Five gateways recur in a 2026 shortlist for enterprise multi-model routing: Bifrost, Kong AI Gateway, LiteLLM, Cloudflare AI Gateway, and Azure API Management. They are not interchangeable. Bifrost and LiteLLM are self-managed, Kong AI Gateway is a set of features inside Kong’s API platform, Cloudflare AI Gateway is a hosted service, and Azure API Management is cloud API management. Read the list as a set of candidates to test against your own requirements, not as a ranking.
What an AI gateway does for multi-model routing
An AI gateway gives applications one endpoint for calling model providers and one place to apply organizational rules: which models teams may call, how each request is routed, what happens when a provider fails, and how usage is counted. Without that layer, each application holds its own provider credentials, retry logic, and cost tracking, and every provider rate limit or outage becomes an application-level problem.
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It helps to separate this from a conventional API gateway. An API gateway manages HTTP traffic for services: authentication, path-based routing, and request limits per client. An AI gateway keeps those functions and adds model-aware ones: routing by model or provider, budgets measured in tokens rather than requests, translation between provider request formats, and fallback when a model is unavailable. Kong and Azure API Management add these functions to API platforms that many organizations already run, which is why the choice often depends on what is already in place.
How routing and failover decisions are made
Model routing is the decision about which model or deployment serves a given request. The shortlist describes four mechanisms, and products differ in which they offer:
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- Weighted or static balancing sends fixed shares of traffic to each deployment. Bifrost documents weighted routing, and LiteLLM routes across configured deployments.
- Conditional rules branch on request attributes. Cloudflare’s Dynamic Routing offers conditional branches. The attributes a rule can read vary by product, so check them before designing routes.
- Percentage splits move traffic gradually, for example a 5 percent share to a new model. Cloudflare documents percentage nodes in Dynamic Routing.
- Adaptive selection shifts traffic based on observed conditions. Bifrost documents adaptive load balancing; confirm whether it responds to provider health, latency, or both.
Routing picks the first destination. Failure handling decides what happens when that destination fails: how many retries occur, which provider is tried next, and how the gateway treats rate limits, timeouts, authentication errors, and unavailable models. Whether a fallback preserves the original request’s behavior is a separate question. These details differ most across products and are the least fully described in published overviews, so they belong at the top of your testing list.
The five gateways at a glance
The table uses only attributes the shortlist documents. “Not stated” means the source does not describe that attribute; it does not mean the product lacks it.
| Gateway | Deployment model | Routing and resilience as documented | Governance as documented | Release status |
|---|---|---|---|---|
| Bifrost | Self-hosted | Rules, weighted routing, adaptive load balancing, fallback behavior | Not stated | Not stated |
| Kong AI Gateway | Feature set within Kong’s API platform; deployment options not stated | Consistent API across major providers; failover; caching | Token budgets; prompt controls | Not stated |
| LiteLLM | Self-managed proxy | Deployment routing and retries; Redis-backed shared rate-limit state across proxy instances | Not stated | Not stated |
| Cloudflare AI Gateway | Hosted | Dynamic Routing: conditional branches, percentage splits, model calls | Quota controls | Dynamic Routing: Beta |
| Azure API Management | Cloud API management, managing models from Microsoft Foundry and other providers | Endpoint load balancing; monitoring | Authentication and authorization; token quotas | Unified multi-provider model API: Preview |
Gateway profiles
Bifrost
Maxim describes Bifrost as a self-hosted gateway with rules, weighted routing, adaptive load balancing, and fallback behavior. The comparison reports 11 microseconds of gateway overhead at 5,000 requests per second. That is the publisher’s own benchmark, and its workload is not independently verified, so treat it as a claim to reproduce rather than a measured result for your traffic. The comparison does not say which functions require an Enterprise license. Ask the vendor for a feature-to-license mapping before you plan around weighted or adaptive routing.
Best fit: teams that want to run the gateway themselves, need weighted or adaptive routing, and have the staff to operate it.
Kong AI Gateway
Kong’s documentation describes one consistent API across major model providers, with model-provider management, token budgets, caching, prompt controls, and failover. It is the natural candidate if your organization already operates Kong’s API platform, because it extends policies and operating practices you already maintain. Confirm which plugins and routing policies your license and deployment model include, and test how provider-specific request formats and provider failures surface to your applications.
Best fit: organizations already running Kong’s API platform that want model routing under the same controls.
LiteLLM
LiteLLM documents a proxy and router with deployments, retries, and Redis-backed shared rate-limit state across multiple proxy instances. You run it yourself, so you own its availability, scaling, security configuration, and upgrades. That suits a platform team that wants direct control of routing and does not want a hosted dependency. It is a poorer fit for a team without capacity to operate a stateful proxy and its Redis dependency. Check how your chosen routing strategy behaves in the release you deploy.
