Measured benchmark in progress · Open source, MIT

The only AI gateway that skips LLM calls.

ClawPipe's Booster resolves greetings, math, dates, JSON, and canonical lookups deterministically, inside the SDK, before the network hop. Everything else flows through a self-learning router across 21 providers, with caching and context packing on the way out.

Public benchmark in progress → Methodology v1.0 locked 2026-05-18.

OpenAI Anthropic Google Gemini Groq DeepSeek Mistral Ollama

The problem

Most AI apps waste 30-50% of their LLM spend

Not because models are expensive. Because every request gets handled the same way: full-price, uncached, to the most capable model available.

Duplicate prompts

Semantically identical requests hit the provider every time. No caching, no deduplication.

Overkill models

GPT-4 answering date conversions and simple lookups. The wrong model for the job, every time.

Wasted tokens

Bloated system prompts, repeated context, uncompressed history inflating every request.

No visibility

No per-project cost breakdown, no routing analytics, no way to know where the money goes.

The platform

One request layer. Three outcomes.

Replace your provider client with pipe.prompt(). ClawPipe handles everything between your app and the LLM.

Cost control

  • Semantic caching deduplicates similar prompts
  • Context compression cuts tokens 20-60%
  • Deterministic resolution skips the LLM entirely
  • Smart routing picks the cheapest viable model

Reliability

  • Multi-provider failover with circuit breakers
  • Automatic retries with exponential backoff
  • Per-project rate limits and budget caps
  • Local fallback via Ollama and llamafile

Governance

  • Provider abstraction across 7+ services
  • Per-request tracing and analytics
  • Team-wide routing policies
  • Self-learning model selection

Integration

Replace one import. Keep your code.

Before
import OpenAI from 'openai';
const client = new OpenAI();

const res = await client.chat.completions
  .create({
    model: 'gpt-4o',
    messages,
  });
// full-price, every time
After: OpenAI drop-in
import OpenAI from 'openai';
const client = new OpenAI({
  baseURL: 'https://api.clawpipe.ai/v1',
  apiKey: process.env.CLAWPIPE_API_KEY,
  defaultHeaders: {
    'X-Project-Id': process.env.CLAWPIPE_PROJECT_ID,
  },
});

const res = await client.chat.completions.create({
  model: 'gpt-4o',
  messages,
});
// booster / cache / router run on every request

Or use our SDK for finer-grained control: import { ClawPipe } from 'clawpipe-ai'.

Available for TypeScript, Python, and Go. Or use the REST API from any language.

How it works

What happens to every request

01

Skip if deterministic

Math, dates, JSON, canonical lookups, conversions: resolved in under 1ms by 246 regex rules. Zero LLM call. This is the wedge; every other stage is support.

  1. 02 PACKStrip redundancy and boilerplate. Cut 20-60% of tokens before they leave the SDK.
  2. 03 CACHEHash plus embedding match. Similar prompts return cached responses instantly.
  3. 04 ROUTEPick the cheapest provider and model that meets quality requirements for this request.
  4. 05 CALL & LEARNDispatch to one of 21 providers. Track outcome. Refine routing weights for next time.

Public benchmark in progress

Numbers coming. Methodology already open.

Pre-registered methodology v1.0 published before any results. 4 baselines (raw, provider prompt caching, Cloudflare AI Gateway, ClawPipe) across 3 workload buckets (agent / chat / extraction). 95% Wilson confidence intervals on the headline metric. Public comment window closed 2026-05-18 (methodology locked).

Prior synthetic in-house run on a 200-prompt dataset (2 passes, mocked gateway) is preserved for transparency at benchmarks/; we are not citing its numbers on this site until the measured run lands.

Read methodology v1.0 · Leave a comment · Try the playground

Use cases

Built for production AI workloads

AI SaaS products

Control per-customer LLM costs without changing product UX. Budget caps, routing policies, and usage analytics per project.

Agents and copilots

Route simple tool calls to cheap models, complex reasoning to frontier models. The router learns your traffic pattern.

RAG systems

Compress retrieved context before it hits the LLM. Cache repeated queries. Fall back across providers if one is down.

Chat applications

Cache common conversation turns. Route trivial responses away from expensive models. Reduce cost per conversation.

Multi-tenant platforms

Isolate cost and routing per tenant. Enforce different model policies per customer tier. One integration, many projects.

Internal tools

Give your team AI features without unpredictable provider bills. Set daily caps, preferred models, and fallback chains.

