Top 5 AI Gateways to Monitor and Control the Costs of LLMs

LLM API costs are one of the fastest-growing line items in enterprise technology budgets. A customer support agent handling 10,000 daily conversations can generate over $7,500 per month in API costs alone. Multiply that across multiple teams, products, and providers, and costs quickly become unpredictable and unmanageable.

The core problem is architectural. When every team and application calls LLM providers directly, there is no shared layer to enforce budgets, cache repeated queries, route to cost-optimal models, or even track where tokens are being consumed. Hidden costs from embeddings, retries, logging, and rate-limit management can account for 20 to 40% of total LLM operational expenses on top of raw API fees.

An AI gateway solves this by sitting between your applications and LLM providers, adding caching, routing, rate limits, and budget controls through a single infrastructure layer. This guide covers the five best AI gateways for monitoring and controlling LLM costs in 2026.

1. Bifrost

Bifrost is a high-performance, open-source AI gateway built in Go that gives engineering teams the most complete cost operations toolkit available today. It unifies access to 20+ providers through a single OpenAI-compatible API, and every request flows through a centralized control plane where cost policies are enforced in real time, not after the bill arrives.

Cost monitoring and control features:

• Hierarchical budget management through a four-tier structure: Customer, Team, Virtual Key, and Provider Configuration. Set hard spending limits at any level, and when a budget is exhausted, Bifrost blocks subsequent requests automatically before additional charges accumulate.
• Virtual keys that isolate usage across different teams, projects, or customers. Each virtual key carries its own budget, rate limits, and model access controls, making it ideal for multi-tenant SaaS applications where per-customer cost isolation is critical.
• Semantic caching that matches requests by meaning rather than exact text. This eliminates redundant API calls for semantically similar prompts, delivering up to 40% cost reduction without changing application logic.
• Automatic failover between providers and models. When a primary provider hits rate limits or returns errors, Bifrost reroutes to the next configured provider, keeping applications responsive while preventing wasted tokens on failed retries.
• Built-in observability with native Prometheus metrics that expose per-provider cost data for real-time dashboards and spend alerts. Teams can query metrics like daily cost estimates by provider and pipe them into Grafana or existing FinOps tooling.
• Intelligent load balancing with weighted distribution across multiple API keys and providers, enabling cost-tiered access where cheaper models handle basic tasks and premium models are reserved for complex workloads.
• Token-based and request-based rate limits that operate in parallel at both the virtual key and provider configuration level, aligning controls with how providers actually bill.

Bifrost adds only 11 microseconds of overhead at 5,000 requests per second, meaning the gateway layer itself contributes virtually nothing to your infrastructure costs. Zero-configuration startup via npx -y @maximhq/bifrost gets a fully functional gateway running in under 30 seconds, and the Apache 2.0 license ensures there are no licensing fees eating into your savings.

Best for: Engineering teams that need real-time budget enforcement, semantic caching, and granular cost attribution across teams and customers without building custom metering infrastructure.

Book a Bifrost demo to see hierarchical cost controls in action.

2. Cloudflare AI Gateway

Cloudflare AI Gateway provides a managed proxy layer on Cloudflare's global edge network that gives teams a lightweight entry point for LLM cost visibility.

Cost-related strengths:

• Response caching at the edge that serves identical requests from Cloudflare's CDN, reducing redundant provider API calls and cutting per-request costs.
• Usage analytics dashboard with visibility into request counts, token consumption, and estimated spend across providers.
• Rate limiting to cap request volume per consumer and prevent runaway usage.
• Free tier available, making it a zero-cost starting point for basic observability.

Limitations: Cloudflare AI Gateway lacks semantic caching (only exact-match caching), hierarchical budget enforcement, virtual keys, per-team spend attribution, and multi-provider failover. It functions primarily as an observability and caching layer rather than a full cost governance platform. Teams scaling beyond basic proxy needs consistently outgrow its capabilities.

Best for: Teams already on Cloudflare that want basic cost visibility and edge caching with minimal setup and no infrastructure management.

3. LiteLLM

LiteLLM is an open-source Python library and proxy server that standardizes access to 100+ LLM providers. It offers basic cost tracking features that work well for development and prototyping workflows.

