If your applications are already using multiple AI providers, the next problem is usually not model access; it is controlling that access at production scale. Different teams end up managing different API keys, provider-specific SDKs, rate limits, retries, model fallbacks, and usage reports. An open-source LLM gateway can add a single control layer between your applications and those models, providing engineering teams with a consistent API while centralizing routing, security, reliability, and observability.
In 2026, Bifrost, LiteLLM, Kong AI Gateway, Apache APISIX, and Envoy AI Gateway are five options worth evaluating, but they are built around different infrastructure philosophies. The best choice depends less on the number of supported models and more on your existing stack, traffic requirements, governance needs, and how much AI-specific functionality you actually need.
What Is an LLM Gateway?
An LLM gateway is a self-hosted layer that sits between an application and one or more AI model providers.
The basic architecture is:
Application → LLM Gateway → AI Provider or Self-Hosted Model
Instead of building provider-specific logic into every application, the gateway can handle common infrastructure concerns such as:
Model and provider routing, authentication, rate limiting, load balancing, retries and fallbacks, token and usage tracking, logging and observability, provider abstraction, security policies, traffic management.
This becomes valuable when an enterprise moves beyond a single model.
For example, a company might use one model for customer support, another for coding assistants, a self-hosted model for sensitive workloads, and a lower-cost provider for high-volume classification.
Without a gateway, each application has to understand those providers separately.
With a gateway, applications can communicate with a standardized internal endpoint while infrastructure teams control what happens behind it.
LLM Gateways Compared
| LLM Gateway | Best For | What Stands Out | Watch For |
|---|---|---|---|
| Bifrost | High-throughput, mission-critical AI workloads | Go-built for microsecond-level overhead with routing, failover, and cluster-level reliability at scale | Evaluate maturity against your operational requirements |
| LiteLLM | Multi-provider LLM access | Strong provider abstraction and unified API | Less focused on traditional API-gateway infrastructure |
| Kong AI Gateway | Enterprise API + AI governance | AI traffic management, policies, and observability | Some capabilities depend on Kong's broader platform |
| Apache APISIX | Cloud-native API + AI traffic | Extensible plugins and unified API/AI gateway model | Best fit when your team is comfortable operating APISIX |
| Envoy AI Gateway | Kubernetes and Envoy environments | Cloud-native networking and AI traffic management | More infrastructure-oriented than a simple LLM proxy |
The key takeaway is simple: Don't choose an LLM gateway based only on model-provider support. Look at where it fits in your existing infrastructure.
Bifrost
Bifrost takes a performance-first approach rather than treating LLM traffic as just another API workload, which is what makes it the fastest enterprise AI gateway among the options here.
It provides a unified OpenAI-compatible interface across multiple providers and includes capabilities such as load balancing, automatic failover, semantic caching, guardrails, and provider management. Its current project documentation also highlights high-throughput performance and low gateway overhead, and its published benchmarks show it processing requests with microsecond-level added latency even at high concurrency.
That makes Bifrost particularly interesting for applications where gateway latency and request volume matter.
For example, consider an enterprise AI platform serving several internal applications. The platform may need to distribute requests across multiple API keys, providers, or self-hosted endpoints while keeping the application-facing interface consistent.
Bifrost supports connections to providers including OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, Mistral, Ollama, Groq, and others. It can also connect to OpenAI-compatible self-hosted systems, such as vLLM, via a custom base URL.
Where Bifrost makes the most sense
Choose Bifrost when your main concern is AI traffic performance, provider routing, reliability, and centralized model access.
Its current repository reports a 5,000-RPS benchmark with low added gateway latency. Still, those figures are project-reported benchmarks, so enterprises should validate performance using their own payload sizes, streaming patterns, network topology, and provider mix before making an architecture decision.
You can review the GitHub repository to inspect the implementation, deployment options, and current project activity.
Best for:
- Enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability.
- Acts as a centralized AI gateway to route, govern, and secure all AI traffic across models and environments with ultra-low latency.