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Best fit: platform teams that can operate proxy infrastructure and want full control of routing behavior.
Cloudflare AI Gateway
Cloudflare’s Dynamic Routing documentation, last updated October 2, 2026, describes versioned route flows with conditional branches, percentage splits, model calls, and quota controls. In the document’s words, “Dynamic routing enables you to create request routing flows through a visual interface or a JSON-based configuration.” Dynamic Routing is labeled Beta, so confirm its current limits before making it a dependency for production traffic. As a hosted service, it also needs review on data handling, logging, provider availability, and whether it meets your residency requirements.
Best fit: teams that want hosted routing with percentage rollouts and can accept Beta-stage constraints.
Azure API Management
Microsoft documents AI gateway capabilities in Azure API Management: authentication and authorization, endpoint load balancing, monitoring, token quotas, and management of models from Microsoft Foundry and other providers. Its unified multi-provider model API is labeled Preview, so check both its status and regional availability for your deployment region. Confirm current Azure pricing and support terms, and list the specific APIs and policies you need before comparing it with the other four.
Best fit: organizations already running Azure API Management or building on Microsoft Foundry that want model routing under the same governance layer.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How much weight the shortlist can carry
The comparison was published by Maxim, the company behind Bifrost, and it favors Bifrost. Its ordering and performance claims are the publisher’s own, not an independent market ranking. No independent benchmark compares these five gateways on the same workload, so latency figures from different vendors should not be placed side by side.
The five are also not like-for-like. Self-managed proxies, features inside a broader API gateway, hosted AI routing, and cloud API management solve adjacent problems and have different owners. A team that already operates an API platform will weigh them differently from a team starting from scratch. Use the list to decide which products to test, and let your own requirements set the order.
What to check before choosing
- Deployment and data control: whether prompts, provider credentials, logs, and routing state stay where your policy requires. Depending on the product, the gateway is customer-operated, hosted, or managed inside an existing cloud or API platform, and each leaves different data in different places.
- Routing inputs: which request attributes policies can read, whether routing is static, conditional, or health- and latency-aware, and how route changes are tested and rolled back.
- Failure handling: the retry limit, fallback order, key rotation process, and how rate limits, provider errors, authentication failures, timeouts, and unavailable models are handled. Confirm that fallback keeps request semantics intact.
- Governance: identity integration, approved-model restrictions, team-level token budgets, audit logs, prompt and data controls, and who may change policy. Kong and Azure API Management document examples of these controls; confirm which ones need a particular edition or configuration.
- Release maturity: whether any requirement depends on a Beta or Preview feature, such as those labeled in the table above.
- Operations and support: staffing and infrastructure for self-hosted options, observability, support levels, and procurement terms.
Running a proof of concept
Documentation shows what a gateway is designed to do. Failure tests show what it does under stress. Run the same test set through each candidate:
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- Configure each candidate with the same test provider keys and, where the product allows, the same routing rules.
- Simulate provider throttling (HTTP 429 responses) and timeouts. Confirm that retries and fallback follow the documented order and limits.
- Rotate a provider key during a live run. Confirm traffic recovers without manual edits and without dropped requests.
- Block one approved model for one team. Confirm the restriction is enforced and, where the product provides audit logs, that the denied request appears in them.
- Record added gateway latency, end-to-end latency, error rates, response quality and format differences between providers, and token usage and cost per request.
- Repeat the tests for every Beta or Preview feature you depend on, against the release you plan to deploy.
Matching a gateway to your situation
- Already running Kong’s API platform: start with Kong AI Gateway and confirm that your license covers the routing and budget features you need.
- Want a self-managed proxy and can run it, including Redis for shared rate limits: LiteLLM is the option the shortlist documents for this model.
- Want a self-hosted gateway with weighted and adaptive routing: Bifrost. Get the license mapping in writing and test latency on your own traffic.
- Want hosted routing with percentage rollouts: Cloudflare AI Gateway, after checking data handling and residency fit.
- Building on Azure or Microsoft Foundry: Azure API Management, with the Preview status of its unified model API built into your rollout plan.
Pricing and what may change
This article does not compare prices. The vendor material behind it describes features and positioning and does not give comparable price points for all five. Model-provider charges apply whichever gateway you choose; gateway charges depend on license, deployment, and cloud billing, so request written quotes from each vendor.
The information here reflects vendor documentation available in early October 2026. Routing features, provider integrations, release labels, pricing, and partner terms change, so recheck the vendor’s current documentation on the day you decide.
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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
