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Kuldeep Paul
Kuldeep Paul

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Top AI Gateways for Controlling LLM Costs in 2026

LLM API usage is becoming one of the fastest‑growing expenses in modern software infrastructure. Even a single production workflow can generate thousands of dollars per month in token usage, and when multiple teams, providers, and applications are involved, spend quickly becomes unpredictable.

The root cause is architectural. When applications call providers directly, there is no shared layer to enforce budgets, cache repeated requests, route traffic to cheaper models, or track where tokens are actually being consumed.

An AI gateway solves this by sitting between applications and model providers, adding routing, caching, rate limits, and budget enforcement in one place. This guide reviews five of the best AI gateways for monitoring and controlling LLM costs in 2026.

1. Bifrost

Bifrost is an open‑source AI gateway built in Go that provides one of the most complete cost‑control toolkits available today. It connects to 20+ providers through a single OpenAI‑compatible API and enforces cost policies in real time before requests reach the provider.

Cost control features

Bifrost adds only microseconds of overhead at high throughput and can be started quickly with npx -y @maximhq/bifrost. Because it is open source, teams can deploy it without licensing costs while still getting enterprise‑grade controls.

Best for: Teams that need real‑time budget enforcement, semantic caching, and detailed cost attribution across multiple applications.

Book a Bifrost demo

2. Cloudflare AI Gateway

Cloudflare AI Gateway provides a managed proxy layer that runs on Cloudflare’s edge network and offers basic visibility into LLM usage.

Strengths

  • Edge caching for identical requests
  • Usage analytics dashboard
  • Rate limiting per consumer
  • Free tier available

Limitations

No semantic caching, no hierarchical budgets, and limited per‑team attribution. It works well as a proxy but not as a full cost governance layer.

Best for: Teams already using Cloudflare that want simple observability and caching.

3. LiteLLM

LiteLLM is an open‑source proxy and Python library that standardizes access to many providers while adding basic cost tracking.

Strengths

  • Spend tracking per key
  • Budget limits per project
  • Support for many providers
  • Self‑hosted deployment

Limitations

Higher latency at scale due to Python runtime constraints and limited enterprise governance features without the paid version.

Best for: Development workflows that need lightweight spend tracking.

4. Kong AI Gateway

Kong AI Gateway extends Kong’s API management platform to LLM traffic, allowing organizations to apply existing governance patterns to AI workloads.

Strengths

  • Token‑based rate limiting
  • Model‑level limits
  • Semantic caching
  • Enterprise analytics

Limitations

Requires existing Kong infrastructure and most advanced features are in the enterprise tier.

Best for: Enterprises already running Kong.

5. AWS Bedrock

AWS Bedrock provides built‑in cost controls for workloads running inside the AWS ecosystem.

Strengths

  • Provisioned throughput pricing
  • CloudWatch monitoring
  • IAM‑based access control
  • Service quotas

Limitations

Limited to AWS models, no semantic caching, and no unified control across external providers.

Best for: AWS‑native deployments.

Choosing the Right Gateway

Different teams need different levels of cost control.

  • Real‑time enforcement → Bifrost
  • Edge proxy → Cloudflare
  • Python workflows → LiteLLM
  • Existing Kong stack → Kong
  • AWS‑only workloads → Bedrock

LLM costs grow quickly, and monitoring alone is not enough. The gateway layer must enforce budgets, route intelligently, and provide visibility across every request.

Ready to control LLM spend? Book a Bifrost demo

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