- Unifies LLM gateway, MCP gateway, and Agents gateway capabilities into a single platform.
- Built for regulated industries and strict enterprise requirements, supporting air-gapped deployments, VPC isolation, and on-prem infrastructure.
- Provides full control over data, access, and execution, along with robust security, policy enforcement, and governance capabilities.
LiteLLM
LiteLLM is a strong option when the central problem is simple:
"How do we give developers one interface for many different LLM providers?"
Instead of forcing applications to integrate separately with each provider, LiteLLM provides a common gateway layer for model access.
This approach is especially useful in organizations where model experimentation happens frequently.
One team might use OpenAI, another Anthropic, another AWS Bedrock, while an infrastructure team tests self-hosted models. A standardized gateway reduces the amount of provider-specific code that applications need to maintain.
Why LiteLLM is practical
The biggest advantage is provider abstraction.
That can make model migration easier. If a team wants to replace one provider with another, much of the provider-specific work can remain within the gateway rather than being spread across multiple applications.
LiteLLM is therefore a natural fit for organizations building an internal AI platform or model-access layer.
The trade-off is that teams should distinguish between an LLM proxy/gateway and a complete enterprise API-management platform.
If your organization also needs broad API lifecycle management, existing API policies, ingress capabilities, or a mature API gateway ecosystem, a platform such as Kong or APISIX may be a better fit.
Best for: Teams that need broad LLM provider coverage and a standardized model-access layer.
Kong AI Gateway
Kong approaches AI gateway infrastructure with an established background in API management.
Its AI Gateway provides routing and load balancing across AI providers, as well as authentication, streaming, usage analytics, token metrics, and observability. Its current platform also extends beyond LLM traffic into MCP and A2A workloads.
This matters for enterprises because AI traffic rarely exists in isolation.
A production AI application may involve:
User → Application → Agent → MCP tools → LLM → Internal APIs
At that point, controlling only the model API is not enough. Identity, authorization, observability, traffic policies, and governance become part of the architecture.
When Kong is the better choice
If your organization already operates Kong, introducing a separate AI gateway may create another infrastructure layer to maintain.
Kong's current AI Gateway is designed to provide centralized control over LLM, MCP, and A2A traffic, including policies and observability.
The important caveat is licensing and deployment. Not every Kong capability should be assumed to be part of the same open-source feature set, so enterprises should verify the current product and licensing model before treating Kong as a purely open-source alternative.
Best for: Enterprises that need AI governance alongside their existing API management strategy.
Apache APISIX
Apache APISIX is particularly interesting when AI traffic needs to coexist with conventional API traffic.
APISIX provides AI gateway functionality for model proxying, multi-provider routing, load balancing, retries, fallback, token-based rate limiting, security, and observability.
That means a platform team does not necessarily need one gateway for APIs and another for AI.
Why APISIX stands out
The plugin architecture is the major reason to consider it.
APISIX can apply gateway-level controls while supporting both traditional application traffic and AI workloads. Its AI gateway capabilities include provider routing, token controls, prompt-related policies, moderation, RAG-related functionality, and token observability.
This is useful for organizations already operating APISIX across Kubernetes or microservice infrastructure.
For those teams, the question becomes:
"Can we extend the gateway we already operate to AI traffic?"
If the answer is yes, that can be operationally simpler than introducing another gateway technology.
Best for: Cloud-native organizations seeking a single extensible gateway for APIs and AI workloads.
Envoy AI Gateway
Envoy AI Gateway is aimed at teams building AI infrastructure around Envoy Gateway and Kubernetes.
Its architecture is more infrastructure-oriented than a simple model proxy. That makes it relevant when AI traffic is already part of a broader cloud-native networking strategy.
This is an important distinction.
If your platform team already manages Kubernetes, Gateway API, and Envoy-based infrastructure, adding an AI gateway within that ecosystem can reduce the number of new concepts engineers have to learn.
When Envoy AI Gateway makes sense
Consider it when you need AI traffic management while keeping the surrounding architecture aligned with cloud-native networking practices.
