Caching.ai vs LiteLLM: Gateway Routing vs Cache Economics
LiteLLM unifies 100+ providers behind one API with response caching. Caching.ai maximizes the prompt-cache discount on the providers you already use. A fair comparison — including running both together.
Short version: LiteLLM standardizes how you call many providers; Caching.ai changes what those calls cost. One is plumbing for provider sprawl, the other is cache economics — and because both are proxies that speak native provider formats, they compose instead of compete. (Disclosure: Caching.ai is our product; LiteLLM is a project we respect. Corrections → support@caching.ai.)
What each tool is
LiteLLM is the de-facto open-source LLM gateway: a Python proxy (and SDK) that exposes 100+ providers behind one OpenAI-compatible API, with load balancing, fallbacks, retries, spend tracking, budgets and virtual keys per team. Its caching is response caching — Redis-backed exact-match (with an optional semantic mode) that replays stored answers for repeated requests.
Caching.ai is a drop-in proxy for Anthropic, OpenAI, Gemini and Grok focused on the provider-side prompt cache: real hit-rate and wasted-spend analytics, automatic cache_control injection, cache-breaker detection, warming through idle gaps with an economic guard, and TTL auto-tuning per key. On a public benchmark of 10,000+ billed calls it saved 67% on sparse support traffic and 89% on a shared-prefix batch; on GPT-5.6 it restored prefix hit rates from 0% (SDK defaults) to 97.8%+.
| Caching.ai | LiteLLM | |
|---|---|---|
| Category | Prompt-cache cost optimizer | Multi-provider gateway |
| Providers | 4, deeply (Anthropic, OpenAI, Gemini, Grok) | 100+, uniformly |
| API shape | Native provider formats, unchanged | OpenAI format for everything |
| Caching | Provider prefix-cache optimization (inject, warm, tune, measure) | Response caching (Redis; semantic optional) |
| Routing / fallbacks / budgets | No | Yes — core product |
| Cache savings accounting | Verified $ saved / $ wasted, net of warming costs | Spend tracking (what you spent, not what you wasted) |
| Ops | Managed cloud, or Apache-2.0 self-host | Self-host (OSS) or enterprise |
| Pricing | 20% of verified savings; <$5/mo waived | OSS free; enterprise licensing |
Choose LiteLLM if…
- You call many providers and want one interface, one key system, one budget layer.
- You need routing, fallbacks and rate limits across models today.
- You're happy operating your own gateway (it's your infrastructure).
Choose Caching.ai if…
- You mostly call the big four providers and your bill is prefix-heavy (agents, chatbots, RAG with fat system prompts).
- You want cache misses found and fixed automatically — not another config surface.
- You want savings verified against provider-reported usage, with warming costs subtracted, before anyone charges you anything.
Or chain them
Keep LiteLLM as the app-facing gateway; set its api_base for Anthropic/OpenAI routes to the Caching.ai proxy. Routing, budgets and virtual keys stay where they are; the cache layer starts earning its keep on everything that flows through. Wider context: Top 7 LLM caching tools in 2026 and What is prompt caching?
Frequently asked questions
Is Caching.ai an alternative to LiteLLM?
Only partially. LiteLLM's core job is unifying 100+ providers behind one OpenAI-format API with routing, budgets and virtual keys. Caching.ai's core job is maximizing the provider-side prompt-cache discount. If your pain is multi-provider sprawl, use LiteLLM; if it's a cache-shaped bill, use Caching.ai; if both, chain them.
Can LiteLLM and Caching.ai run together?
Yes. LiteLLM lets you set a custom api_base per provider, so you keep LiteLLM as your app-facing gateway and point its Anthropic/OpenAI routes at the Caching.ai proxy. LiteLLM keeps handling routing and budgets while Caching.ai optimizes the cache on the traffic that flows through.
Does LiteLLM support prompt caching?
LiteLLM forwards provider caching parameters like cache_control if your code sets them, and its own caching feature is response caching (Redis-backed, with an optional semantic mode). It does not automatically inject breakpoints, keep provider caches warm through idle gaps, or auto-tune TTLs — that active prefix-cache management is what Caching.ai adds.