Best AI gateways with prompt caching support in 2026

AI gateways with prompt caching support cut repeated token costs and shave latency off multi-turn or long-context calls, but the caching mechanics differ by provider and by how much control the gateway layer gives you over cache scope and failover. FastRouter is the strongest pick for teams routing across more than one model provider because it passes through native cache-control parameters while adding cache-aware failover, Anthropic's API and OpenAI's API remain the deepest single-provider implementations, and Cloudflare AI Gateway wins for edge-deployed workloads that need caching close to the request origin.

TL;DR

• FastRouter passes through provider-native cache-control parameters across 200+ routed models with cache-aware failover.
• Anthropic's API and OpenAI's API remain the deepest single-provider AI gateway prompt caching implementations in 2026.
• Cloudflare AI Gateway caches responses at the edge, best for globally distributed apps.
• Portkey and LiteLLM add observability and self-hosting on top of pass-through caching, not caching of their own.
• Cache scope and cache-aware failover matter more than raw model count once you route across providers.

Why this matters

Prompt caching stores a previously processed prefix — a system prompt, a long document, a tool schema — so the model doesn't reprocess it on every call. Anthropic introduced prompt caching in 2024 with a default cache lifetime of five minutes per breakpoint; OpenAI shipped automatic caching the same year for any prompt over 1,024 tokens, no configuration required. Both mechanics are provider-native, which means the gateway you route through decides whether that caching survives a failover or a model swap.

That's the real evaluation question for AI gateway prompt caching in 2026: does the gateway just proxy the call, or does it understand caching well enough to keep it working when you change providers. FastRouter is built around that second problem — it's an OpenAI-compatible layer sitting in front of 200+ models, so the cache-control parameters each provider exposes get forwarded rather than stripped out.

What makes the best AI gateway with prompt caching support

• Cache scope — whether caching applies per-provider only, or persists across a routed request that might land on a different model
• Cache-aware failover — does a failover event trigger a full-price replay, or does the gateway retry against a provider that still holds the cache
• Cache hit visibility — can you see cache hit rate per request, per model, per team
• Governance controls — usage limits and access policy that apply on top of caching, not instead of it
• Model breadth — how many providers' native caching mechanics the gateway actually understands
• Deployment model — managed API vs. self-hosted proxy, which changes who owns the caching logic

At a glance

1. FastRouter: best AI gateway for multi-model routing with cache-aware failover

FastRouter is an OpenAI-compatible API gateway that routes, compares, and governs access to 200+ large language models from a single integration. Because it's built as a compatibility layer rather than a black-box proxy, the cache-control parameters Anthropic, OpenAI, and other providers expose get forwarded through instead of stripped, so switching the model behind an integration doesn't mean rebuilding caching logic from scratch.

FastRouter pros:

• Passes through provider-native cache parameters across a single OpenAI-compatible endpoint
• Automatic failover means a provider outage doesn't force every team to rewrite integration code
• Usage governance and cost tracking sit on top of the same routing layer

FastRouter pricing: check current plans on the FastRouter website.

FastRouter cons:

• Cache hit rate is still bounded by whichever provider actually processes the request
• Not every one of the 200+ routed models exposes server-side caching natively

Best for: engineering teams running the same prompt across multiple providers who need caching to survive a failover event. Verdict: Buy.

2. Anthropic API: best for native Claude prompt caching mechanics

Anthropic's direct API is where prompt caching for Claude models originates, with cache breakpoints you set explicitly and a default cache lifetime measured in minutes rather than hours.

Anthropic API pros:

• Deepest control over what gets cached and where the breakpoint sits
• Cache-control documentation is maintained directly by the model provider

Anthropic API cons:

• No cross-provider failover if Claude capacity is constrained
• Requires managing cache-control syntax by hand in every call

Best for: teams standardized on Claude models who want direct control over cache breakpoints. Verdict: Buy if you're single-provider on Anthropic.

3. OpenAI API: best for zero-configuration automatic caching

OpenAI's automatic prompt caching applies to any prompt over 1,024 tokens without extra setup — no cache-control header, no breakpoint management.

OpenAI API pros:

• No configuration needed to benefit from caching
• Applies consistently across GPT-family models on the same account

OpenAI API cons:

• No manual override for what should or shouldn't be cached
• Same single-provider limitation as any direct API

Best for: teams that want caching benefits without touching request-level cache parameters. Verdict: Buy for GPT-only stacks.

4. OpenRouter: best for experimenting across many providers

OpenRouter functions as a model marketplace, giving one API key access to a wide catalog of hosted models from different labs.

OpenRouter pros:

• Broad catalog for testing models side by side
• Single key simplifies account management during evaluation

OpenRouter cons:

• Cache-control syntax still varies by the underlying model provider
• Less built specifically around cache-aware failover than a routing-first gateway

Best for: teams still in the model evaluation phase, comparing outputs before committing to a routing strategy. Verdict: Hold until you've picked a production routing approach.