It is less compelling if your only requirement is:
"Give me one endpoint that converts different LLM APIs into a common format."
For that narrower problem, an LLM-focused gateway such as LiteLLM may be simpler to evaluate.
Best for: Kubernetes-heavy platform teams already invested in Envoy-based networking.
LLM Gateway vs API Gateway: What's the Difference?
This distinction is easy to miss.
A traditional API gateway primarily manages API traffic: authentication, routing, rate limiting, access control, load balancing, and observability.
An LLM gateway adds AI-specific concerns such as:
Model-provider abstraction, token-aware rate limits, model routing, LLM-specific fallbacks, AI usage and cost tracking, prompt policies, model-specific request handling.
The boundaries are increasingly overlapping.
Kong and Apache APISIX demonstrate this clearly by extending established API gateway capabilities into AI traffic.
So before deploying a separate LLM gateway, ask:
Do we actually need another gateway, or can our existing API infrastructure handle the AI requirements?
That question can save significant operational overhead.
Which LLM Gateway Is Best for Enterprise AI?
There is no universal winner.
The practical choice looks like this:
- Choose Bifrost if performance-focused AI traffic, provider routing, failover, and AI-specific gateway functionality are your priorities.
- Choose LiteLLM if your main requirement is a unified interface across many LLM providers.
- Choose Kong AI Gateway if AI needs to fit into a broader enterprise API governance and connectivity strategy.
- Choose Apache APISIX if you want an extensible gateway that can handle both conventional APIs and AI workloads.
- Choose Envoy AI Gateway if your platform is heavily based on Kubernetes, Gateway API, and Envoy.
What Should You Test Before Putting an LLM Gateway in Production?
Feature checklists are useful, but production testing matters more.
I would evaluate these areas before committing:
1. Failure handling
Test what happens when a provider returns 429, times out, or becomes unavailable.
A gateway should not simply retry blindly. Excessive retries can increase latency and amplify provider failures.
2. Authentication and authorization
Confirm that applications cannot bypass the gateway and call provider endpoints directly.
Centralizing credentials only helps if the gateway is actually the enforced control point.
3. Token and cost visibility
Request counts alone are not enough for LLM infrastructure.
Track tokens, models, users or teams, latency, errors, and provider usage to understand where AI spending is going.
4. Streaming performance
Test streaming separately from ordinary request/response traffic.
A gateway that performs well for short JSON responses may behave differently when handling long streaming generations.
5. Provider failover
Don't just test whether a failover exists. Test whether it works correctly when the primary provider is rate-limited, unavailable, or returns an error.
6. Operational complexity
Ask who will own upgrades, configuration, monitoring, security patches, incident response, and backups.
A gateway becomes part of your critical AI infrastructure once every application depends on it.
7. Existing infrastructure compatibility
This may be the most important test.
If you already successfully operate Kong, APISIX, or Envoy, extending those platforms may be easier than introducing a completely separate LLM gateway.
My Practical Take
For a new AI platform, start the evaluation with the architecture rather than the feature list.
If the requirement is mainly multi-provider model access, LiteLLM and Bifrost deserve close attention.
If the organization already has a mature API gateway, first investigate whether Kong or APISIX can cover the AI requirements without adding another operational layer.
If the infrastructure is deeply Kubernetes- and Envoy-oriented, Envoy AI Gateway becomes a more natural candidate.
And if you're running high-volume, mission-critical AI workloads where gateway overhead, governance, and reliability all matter at once, Bifrost is the one I'd put at the top of your benchmarking list. It's the only gateway on this list built from the ground up as a unified LLM, MCP, and Agent gateway, with enterprise-grade security, policy enforcement, and deployment flexibility (including air-gapped and on-prem options) designed in rather than bolted on, which makes it my starting recommendation for teams that need production-grade AI infrastructure without stitching together multiple tools.
There is no reason to deploy the most complicated option simply because it has the most features.
Conclusion
Open-source LLM gateways are becoming a practical infrastructure layer for enterprises running AI across multiple providers, applications, and deployment environments. But the right gateway is determined by architecture and operational fit, not by a simple feature-count comparison.