Cost-related strengths:

• Spend tracking per API key and per team, with support for setting basic budget limits.
• Virtual key management that allows different keys for different projects with independent spend tracking.
• Broad provider coverage enabling teams to route to the cheapest available model for a given task.
• Self-hosted deployment keeps infrastructure costs predictable.

Limitations: LiteLLM's Python-based architecture introduces meaningful latency overhead at high concurrency due to the Global Interpreter Lock. Published benchmarks show P99 latency climbing significantly at 500+ RPS compared to Go-based alternatives, and that latency overhead itself translates to infrastructure cost. Enterprise budget features like SSO, RBAC, and team-level enforcement are locked behind the paid Enterprise license. Users have also reported that the database logging layer slows request processing after accumulating over 1 million logs, a threshold that teams processing 100,000 daily requests hit in just 10 days.

Best for: Python-heavy teams that need basic spend tracking during development and prototyping, where high concurrency performance and enterprise-grade budget enforcement are not immediate requirements.

4. Kong AI Gateway

Kong AI Gateway extends Kong's mature API management platform to handle LLM traffic, bringing enterprise governance capabilities to AI cost management.

Cost-related strengths:

• Token-based rate limiting through the AI Rate Limiting Advanced plugin, which operates on actual token consumption rather than raw request counts, aligning controls with provider billing.
• Model-level rate limits that can be set per model (for example, GPT-4o versus Claude Sonnet) for cost-aligned enforcement.
• Semantic caching to reduce redundant calls and lower per-request costs.
• Enterprise analytics dashboards for tracking AI consumption as API requests and token usage.

Limitations: Kong AI Gateway requires an existing Kong deployment, making it impractical for teams without prior Kong infrastructure. Advanced AI-specific cost features like token-based rate limiting are restricted to the Enterprise tier, and pricing targets larger organizations. The adoption curve is steeper than standalone AI gateways, and the overall cost of running Kong infrastructure can offset savings on LLM spend.

Best for: Enterprises already running Kong for traditional API management that want to bring LLM cost governance under the same operational layer without adopting a separate tool.

5. AWS Bedrock

AWS Bedrock provides a managed multi-model service with built-in cost controls for organizations deeply invested in the AWS ecosystem.

Cost-related strengths:

• Provisioned throughput pricing that allows teams to reserve capacity at predictable costs rather than paying per-token at variable rates.
• CloudWatch integration for monitoring token usage, request counts, and estimated spend with native AWS alerting.
• IAM-based access controls that restrict model access at the user and role level to prevent unauthorized usage.
• Service Quotas for setting request and token limits per account or per region.

Limitations: Bedrock is limited to models available within the AWS ecosystem, which narrows provider flexibility. There is no semantic caching, no virtual key abstraction for multi-tenant cost isolation, and no built-in failover to non-AWS providers. Teams using models outside Bedrock still need a separate gateway for unified cost tracking, creating fragmented visibility.

Best for: Organizations already running workloads on AWS that want native cost controls and provisioned pricing for Bedrock-supported models without adding external infrastructure.

Choosing the Right Gateway for LLM Cost Control

The right cost management gateway depends on your provider mix, scale, and operational requirements:

• If you need real-time budget enforcement, semantic caching, and per-team cost attribution, Bifrost delivers the most complete cost operations toolkit with virtually zero overhead. Its hierarchical budget system and open-source availability make it the strongest choice for teams serious about LLM spend governance.
• If you want managed edge caching with zero setup, Cloudflare AI Gateway is a lightweight starting point for basic cost visibility.
• If you need broad provider coverage for prototyping, LiteLLM offers basic spend tracking across 100+ providers in a Python-native workflow.
• If you already standardize on Kong, Kong AI Gateway extends familiar governance patterns to LLM traffic.
• If your models run exclusively on AWS, Bedrock provides native cost controls with provisioned throughput pricing.

LLM costs compound fast at scale, and the difference between monitoring costs and actually controlling them is the enforcement layer. An AI gateway that enforces budgets in real time, caches intelligently, and provides granular attribution is not optional infrastructure. It is how production AI teams keep spend predictable.

Ready to take control of your LLM costs? Book a Bifrost demo to see how hierarchical budget management and semantic caching work in production.