Skip unnecessary LLM calls. Explain the ones you still pay for.
ClawPipe is an LLM FinOps layer for production AI teams. Booster avoids provider calls for deterministic work. Cost Analyst traces every remaining dollar to the request, feature, model, route, customer, and deploy that caused it.
Prior in-house synthetic run: 57.3% costSavingsPercent (n=400 prompts, mock gateway, benchmarks/results/summary.json) — not a production claim. The pre-registered measured benchmark replaces it; see methodology v1.0 (locked 2026-05-18) at github.com/finsavvyai/clawpipe-booster-benchmark.
The problem
Your AI bill jumped. Nobody can explain why.
Provider dashboards show tokens by model. They cannot tell you which feature, customer, route, or deploy caused this week's spike — and they certainly do not skip the deterministic calls you should never have paid for.
Deterministic calls paid in full
Tool retries, JSON normalization, repeat prompts, regex-answerable lookups — all routed through a frontier model.
No request-level attribution
Provider invoices stop at the model. There is no join from a billed dollar to a feature, customer, deploy, or developer.
Bill spikes without evidence
Finance asks why spend grew 40%. The on-call answer is a guess about a recent deploy and a screenshot of token graphs.
Provider caching saves tokens
It still bills you for the request. The actual savings come from never sending the call in the first place.
Booster
The deterministic skip layer
Booster runs in your SDK, before the network hop. If a regex, JSON validator, or repeat-prompt hash can answer the request, the LLM never sees it. Open source under MIT (@clawpipe/booster). Works with raw OpenAI / Anthropic SDKs, LiteLLM, Portkey, OpenRouter, Cloudflare AI Gateway, Vercel AI Gateway, and custom gateways.
Regex-answerable
Dates, conversions, format normalization, canonical lookups. Resolved in <1ms, no provider call.
JSON / schema normalization
Validate, repair, reshape structured output deterministically. Common in agent tool-use loops.
Repeat-prompt hash
Identical prompt+model+params hits return the cached response. Different from provider prompt caching — Booster does not send the call.
Pre-registered benchmark · agent / chat / extraction buckets
Methodology v1.0 (locked 2026-05-18) compares Booster against raw provider calls, provider prompt caching, and standard gateway caching across three workload buckets. We publish all bucket results — no blending into a flattering average. Decision rule: 25%+ incremental savings on agent workloads commits us to the agent-infra path; under 10% kills the standalone gateway pitch.
Methodology, decision rule, raw data, and reproduction scripts: github.com/finsavvyai/clawpipe-booster-benchmark. The earlier in-house number (57.3% costSavingsPercent, n=400 synthetic, mock gateway, benchmarks/results/summary.json) is preserved in-repo for transparency and is not cited as a production claim.
Cost Analyst · Read-only beta
Explains every dollar that remains
Plain-English questions, request IDs and SQL evidence in return. Read-only beta at chat.clawpipe.ai. No write tools, no Apply button, no provider-switch automation — just three jobs, answered with locked deterministic facts.
How much did Booster save?
Skip count, top saving rules, dollar amount — week-over-week, per project. Sourced from the requests + savings tables, not a heuristic.
Why did my bill jump?
Identifies the endpoint, the route change, the deploy SHA, and the request volume responsible for a spend delta — with the SQL that proves it.
Which feature costs most per user?
Cost-per-active-user by feature, top customers by GPT-4o spend, model mix by endpoint. Every dollar traced to its cause.
Iframe load may fail if chat.clawpipe.ai sets a strict frame-ancestors CSP. Open in a new tab if so.
Gateway
21 providers, one switch
Provider count is parity, not a differentiator. The gateway is what makes the FinOps layer possible — every call, route, model, deploy, and customer becomes structured telemetry the Cost Analyst can query.
Plus Together AI, Fireworks AI, Perplexity, xAI, Cohere, AI21, Cerebras, Replicate, Hugging Face, Writer, Databricks, Azure OpenAI, and any OpenAI-compatible endpoint (llamafile, LM Studio, vLLM, TGI). Provider key BYOK; rotation and rate-limit handling per project. Full list: docs.clawpipe.ai/providers.
Integration
Replace one import. Keep your code.
import OpenAI from 'openai';
const client = new OpenAI();
const res = await client.chat.completions
.create({
model: 'gpt-4o',
messages,
});
// full-price, every time
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
Skip if deterministic
Math, dates, JSON, conversions resolve in <1ms with no LLM call.
Compress context
Strip redundancy and boilerplate. Token-reduction per request is reported in telemetry — no headline % claim until measured.
Check cache
Hash and embedding match. Similar prompts return cached responses instantly.
Route to best model
Pick the cheapest provider/model that meets quality requirements for this specific request.
Execute and learn
Call the provider, track the outcome, and 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
Provider gateways move traffic between providers. ClawPipe also skips deterministic calls and attributes the remaining spend at the request level. The difference is FinOps, not just dispatch. Comparison reflects each tool's documented out-of-box behavior as of 2026-05; verify with each project's docs before procurement.
| Capability | ClawPipe | LiteLLM | Direct API | DIY middleware |
|---|---|---|---|---|
| Deterministic skip (no provider call) | Built-in (Booster) | Not in core | Not provided | Build yourself |
| Request-level cost attribution | Built-in (Cost Analyst) | Not in core | Not provided | Build yourself |
| Bill-spike explanation (J2) | Built-in | Not in core | Not provided | Build yourself |
| Semantic caching | Built-in | Hash-key cache only | Not provided | Build yourself |
| Multi-provider failover | Built-in | Built-in | Not applicable | Build yourself |
| Per-project analytics | Built-in | Built-in | Provider-only | Build yourself |
| SDK-local (no proxy hop) | Yes | Proxy required | Yes | Depends |
| Offline / local model support | Built-in | Not in core | Not applicable | Build yourself |
ROI Calculator
How much will you save?
Conservative estimates. Based on real pipeline performance.
Pricing
Start free. Scale when ready.
Every plan includes the full pipeline. No feature gating.
- All pipeline stages
- 1 project
- SDK + gateway access
- Community support
- Unlimited projects
- Analytics dashboard
- Router weight learning
- Email support
- SLA guarantee
- Team management
- Dedicated routing
- Slack support
- SSO + audit logs
- Dedicated infra
- Custom SLA
- 24/7 support
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
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.Skip the calls that do not need a model. Explain the calls that still cost money.
Reproduce the Booster benchmark on your own traffic. Try the Cost Analyst read-only beta. No credit card.