7 Best AI Gateways with Rate Limiting for Multi-Tenant Applications

This post compares the top AI gateways for managing rate limits in multi-tenant applications, evaluating them on token-based limits, hierarchical quotas, and provider failover. Bifrost emerges as the top choice for its comprehensive, low-latency approach to both internal and provider-facing rate limits.

Running a multi-tenant AI application introduces a critical infrastructure challenge: how to manage costs and ensure fair resource allocation when user traffic is unpredictable and a single request can be thousands of times more expensive than another. Traditional request-per-minute (RPM) rate limiting is insufficient for LLM workloads where costs are driven by token consumption. A runaway agent or a single tenant's batch job can exhaust an entire provider budget, triggering 429 errors and causing outages for all other tenants.

This problem requires a dedicated infrastructure layer. An AI gateway sits between your application and the LLM providers, centralizing control over both provider-imposed rate limits and internal tenant quotas. A capable gateway handles multi-tenancy by assigning granular, token-aware rate limits and budgets to each user, team, or customer, ensuring one tenant cannot disrupt service for others.

This article evaluates the best AI gateways that provide robust rate-limiting capabilities specifically for multi-tenant applications.

Key Criteria for Multi-Tenant Rate Limiting

A gateway's effectiveness in a multi-tenant environment depends on several key features:

• Token-Based Limits: The ability to set limits on tokens-per-minute (TPM) in addition to requests-per-minute (RPM).
• Hierarchical Quotas: The capacity to define and enforce nested budgets and limits (e.g., per-user, per-team, per-customer).
• Virtual Keys/Tenants: A mechanism to issue unique credentials per tenant that map to specific policies without exposing provider keys.
• Automatic Failover & Load Balancing: The ability to route traffic away from providers whose rate limits have been exhausted.
• Low Latency Overhead: The gateway itself must not become a bottleneck, especially under high load.

The Top 7 AI Gateways

1. Bifrost

Bifrost is a high-performance, open-source AI gateway from Maxim AI, written in Go. It offers the most comprehensive and granular rate-limiting system for multi-tenant applications, adding only 11 microseconds of overhead per request.

Bifrost addresses multi-tenancy through Virtual Keys. Each virtual key is a unique credential that can be assigned to a specific tenant, team, or user. These keys carry their own independent budgets and rate limits, which are enforced on every request.

Key Rate-Limiting Features:

• Hierarchical Governance: Bifrost enforces limits at four distinct levels: Provider, Virtual Key, Team, and Customer. A request must have sufficient budget at all applicable levels to proceed, providing robust protection against overages.
• Token and Request Limits: Rate limits can be configured based on both request counts and token counts (request_max_limit, token_max_limit) over flexible time windows (minute, hour, day).
• Provider-Level Controls: Within a single virtual key, administrators can set separate rate limits and budgets for each downstream LLM provider. This isolates tenants from one another's provider-specific quota issues.
• Intelligent Failover: When a provider's rate limit is reached, Bifrost automatically routes traffic to a healthy fallback provider without dropping the request.
• Multi-Key Load Balancing: It can pool multiple provider API keys to increase the total available rate limit, distributing traffic intelligently across them.

# Example Bifrost Virtual Key Configuration
keys:
  - virtual_key: "vk-tenant-a-prod"
    request_max_limit: 1000 # requests per minute
    token_max_limit: 500000 # tokens per minute
    models: ["openai/*", "anthropic/claude-3-sonnet-20240229"]
    # ... other provider configs

Best for: Teams building production-grade, multi-tenant AI applications that require granular, low-latency, and hierarchical rate limiting with automatic failover.

2. Kong AI Gateway

The Kong AI Gateway extends Kong's popular open-source API gateway with AI-specific plugins. For organizations already using Kong to manage their microservices, this offers a familiar path to governing LLM traffic. Multi-tenancy is typically handled through Kong's concept of "Consumers," which can represent individual tenants.

Key Rate-Limiting Features:

• Plugin-Based Architecture: Rate limiting is implemented via plugins like rate-limiting and the enterprise ai-rate-limiting-advanced.
• Token-Aware Limits: The enterprise version supports token-based rate limiting, which is crucial for controlling LLM costs and usage accurately.
• Per-Consumer Policies: Rate limits can be applied on a per-consumer basis, allowing different tenants to have different usage tiers.
• Multiple Strategies: Kong supports local (in-memory), cluster (database), and redis strategies for counter storage, allowing teams to trade off performance and accuracy.

Best for: Enterprises already invested in the Kong ecosystem that need to apply familiar, robust API management patterns to their AI workloads.

3. LiteLLM

LiteLLM is a popular open-source Python proxy that provides a unified interface to over 100 LLM providers. It has built-in support for multi-tenancy through virtual keys, teams, and user-level management.

