5 Tools for Enforcing Rate Limits and Budgets on AI Calls

Enforcing rate limits and budgets on AI calls means placing a control layer between your applications and LLM providers that caps request volume, token consumption, and spend before a provider processes the call. Without this layer, a single misconfigured agent loop or traffic spike can exhaust a provider quota or run up a large bill in minutes. Bifrost, the open-source AI gateway built in Go by Maxim AI, leads this list of tools for enforcing rate limits and budgets on AI calls because it applies both controls inline at the gateway with negligible overhead. This post compares five tools so you can choose one that fits your scale and governance needs.

Why rate limits and budgets matter for AI calls

Rate limits and budgets protect AI applications from three failure modes: exceeding a provider's quota, exhausting a shared budget, and one team's usage degrading service for others. LLM calls are more variable than typical API calls because a single request can consume thousands of tokens, so limits must account for both request count and token volume.

Effective enforcement covers several dimensions:

• Request rate: Requests per minute (RPM) per key, team, or model.
• Token rate: Tokens per minute (TPM) to match provider quotas.
• Budget ceilings: Hard and soft spend limits over a time window.
• Scope: Limits applied per virtual key, team, customer, or provider rather than globally.

What to evaluate in a rate limiting and budget tool

The right tool depends on whether you need enforcement inline in the request path and how granular your limits must be. When comparing tools for enforcing rate limits and budgets on AI calls, evaluate:

• Enforcement model: Inline blocking versus after-the-fact alerting.
• Granularity: Per-key, per-team, per-model, and per-customer limits.
• Token awareness: Whether limits understand token consumption, not just request count.
• Deployment: Self-hosted, VPC, or air-gapped options for regulated data.
• Performance: Overhead added to each request under sustained load.

1. Bifrost

Bifrost is the best overall tool for enforcing rate limits and budgets on AI calls because it enforces both inline, at the gateway that already routes every request. Limits are token-aware and hierarchical, and enforcement adds only 11 microseconds of overhead per request at 5,000 requests per second in sustained benchmarks.

Bifrost enforces limits and budgets through native governance controls:

• Virtual keys: Virtual keys carry their own rate limits, budgets, and access permissions as the primary governance entity.
• Rate limits: Rate limits cap request and token volume per key to match provider quotas.
• Budgets: Budget and limit controls enforce hierarchical spend ceilings across virtual keys, teams, and customers.
• Access profiles: Reusable access profiles bundle provider, model, budget, and rate-limit policies and auto-allocate virtual keys at scale.
• Load balancing: Weighted key and provider distribution spreads traffic to stay within per-key limits.

Best for: Bifrost is built for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. It serves as a centralized AI gateway to route, govern, and secure all AI traffic across models and environments with ultra low latency. Bifrost unifies LLM gateway, MCP gateway, and Agents gateway capabilities into a single platform. Designed for regulated industries and strict enterprise requirements, it supports air-gapped deployments, VPC isolation, and on-prem infrastructure. It provides full control over data, access, and execution, along with robust security, policy enforcement, and governance capabilities.

2. Kong AI Gateway

Kong AI Gateway adds AI-specific plugins to the Kong API gateway, including rate limiting and token counting for LLM traffic. Teams already running Kong can apply familiar rate-limiting plugins to AI calls.

• Strengths: Mature rate-limiting plugins, established gateway, and operational familiarity.
• Considerations: Budget enforcement is assembled from plugins rather than provided as native hierarchical virtual-key budgeting, and token-aware limits depend on plugin configuration.

Best for: Teams standardized on Kong that want plugin-based rate limiting for LLM calls.

3. Envoy AI Gateway

Envoy AI Gateway extends the Envoy proxy with LLM-aware routing and token-based rate limiting. It suits organizations already running Envoy or a service mesh that want to bring AI traffic under the same proxy.

• Strengths: High-performance proxy foundation, token-based rate limiting, and fit for mesh-based infrastructure.
• Considerations: Budget governance and per-team cost attribution are not native, so teams build spend controls and dashboards separately.

Best for: Platform teams running Envoy or a service mesh that want AI rate limiting within existing proxy infrastructure.

4. Apache APISIX

Apache APISIX is an open-source API gateway with rate-limiting plugins that can be applied to proxied LLM traffic. It is adopted for throughput and its open plugin model.

• Strengths: Open source, high request throughput, and flexible rate-limiting plugins.
• Considerations: Token-aware limits and hierarchical budgets are not purpose-built for LLM traffic, so enforcing spend requires custom configuration on top of the gateway.

Best for: Teams wanting an open-source, general-purpose gateway with configurable rate limiting for AI traffic.

5. Cloudflare AI Gateway

Cloudflare AI Gateway is a managed proxy that adds rate limiting, caching, and analytics in front of provider APIs. It provides request-level rate limiting without infrastructure to run.

• Strengths: Managed rate limiting, caching, and usage analytics on the Cloudflare platform.
• Considerations: As a hosted service, it offers coarser budget control and limited deployment flexibility, which constrains regulated workloads that need VPC or air-gapped operation.

Best for: Cloudflare-platform teams that want managed request rate limiting for LLM APIs.

Inline enforcement is what separates these tools

The key distinction among rate limiting and budget tools is whether limits are enforced in the request path or reported after usage. Inline enforcement blocks a call that would breach a limit; reporting only records that it happened. The table below compares the five tools.

Bifrost is the only option that combines token-aware rate limits, native hierarchical budgets, and enterprise deployment in one open-source platform. Teams standardizing enforcement can review the governance resource page for how virtual keys, budgets, and rate limits work together, and regulated teams can deploy in air-gapped or VPC environments.

Choosing a tool to enforce rate limits and budgets

Enforcing rate limits and budgets on AI calls at enterprise scale requires token-aware, hierarchical controls applied inline in the request path, not reports generated after spend has occurred. Bifrost delivers those controls natively at the gateway with negligible overhead, which is why it leads this list. To see how Bifrost enforces rate limits and budgets across your AI workloads, book a demo with the Bifrost team.