Top 5 AI Gateways for Optimizing LLM Cost in 2026

TL;DR:

LLM API costs compound fast at scale. An AI gateway adds a control layer between your app and providers to enforce caching, fallbacks, and budgets.

Bifrost (by Maxim AI) leads on cost ops with semantic caching, virtual key budgets, 12+ providers, and native observability.

Cloudflare AI Gateway is the fastest setup for serverless/edge apps, with a free tier and 350+ models.

LiteLLM is the strongest open-source option for self-hosted cost tracking across 100+ LLMs.

Vercel AI SDK suits frontend-first teams building on Next.js.

Kong AI Gateway fits enterprises extending existing API governance to LLM workloads.

A production LLM app calling GPT-4o or Claude 3.5 Sonnet at scale can burn through thousands of dollars monthly without guardrails. Prompt costs, redundant calls, untracked team usage, and zero fallback logic are the usual culprits.

An AI gateway solves this by sitting between your application and LLM providers, adding caching, routing, rate limits, and budget controls in a single layer. It is the difference between flying blind on spend and having a real cost operations workflow.

This guide covers five platforms worth considering in 2026, what each one does well, and where each one fits.

How an AI Gateway Reduces LLM Cost

Before comparing tools, it helps to understand the four primary levers:

• Caching reduces redundant provider calls by returning saved responses for repeated or semantically similar queries.
• Fallbacks and load balancing route requests to cheaper or available models when a primary provider fails or rate-limits.
• Budget controls enforce spend limits at the key, team, or project level before costs escalate.
• Observability surfaces per-model, per-team cost data so you can make informed routing and model decisions.

Each gateway below addresses these levers differently.

Comparison at a Glance

1. Bifrost by Maxim AI

Platform Overview

Bifrost is a high-performance LLM gateway built by Maxim AI. It provides a single OpenAI-compatible API for 12+ providers including OpenAI, Anthropic, AWS Bedrock, Google Vertex AI, Azure OpenAI, Cohere, Mistral, Groq, and Ollama. Teams can swap providers, enforce budgets, and run fallbacks with zero application-level code changes.

Where Bifrost differs from most gateways is the tight integration with Maxim AI's observability platform. Cost data does not live in isolation; it connects directly to production trace monitoring, evaluation workflows, and quality dashboards. This matters for teams where cost and quality are both production concerns.

Key Features

Semantic Caching

Bifrost's semantic cache stores responses based on the meaning of a request, not just exact string matches. When a new prompt is semantically similar to a cached one, Bifrost returns the cached response and skips the provider call entirely. For applications where users tend to ask overlapping questions (support bots, search tools, knowledge assistants), this meaningfully cuts API spend.

Automatic Fallbacks and Load Balancing

Fallback logic is configured at the gateway level. If a primary provider hits rate limits, returns errors, or exceeds latency thresholds, Bifrost automatically reroutes to the next configured provider or key. This keeps applications responsive without engineering effort per failure scenario.

Budget Management with Virtual Keys

Virtual keys let teams assign spend limits at any level: individual developer, team, project, or customer. Budget exhaustion blocks further requests or triggers fallback logic rather than silently accumulating cost. This is particularly useful for multi-tenant applications where each customer's usage needs to be isolated and capped.

MCP Support

Bifrost includes native support for the Model Context Protocol (MCP), enabling AI models to interact with external tools and data sources (filesystems, databases, web search) through a standardized interface. This is increasingly important as agentic applications become more common.

Native Observability

Prometheus metrics, distributed tracing, and structured logging are built in. When paired with Maxim AI's observability suite, teams get full visibility across cost, latency, model behavior, and output quality from a single platform. For teams working on LLM observability in production, this removes the need to stitch together separate tools.

Drop-in Replacement

Bifrost is designed as a drop-in replacement for existing OpenAI and Anthropic SDK calls. Changing the base URL is typically the only code change required. Provider configuration supports web UI, API-driven, or file-based setup.

Enterprise Security

HashiCorp Vault integration handles secure API key management. SSO via Google and GitHub is supported out of the box, along with fine-grained role-based access control.

Best For

Teams that need cost operations and production quality monitoring under one roof. Bifrost is particularly well-suited for organizations that want budget enforcement, semantic caching, and model fallbacks connected to the same platform where they run AI agent evaluations and production monitoring.

2. Cloudflare AI Gateway

Platform Overview

Cloudflare AI Gateway runs on Cloudflare's global edge network and acts as a proxy between your application and AI providers. It supports 350+ models across 20+ providers including OpenAI, Anthropic, AWS Bedrock, Google AI Studio, and Azure OpenAI. Setup is a single endpoint change; no infrastructure to manage.

