Measured 57% cost reduction on 400 real prompts

Control cost, routing, and reliability for every AI request

ClawPipe is a lightweight request layer that sits between your application and LLM providers. It caches, routes, compresses, and governs every call automatically.

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
import { ClawPipe } from 'clawpipe-ai';
const pipe = new ClawPipe({ apiKey });

const { text } = await pipe.prompt(messages);
// cached, routed, compressed
// or skipped entirely

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, conversions resolve in <1ms with no LLM call.

02

Compress context

Strip redundancy and boilerplate. Cut 20-60% of tokens before they're sent.

03

Check cache

Hash and embedding match. Similar prompts return cached responses instantly.

04

Route to best model

Pick the cheapest provider/model that meets quality requirements for this specific request.

05

Execute and learn

Call the provider, track the outcome, and refine routing weights for next time.

Measured results

Real cost savings. Real numbers.

400 prompts across four workload categories. Public benchmark data, reproducible scripts.

57%
Average cost reduction
30%
Requests skipped entirely
35%
Cache hit rate
<1ms
Pipeline overhead

Source: benchmarks/results/summary.json · Try the live playground · Estimate your savings

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.

Feature comparison
CapabilityClawPipeLiteLLMDirect APIDIY middleware
Deterministic resolution (skip LLM)YesNoNoManual
Semantic cachingYesHash onlyNoManual
Context compressionYesNoNoManual
Self-learning routingYesNoNoManual
Multi-provider failoverYesYesNoManual
Per-project analyticsYesPartialNoManual
SDK-local (no proxy hop)YesProxyYesYes
Offline / local model supportYesNoNoManual

Pricing

Start free. Scale when ready.

Every plan includes the full pipeline. No feature gating.

Free

$0

For evaluation and side projects

  • 1,000 calls/day
  • All pipeline stages
  • 1 project
  • Community support
Get started

Team

$149/mo

For teams shipping AI features together

  • 1,000,000 calls/day
  • Team management
  • SLA guarantee
  • Priority support
Contact us

Enterprise

Custom

For organizations with compliance needs

  • Unlimited calls
  • SSO + audit logs
  • Dedicated infrastructure
  • 24/7 support + SLA
Talk to sales
If ClawPipe reduces even 20% of your LLM spend, Pro pays for itself at $250/month in provider costs. Estimate your savings.

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.

Start controlling your AI costs today

Free tier includes 1,000 calls/day and the full pipeline. No credit card. Set up takes about 60 seconds.