TL;DR: When AI workloads span multiple providers, static routing breaks fast. Dynamic routing at the gateway layer lets teams distribute requests by weight, cost, latency, or availability, without touching application code. Here are five gateways built for it.
<hr>
<h2>Why Dynamic Routing Matters</h2>
A single-provider setup is simple until it is not. Add a second model for cost optimization, a third for redundancy, and suddenly the routing logic lives scattered across your codebase. Every new provider means more conditionals, more error handling, more deployment risk.
Dynamic routing at the gateway layer solves this cleanly. The gateway decides where each request goes based on configurable rules. Your application sends one request to one endpoint. The <a href="https://docs.getbifrost.ai/features/governance/routing" target="_blank" rel="noopener nofollow">routing config</a> handles the rest.
Here are five enterprise-grade gateways that handle dynamic routing well.
<hr>
<h2>1. Bifrost (by Maxim AI)</h2>
<a href="https://git.new/bifrost" target="_blank" rel="noopener nofollow">GitHub</a> | <a href="https://getmax.im/bifrostdocs" target="_blank" rel="noopener nofollow">Docs</a> | <a href="https://getmax.im/bifrost-home" target="_blank" rel="noopener nofollow">Website</a>
What it does: Open-source LLM gateway written in Go. Purpose-built for production workloads where routing decisions need to happen at microsecond speed.
Routing features:
<ul>
<li><a href="https://docs.getbifrost.ai/features/governance/routing" target="_blank" rel="noopener nofollow">Weighted routing</a> with percentage-based traffic distribution across providers</li>
<li>Automatic <a href="https://docs.getbifrost.ai/features/fallbacks" target="_blank" rel="noopener nofollow">failover</a> when a provider returns errors or exceeds latency thresholds</li>
<li>Four-tier <a href="https://docs.getbifrost.ai/features/governance" target="_blank" rel="noopener nofollow">governance hierarchy</a> (Customer, Team, Virtual Key, Provider Config) so routing rules can be scoped per team or project</li>
<li>Dual-layer semantic caching reduces unnecessary provider calls entirely</li>
</ul>
Get started in one command:
<pre><code>npx @anthropic-ai/bifrost
</code></pre>
Follow the full <a href="https://docs.getbifrost.ai/quickstart/gateway/setting-up" target="_blank" rel="noopener nofollow">setup guide</a> for production deployments.
Strengths: 11 microsecond latency overhead (<a href="https://docs.getbifrost.ai/benchmarking/getting-started" target="_blank" rel="noopener nofollow">benchmarks</a>). 5,000 RPS sustained throughput. 50x faster than Python-based alternatives. Routing decisions happen at compiled-code speed, not interpreted-code speed. The weighted distribution and failover logic run in the same request path with near-zero overhead.
Limitations: Younger project. Fewer third-party integrations compared to established API management platforms.
<hr>
<h2>2. LiteLLM</h2>
What it does: Python-based proxy providing a unified OpenAI-compatible interface to 100+ providers.
Routing features:
<ul>
<li>Router class with configurable model groups and fallback chains</li>
<li>Weighted random selection across deployments</li>
<li>Cooldown logic for failing providers</li>
</ul>
Strengths: Broadest provider coverage. Active community. Flexible YAML-based configuration.
Limitations: Python runtime adds measurable latency at the proxy layer. At high RPS, the routing overhead becomes significant compared to compiled alternatives.
<hr>
<h2>3. Kong AI Gateway</h2>
What it does: AI-specific plugins for the Kong API Gateway, targeting teams already running Kong infrastructure.
Routing features:
<ul>
<li>Load balancing across upstream AI services</li>
<li>Health checks with automatic unhealthy target removal</li>
<li>Rate limiting tied to consumer groups</li>
</ul>
Strengths: Battle-tested API gateway foundation. Strong plugin ecosystem. Good fit for organizations with existing Kong deployments.
Limitations: Not AI-native. Features like weighted model routing or semantic caching require custom plugin development or external tooling.
<hr>
<h2>4. Cloudflare AI Gateway</h2>
What it does: Managed gateway service from Cloudflare that proxies requests to AI providers through their edge network.
Routing features:
<ul>
<li>Fallback model chains with ordered provider lists</li>
<li>Request retries with configurable attempts</li>
<li>Edge-based caching for repeated prompts</li>
</ul>
Strengths: Global edge network means low network latency to most regions. Simple setup through the Cloudflare dashboard. Caching and rate limiting included by default.
Limitations: Fully managed, no self-hosted option. Limited routing granularity. No weighted distribution or team-level routing scoping.
<hr>
<h2>5. Azure API Management</h2>
What it does: Microsoft’s API management platform with AI gateway capabilities for Azure OpenAI and third-party providers.
Routing features:
<ul>
<li>Backend pools with weighted load balancing</li>
<li>Circuit breaker pattern for failing backends</li>
<li>Policy-based routing with conditional expressions</li>
</ul>
Strengths: Deep Azure ecosystem integration. Enterprise compliance and security features. Policy engine is highly flexible for complex routing rules.
Limitations: Azure-centric. Heavy operational overhead for teams not already on Azure. Pricing can scale steeply with request volume.
<hr>
<h2>Comparison Table</h2>
<img src="https://media.daily.dev/image/upload/s--3eSQvkVo--/f_auto/v1775195199/ugc/content_120acd78-4fec-4956-94c4-2bd3c83c77dc?_a=BAMAMiWQ0" alt="image.png">
<hr>
<h2>Picking the Right Gateway</h2>
For teams that need routing decisions at minimal latency with granular <a href="https://docs.getbifrost.ai/features/governance" target="_blank" rel="noopener nofollow">governance</a> controls, <a href="https://git.new/bifrost" target="_blank" rel="noopener nofollow">Bifrost</a> is worth evaluating first. The Go-based architecture keeps the gateway from becoming the bottleneck, and the four-tier hierarchy maps cleanly to how engineering organizations actually assign budgets and permissions.
LiteLLM works well for teams that prioritize provider breadth over raw performance. Kong fits if it is already in the stack. Cloudflare is the simplest managed option. Azure APIM suits enterprise teams deep in the Microsoft ecosystem.
The key decision: routing logic belongs in your infrastructure layer, not your application code. Start with the <a href="https://getmax.im/bifrostdocs" target="_blank" rel="noopener nofollow">Bifrost docs</a> or check the source on <a href="https://git.new/bifrost" target="_blank" rel="noopener nofollow">GitHub</a>.

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A comparison of five enterprise AI gateways that support dynamic routing across multiple LLM providers. Covers Bifrost (Go-based, open-source, 11µs overhead), LiteLLM (Python, 100+ providers), Kong AI Gateway (plugin-based), Cloudflare AI Gateway (managed edge), and Azure API Management. Each is evaluated on routing features, strengths, and limitations. The core argument is that routing logic belongs in the infrastructure layer rather than application code.

Top 5 Enterprise AI Gateways for Dynamic Routing in 2026