Key Features

• Response caching serves identical requests from Cloudflare's edge, reducing provider API calls by up to 90% and cutting latency significantly.
• Rate limiting and model fallbacks are configurable through a visual dashboard without code changes.
• Unified billing consolidates charges from multiple providers into a single Cloudflare invoice, with spend limits to prevent overruns.
• Dynamic routing supports A/B testing, geography-based routing, and user segment routing through a no-code interface.
• Data Loss Prevention (DLP) adds prompt-level security controls for sensitive workloads.
• Free tier covers core features: analytics, caching, and rate limiting.

Best For

Developers building serverless or edge-native applications on Cloudflare Workers who want observability and basic cost controls without standing up additional infrastructure. The free tier makes it easy to get started quickly.

3. LiteLLM

Platform Overview

LiteLLM is an open-source proxy and Python SDK that standardizes access to 100+ LLMs using the OpenAI API format. It is self-hostable via Docker and widely adopted in the AI engineering community for cost tracking and provider abstraction. The managed proxy (LiteLLM Proxy Server) adds a UI, budget dashboards, and team management on top of the core routing logic.

Key Features

• Per-key and per-team spend limits with configurable budget durations (daily, monthly, custom). Budget exhaustion automatically blocks further requests.
• Tag-based budgets allow cost isolation by project, department, or cost center using metadata tags on requests.
• Semantic caching via Redis reduces redundant provider calls for repeated or similar queries.
• Automatic fallbacks and retries with configurable retry counts and provider fallback chains.
• Built-in pricing calculator for estimating model costs before deployment based on expected token volumes.
• Cost tracking across 100+ models with a detailed model cost map, exportable as CSV or PDF.

Best For

Engineering teams that want complete control over their infrastructure and prefer open-source tooling. LiteLLM is the most flexible option for organizations running many providers with complex cost isolation requirements across teams.

4. Vercel AI SDK

Platform Overview

The Vercel AI SDK is a TypeScript-first toolkit for building AI-powered applications, primarily targeting Next.js and React environments. It abstracts provider-specific SDKs and adds streaming, React hooks, and basic multi-provider routing. It is not a standalone gateway in the traditional sense but is often used in that role by frontend teams.

Key Features

• Unified TypeScript API for OpenAI, Anthropic, Google, Mistral, and other major providers.
• Streaming support with built-in React hooks for real-time UIs.
• Provider switching and model routing at the application layer.
• Designed to work within Vercel's deployment platform and serverless function model.

Best For

Frontend and full-stack JavaScript developers building on Vercel. It is the natural choice for Next.js applications that need basic provider abstraction and streaming. Teams that need advanced cost controls, semantic caching, or enterprise budget management will outgrow it quickly.

5. Kong AI Gateway

Platform Overview

Kong AI Gateway extends Kong's enterprise API gateway with AI-specific plugins. It is designed for organizations already running Kong for API management who want to bring LLM traffic under the same governance layer without adopting a separate platform.

Key Features

• Semantic caching and AI-specific rate limiting via plugins that attach to existing Kong routes.
• AI prompt and response transformation at the proxy layer for standardizing inputs/outputs across providers.
• Load balancing across LLM providers with health checks and circuit breaking.
• Enterprise governance through Kong Konnect: audit logs, RBAC, and developer portals.
• Plugin ecosystem allows extending gateway behavior with custom logic.

Best For

Enterprise API teams that already run Kong infrastructure. If LLM traffic is one of many API concerns and unified governance is the priority, Kong is a natural extension rather than a new tool to manage.

Choosing the Right Gateway

The right choice depends on where your primary pain point sits:

• Cost ops with production observability - Bifrost provides the most complete solution, especially if you need budget controls, semantic caching, and quality monitoring in the same workflow.
• Serverless and edge deployments - Cloudflare AI Gateway is the lowest-friction option with a generous free tier and zero infrastructure overhead.
• Open-source and self-hosted - LiteLLM gives the widest provider coverage and the most granular cost isolation for teams that want to own their stack.
• Frontend-first teams on Next.js - Vercel AI SDK is the path of least resistance.
• Enterprises extending existing API governance - Kong is the right fit if Kong is already in the stack.

The Layer Above the Gateway: Quality in Production

Reducing LLM cost is one side of the equation. The other is knowing whether the responses your application is sending at lower cost are still high-quality. A cheaper model or a cache hit is only valuable if the output is reliable.

This is where a platform like Maxim AI becomes relevant. Beyond the gateway layer, Maxim provides end-to-end agent observability, production trace monitoring, and automated evaluations that run against live traffic. Teams can monitor cost trends alongside quality metrics like accuracy, hallucination rate, and task completion, catching regressions before they affect users.

For teams building on Bifrost, the integration is native: gateway cost data flows directly into Maxim's dashboards, giving a complete picture of spend and output quality in one place.

If you are at the stage of tightening production reliability alongside cost, see how Maxim works.

Related reading: LLM Observability: How to Monitor Large Language Models in Production | AI Reliability: How to Build Trustworthy AI Systems | Prompt Management in 202