibility and basic traffic controls without managing another self-hosted service. It is particularly attractive when analytics, caching, rate limiting, and integration with existing infrastructure are the main priorities.

5. OpenRouter: Unified Access to a Broad Model Catalog

OpenRouter focuses on making a broad catalog of AI models accessible through a unified API. Instead of integrating separately with each supported provider, developers can use one interface to experiment with and access different models.

This approach is useful when teams are comparing models, building prototypes, or developing applications that may need to switch between different model families over time.

What stands out

OpenRouter emphasizes convenient access to a broad model ecosystem:

• Broad model catalog: Discover and access models from different providers through one interface.
• Unified API: Reduce the need to build separate integrations for each supported model.
• Model selection: Choose models according to the capabilities and requirements of an application.
• Provider routing: Use supported routing options to select among available provider routes.
• Fallback capabilities: Configure supported alternatives for handling certain provider availability issues.

The main attraction is the ability to experiment with different models without maintaining a separate integration for every provider. This can simplify early development and make model comparisons easier.

OpenRouter is primarily a managed model-access service, so teams with strict requirements around private deployment, data handling, or internal infrastructure control should review whether its operating model fits their needs.

Explore the OpenRouter model catalog to see the available models and provider options.

Best for

OpenRouter is best suited for developers who want convenient access to a broad selection of models through one API. It is particularly useful for model experimentation, comparing different options, and building applications where flexibility in model selection is a priority.

How the Five LLM Gateways Compare

The table below summarizes their primary positioning. Feature availability can vary by edition, deployment, and configuration, so use it as a starting point rather than a substitute for checking current documentation.

Gateway	Primary strength	Routing	Governance and cost controls	Deployment approach
Bifrost	Enterprise AI gateway with MCP capabilities	Multi-provider routing and fallback	Virtual keys, budgets, access controls	Open-source and self-hosted options; enterprise deployment options
LiteLLM	Flexible multi-provider proxy	Provider and model routing	Usage tracking, budgets, rate limits	Open-source, self-hosted proxy
Kong AI Gateway	API management extended to AI	AI proxy and configurable routing	API policies and AI governance features	Self-hosted and managed options
Cloudflare AI Gateway	Managed AI traffic management	Provider connectivity and configured fallbacks	Analytics, rate limiting, usage visibility	Managed Cloudflare service
OpenRouter	Broad model access	Provider selection and fallback options	Centralized access and billing; assess enterprise requirements	Managed service

Comparing Them by Use Case

Sometimes it is easier to choose a gateway by starting with the problem you need to solve.

Your main requirement	Gateway to evaluate	Why
Monitoring and governing AI requests	Bifrost	Combines model routing with governance, usage controls, observability, and MCP gateway capabilities
Open-source gateway with customizable deployment	Bifrost or LiteLLM	Both provide open-source options; compare their routing, governance, ecosystem, and deployment requirements
AI traffic managed through existing API infrastructure	Kong AI Gateway	Extends Kong's established API management approach to AI
Managed analytics and traffic controls in a Cloudflare environment	Cloudflare AI Gateway	Provides caching, rate limiting, logs, and request resilience features
Easy access to many models for experimentation	OpenRouter	Centralizes model discovery and access across providers

The important distinction is that these products overlap without being interchangeable. A gateway that makes model experimentation easy may not provide the deployment control or governance depth an enterprise needs. Likewise, an organization already operating a mature API platform may value integration with its existing infrastructure more than a separate model-routing interface.

What Makes an LLM Gateway Production-Ready?

A gateway can simplify development, but production readiness depends on how well it handles the realities of running AI workloads.

Reliability: Provider failures and rate limits are inevitable. Understand how the gateway handles retries and fallback behavior, and whether it can route requests to suitable alternatives without creating unexpected costs or changing application behavior.

Performance: Measure the gateway's overhead under realistic traffic. Consider not only average latency but also tail latency, throughput, streaming behavior, and the impact of multiple sequential calls in agentic workflows.

Governance: Teams need to know who is using models, how much they are spending, and which policies apply. Look for controls that match your organizational structure, including per-user or per-team limits where required.

Observability: Request-level visibility helps developers investigate failures and understand usage patterns. Check which metrics, logs, and integrations are available, and how sensitive prompt or response data is handled.

Deployment and security: Consider where requests are processed, how credentials are stored, what data is retained, and whether the deployment model satisfies internal security and compliance requirements.

A feature checklist is useful, but a small proof of concept is often more revealing. Test the gateway using the models, request patterns, traffic levels, and policies your application will actually use.

Frequently Asked Questions

What is the difference between an LLM gateway and an API gateway?

A traditional API gateway manages API traffic through functions such as authentication, routing, and rate limiting. An LLM gateway adds model-oriented capabilities such as provider selection, token usage tracking, model-specific fallback behavior, and AI cost management.

Do I need an LLM gateway if I use only one model provider?

Not necessarily. A direct integration may be sufficient for a small application, but a gateway can still help centralize logging, budgets, authentication, and rate limits. The decision depends on whether those operational benefits justify another layer in your architecture.

Which LLM gateways support open-source or self-hosted deployment?

Bifrost and LiteLLM provide open-source options, while Kong offers open-source gateway components alongside additional commercial capabilities. Cloudflare AI Gateway and OpenRouter are primarily managed services, so verify each platform's current deployment options and licensing before deciding.

Can an LLM gateway reduce AI costs?

It can help by providing usage visibility, enforcing budgets, routing requests to suitable models, and caching eligible responses. Actual savings depend on the workload, provider pricing, cache hit rates, and how routing policies are configured.

Can an LLM gateway manage MCP traffic as well as LLM requests?

Some platforms support both, but their capabilities differ. Bifrost and Kong document MCP gateway functionality, while teams evaluating LiteLLM or other gateways should check the current scope of their MCP support and whether it provides the controls they need.

How should I choose the best LLM gateway for my team?

Start by identifying your biggest requirement: model flexibility, production reliability, governance, deployment control, or integration with existing infrastructure. Then compare the relevant capabilities, validate any important performance claims, and test your leading options against a representative workload.

Final Thoughts

Choosing an LLM gateway is less about finding a universally superior product and more about finding the right operational fit.

LiteLLM offers a flexible route for teams that want an open-source proxy, Kong makes sense when AI traffic needs to fit into an established API management environment, Cloudflare AI Gateway simplifies traffic management for Cloudflare users, and OpenRouter makes access to a broad model catalog straightforward.

Bifrost is particularly worth evaluating when the requirement extends beyond model access to enterprise governance, performance, and MCP tool management in a unified gateway.

Before committing, test the options against your actual workload and confirm that their security, deployment, and governance features meet your needs. If you'd like to see how Bifrost could fit into your AI infrastructure, book a demo with the Bifrost team.