Key Rate-Limiting Features:

• Multi-Level Limits: LiteLLM can enforce RPM and TPM limits at the server, virtual key, user, and team levels.
• Budget Management: It supports budget controls in USD per API key, which can be reset over configurable time periods.
• Redis-Backed Tracking: Rate limit counters are typically stored in Redis, allowing for consistent enforcement across a distributed deployment.
• Active Community: As a widely adopted open-source project, it has extensive documentation and community support.

The primary trade-off with LiteLLM is the performance overhead of the Python runtime, which can add milliseconds of latency per request at scale.

Best for: Python-centric teams that need a quick and flexible way to implement basic multi-tenant rate limiting and provider abstraction.

4. Cloudflare AI Gateway

Cloudflare AI Gateway is a managed service that runs on Cloudflare's global edge network. It provides analytics, caching, and rate limiting for AI traffic without requiring teams to host their own infrastructure.

Key Rate-Limiting Features:

• Edge Enforcement: Limits are applied at the edge, close to the user, which can reduce latency for rejected requests.
• Simple Configuration: Rate limiting rules are configured through the Cloudflare dashboard with a straightforward UI.
• Analytics and Logging: Provides detailed visibility into requests, tokens, and costs, which helps in setting appropriate limits.
• Integration with Cloudflare Stack: It works seamlessly with other Cloudflare products like Workers and Zero Trust for enhanced security.

While easy to set up, Cloudflare's rate limiting is less granular than dedicated gateways like Bifrost, lacking native support for token-based limits and deep hierarchical tenant structures.

Best for: Teams already using the Cloudflare ecosystem who need simple, edge-based request limiting and analytics for their AI applications.

5. Azure API Management

For teams building on the Microsoft Azure stack, Azure API Management (APIM) offers native, sophisticated capabilities for AI workloads. It is the most advanced of the major cloud-provider gateways for token-aware enforcement.

Key Rate-Limiting Features:

• Native Token-Based Limits: The llm-token-limit policy provides true token-based rate limiting, even pre-calculating prompt tokens to avoid unnecessary backend calls.
• Policy-Driven: As a mature API management solution, it allows for complex rules and conditional logic to be applied to different tenants (represented as "products" or "subscriptions").
• Deep Azure Integration: It integrates tightly with Azure Entra ID for authentication and Azure Monitor for observability.

Best for: Organizations committed to the Azure cloud that need an enterprise-grade, platform-integrated solution for managing multi-tenant AI traffic.

6. Apache APISIX

Apache APISIX is a high-performance, open-source API gateway known for its dynamic configuration and plugin-based architecture. It can be extended with AI-specific capabilities for managing LLM traffic.

Key Rate-Limiting Features:

• Dynamic Plugins: Rate limiting rules can be updated dynamically without gateway restarts.
• AI-Specific Logic: Supports plugins for smart traffic routing based on cost or latency and token-based limiting.
• Extensibility: Teams can write custom plugins in Lua or other languages to implement complex, tenant-specific logic.

Best for: Engineering teams with existing APISIX deployments or those who need a highly extensible, open-source platform to build custom multi-tenant AI governance.

7. OpenRouter

OpenRouter is a managed service that provides a unified API to a wide range of open-source and proprietary models. While primarily a model router, it enforces rate limits that are important for developers to understand.

Key Rate-Limiting Features:

• Tiered Limits: OpenRouter has different rate limits for free-tier users versus users who have purchased credits. After a one-time purchase of $10 in credits, the daily request limit for free models increases from 50 to 1,000.
• Per-Key Limits: Rate limits are applied on a per-API-key basis, allowing some degree of tenant isolation if each tenant is given a separate key.
• Inherited Limits: Many rate limits are inherited directly from the upstream model provider. A 429 error often originates from the provider, not OpenRouter's own throttling.

OpenRouter's rate limiting is less about enforcing internal business rules and more about managing its own upstream capacity. It's a good option for routing, but teams needing to enforce their own complex multi-tenant quotas will need a gateway in front of it.

Best for: Developers and startups looking for simple access to a wide variety of models who can operate within a per-key rate-limiting structure.

Conclusion

For multi-tenant applications, rate limiting is not just a technical feature but a core component of cost control, platform stability, and customer fairness. While traditional API gateways offer request-based limiting, the variable cost of LLM requests demands token-aware, hierarchical controls.

Among the available options, Bifrost provides the most complete and performant solution. Its design around virtual keys, multi-level budget enforcement, and automatic provider failover directly addresses the primary challenges of serving AI to multiple tenants, all with negligible performance impact. Teams evaluating their options can start by exploring the Bifrost open-source repository or requesting a demo to see how its governance model fits their architecture.

Sources

• How AI Gateways Tackle Rate Limiting for LLM Apps - maxim.ai
• Budget and Limits - Bifrost AI Gateway Docs - getbifrost.ai
• Kong AI Rate Limiting Advanced Plugin Docs - konghq.com
• Multi-Tenant Architecture with LiteLLM - litellm.ai
• Cloudflare AI Gateway Docs - cloudflare.com