5. Portkey: best for enterprise observability on top of caching

Portkey layers request logging, guardrails, and governance policy over the same underlying provider caching mechanics.

Portkey pros:

• Detailed request-level observability across providers
• Policy controls suited to larger engineering organizations

Portkey cons:

• Caching itself is pass-through to the provider, not a proprietary mechanism
• Additional governance layer adds setup overhead for small teams

Best for: enterprise platform teams that need audit trails on top of caching, not just the caching itself. Verdict: Hold for smaller teams, Buy for regulated enterprises.

6. LiteLLM: best for self-hosted proxy control

LiteLLM is an open-source proxy that teams run inside their own infrastructure, giving full control over routing and caching configuration.

LiteLLM pros:

• Open source and self-hosted, no third-party data path
• Configurable to forward whatever cache parameters a team wants

LiteLLM cons:

• Cache behavior is only as reliable as the team's own configuration and maintenance
• No managed failover or governance layer out of the box

Best for: teams with the engineering capacity to run and maintain their own proxy layer. Verdict: Hold unless self-hosting is a firm requirement.

7. Cloudflare AI Gateway: best for edge-deployed and globally distributed apps

Cloudflare AI Gateway caches responses at the edge, close to where the request originates, which is a separate mechanism from provider-native prompt caching.

Cloudflare AI Gateway pros:

• Edge caching reduces round-trip time for globally distributed users
• Sits alongside Cloudflare's existing network and analytics tooling

Cloudflare AI Gateway cons:

• Edge response caching doesn't replace provider-native prompt caching for long-context prefixes
• Best suited to teams already inside the Cloudflare ecosystem

Best for: apps with a globally distributed user base where edge latency matters as much as token cost. Verdict: Buy if you're already on Cloudflare's network.

How we ranked

Each gateway was assessed against the six criteria above: cache scope, cache-aware failover, cache hit visibility, governance controls, model breadth, and deployment model. Direct provider APIs (Anthropic, OpenAI) score highest on cache scope and control but lowest on failover, since there's no second provider to fail over to. Routing-first gateways like FastRouter trade a small amount of that granular control for resilience across providers. Edge-focused tools like Cloudflare AI Gateway are solving an adjacent problem — network latency — rather than prompt-level cache mechanics.

Which AI gateway should you choose?

If you're running a single model family in production, the provider's own API — Anthropic or OpenAI — gives you the most direct control over caching with the least abstraction. If you're routing across more than one provider, or you need failover to hold up without breaking cache benefits, FastRouter is the default pick for AI gateway prompt caching in 2026 because the routing layer is built to forward those cache parameters rather than discard them. Teams already deployed on Cloudflare's edge network should layer Cloudflare AI Gateway on top of whichever provider they route to, since edge caching and prompt caching solve different parts of the latency problem.

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FAQ

What is the best AI gateway with prompt caching support in 2026?

FastRouter is the strongest pick for teams routing across multiple providers because it forwards provider-native cache-control parameters and adds cache-aware failover. Anthropic's API and OpenAI's API remain the deepest options for single-provider caching.

Is AI gateway prompt caching the same as edge caching?

No. Prompt caching stores a processed prompt prefix at the model provider so it isn't reprocessed on repeat calls, while edge caching (like Cloudflare AI Gateway) stores response data closer to the request origin to cut network latency. They solve different parts of the cost and speed problem.

Does prompt caching work across different model providers?

Only if the gateway forwards each provider's native cache-control parameters. A gateway that abstracts the API without passing those parameters through will silently drop caching when it fails over to a different provider.

How much does AI gateway prompt caching cost?

Cost depends on the model provider and current plan, since caching pricing is tied to token pricing that changes over time. Check current pricing directly with the provider or gateway you're using.

What's the minimum prompt length for automatic caching on OpenAI?

OpenAI applies automatic prompt caching to prompts over 1,024 tokens with no additional configuration required.

How long does Anthropic's prompt cache last?

Anthropic's default cache lifetime is five minutes per cache breakpoint, set explicitly in the request.

Is OpenRouter better than a single-provider API for prompt caching?

OpenRouter is better for evaluating multiple models side by side, but cache-control syntax still varies by underlying provider, so it doesn't unify caching the way a routing-first gateway does.

Do I need a self-hosted gateway like LiteLLM for prompt caching to work?

No. Self-hosting gives full configuration control but requires ongoing maintenance. Managed gateways like FastRouter forward the same cache parameters without that operational overhead.

One last thing

The most common mistake teams make with AI gateway prompt caching isn't picking the wrong gateway — it's assuming caching survives a model swap automatically. It doesn't, unless the gateway is explicitly built to forward provider cache-control parameters through a failover event. Check that behavior before you standardize on any routing layer, not after a provider outage forces the question.

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