Comparison

How ClawPipe compares

Routers move traffic between providers. ClawPipe also skips, caches, compresses, and learns from every request. The difference is cost optimization, not just request dispatch. Comparison reflects each tool's documented out-of-box behavior as of 2026-05; verify with each project's docs before procurement.

Feature comparison: out-of-box capabilities, May 2026
CapabilityClawPipeLiteLLMDirect APIDIY middleware
Deterministic resolution (skip LLM)Built-inNot in coreNot providedBuild yourself
Semantic cachingBuilt-inHash-key cache onlyNot providedBuild yourself
Context compressionBuilt-inNot in coreNot providedBuild yourself
Self-learning routingBuilt-inStatic rulesNot applicableBuild yourself
Multi-provider failoverBuilt-inBuilt-inNot applicableBuild yourself
Per-project analyticsBuilt-inBuilt-inProvider-onlyBuild yourself
SDK-local (no proxy hop)YesProxy requiredYesDepends
Offline / local model supportBuilt-inNot in coreNot applicableBuild yourself

ROI Calculator

How much will you save?

Conservative estimates. Based on real pipeline performance.

Provider mix
OpenAI60%
Anthropic30%
Other10%
Estimated monthly savings
$0
/mo (0%)
Agent Booster$0
Semantic Cache$0
Smart Routing$0
See my recommended plan →

Pricing

Start free. Scale when ready.

Every plan includes the full pipeline. No feature gating.

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$0 $0 /mo
1,000 calls/day
  • All pipeline stages
  • 1 project
  • SDK + gateway access
  • Community support
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Dev
Spend $300/mo on LLMs → save $45–150/mo.
$79 $63 /mo
15K calls/day
  • Unlimited projects
  • Analytics dashboard
  • Router weight learning
  • Email support
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Scale
Spend $5K/mo → save $1,500–$5,000/mo.
$799 $639 /mo
1.5M calls/day
  • SLA guarantee
  • Team management
  • Dedicated routing
  • Slack support
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Enterprise
$20K+ LLM spend → dedicated ROI analysis.
$2,500 Custom /mo+
Unlimited
  • SSO + audit logs
  • Dedicated infra
  • Custom SLA
  • 24/7 support
Talk to sales

Security and reliability

Built for production infrastructure

ClawPipe handles sensitive request flows. We designed it for teams that can't afford surprises in their AI stack.

Read the security page
  • KeysSHA-256 hashed. Plaintext shown once.
  • PromptsNever logged or stored. Hash-only for cache.
  • Provider keysEncrypted at rest in Cloudflare KV.
  • IsolationPer-invocation V8 context. No shared state.
  • Local modeSDK + Ollama = data never leaves your machine.

Frequently asked questions

Do you store my prompts?
No. Prompt content is never logged or stored. ClawPipe uses SHA-256 hashes for cache lookup and records only metadata (token counts, latency, cost, provider, model). Your prompts stay in your process for SDK-local stages.
Which providers are supported?
OpenAI, Anthropic, Google Gemini, Groq, DeepSeek, Mistral, Together AI, Fireworks AI, and any OpenAI-compatible endpoint. Local models supported via Ollama, llamafile, and LM Studio.
Does ClawPipe add latency?
No. The SDK runs in your process with under 1ms overhead. Boosted and cached responses are faster than direct provider calls. The gateway adds no extra network hop for SDK-local stages.
Can it run fully offline?
Yes. Point the SDK at a local Ollama or llamafile instance. Booster, Packer, and Cache stages run entirely in-process. Your data never leaves your machine.
How hard is migration?
One import change. ClawPipe's pipe.prompt() replaces your provider client call. The response shape is compatible. Or use the OpenAI drop-in replacement interface to keep your existing code entirely unchanged.
How is this different from LiteLLM?
LiteLLM is a proxy server that routes requests between providers. ClawPipe is an SDK that also caches, compresses, resolves deterministically, and learns optimal routing. SDK-local means no extra network hop, no proxy to maintain, and no prompts transiting a third-party server.
Is this only for developers?
ClawPipe is a developer tool, yes. It integrates via npm/pip/go package and a REST API. Non-developers can use the dashboard to monitor usage and costs, but integration requires engineering work.
Does ClawPipe work as a drop-in OpenAI proxy?
Yes. Point OpenAI's SDK at https://api.clawpipe.ai/v1 and we run booster / cache / router / provider on every request, returning the standard OpenAI response shape. Streaming SSE is supported. No code changes beyond setting baseURL and adding your X-Project-Id header.

Start controlling your AI costs today

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