Bifrost is worth evaluating for performance-focused AI gateway workloads. LiteLLM is a strong fit for multi-provider model abstraction. Kong AI Gateway makes sense when AI governance needs to connect with enterprise API infrastructure. Apache APISIX is compelling for teams that want a programmable API and AI gateway together. Envoy AI Gateway is well-suited to organizations already invested in Kubernetes and Envoy-based networking.
The most important decision is therefore not "Which gateway has the most features?"
It is:
"Which gateway gives our applications the control we need without creating another infrastructure problem?"
That is the question that matters when moving enterprise AI from experimentation to production.
Frequently Asked Questions (FAQs)
1. What is the best LLM gateway in 2026?
There is no single best gateway. Bifrost is for performance-focused AI gateway workloads; LiteLLM is well-suited to multi-provider LLM access; Kong AI Gateway is for enterprise AI governance; Apache APISIX is for API-plus-AI infrastructure; and Envoy AI Gateway is for Kubernetes and Envoy environments.
2. Why should enterprises use an LLM gateway?
An LLM gateway centralizes model routing, authentication, rate limiting, failover, observability, token usage, and provider management. This reduces duplicated AI infrastructure logic across applications and makes multi-provider architectures easier to operate.
3. Is LiteLLM better than Bifrost?
Neither is universally better. LiteLLM is particularly strong when provider abstraction is the main requirement, while Bifrost is worth considering when AI gateway performance, routing, and failover are major priorities. The right choice should be validated against your workload and infrastructure.
4. Can Apache APISIX work as an LLM gateway?
Yes. Apache APISIX provides AI gateway capabilities including multi-provider routing, load balancing, retries, fallback, token rate limiting, security, and observability. It can also manage API and AI traffic through the same gateway architecture.
5. Should I use an LLM gateway if I already have an API gateway?
Not necessarily. First, determine whether your existing API gateway can satisfy your AI requirements. If it already provides authentication, routing, rate limiting, observability, and extensibility, extending it may be operationally simpler. A dedicated LLM gateway becomes more attractive when you need deeper model-provider abstraction and LLM-specific traffic controls.
Top comments (4)
We went with LiteLLM first for the provider abstraction, but hit real limits once traffic grew, retries stacking on top of retries and no clean token level cost breakdown. Ended up putting Kong in front since we already ran it for regular API traffic, so extending it saved us from managing two separate gateway layers. Would add one thing to your production checklist: test what happens when the gateway itself restarts or redeploys mid stream, that exposed more issues for us than provider failover did.
Absolutely agree on the restart/redeploy point. Provider failover gets most of the attention, but mid-stream gateway failures can expose a completely different class of production issues.
And the decision to put Kong in front makes a lot of sense if it was already handling your API traffic, fewer gateway layers to operate is a win in itself. Thanks for sharing the real-world perspective!
Solid breakdown, especially the point about testing failover behavior rather than just confirming it exists. I've seen teams add a gateway, assume retries and fallbacks are handled, and then get surprised in production when a provider rate limits them and the "fallback" adds latency instead of masking it.
One thing I'd add to the evaluation list: watch how each gateway handles partial failures in streaming responses. A provider timing out mid-stream is a very different failure mode than a clean 429 before the response starts, and not every gateway degrades gracefully there.
Also agree with the framing around existing infrastructure. Teams already running Kong or APISIX often reach for a dedicated LLM gateway out of habit rather than necessity, when extending what they already operate would mean one less system to patch and monitor. Good reminder to start with architecture fit instead of the feature checklist.
Great points. Streaming failures are definitely worth adding to the evaluation checklist. A mid-stream timeout can expose very different failure behavior than a 429, especially around retries, partial responses, and latency.
And I completely agree on architecture fit. If a team already runs Kong or APISIX, extending that infrastructure can often be more practical than introducing another gateway just for AI traffic. The goal should be reducing operational complexity, not adding another layer by default.