Optimize AI
Automatically

Statistical optimization that reduces tokens, improves responses, and validates inputs automatically.

Start free • No credit card required • One line integration

See Deadpipe in Action

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deadpipe.com/dashboard
Deadpipe
Deadpipe
Healthy
2,847calls
98.2%success
$2.34today
142msavg
call_123checkout-agentgpt-4o-mini
124ms$0.0032s ago
Process order for 3 widgets
call_124support-botgpt-4
345ms$0.0125s ago
Help with refund request
call_125content-genclaude-3-haiku
89ms$0.00112s ago
Generate blog post about AI
call_126data-extractorgpt-4o-mini
67ms$0.00218s ago
Extract contact info from email
call_127checkout-agentgpt-4o-mini
156ms$0.00422s ago
Validate payment method

What Is Suboptimal AI Costing You?

Adjust the sliders to calculate your potential losses

Your usage estimates
AI prompts3
Calls/day250
Regressions/month1
Without monitoring
$195/mo revenue loss
$2.3k/year
With Deadpipe
$117 saved/mo
$1.4k saved/year
$150-$250/regression avg per month 73% go undetected for days
Start Free

Three Steps to Prompt Observability

1

Wrap Your Prompts

One line captures everything: latency, tokens, validation, and behavioral patterns.

2

Statistical Learning

AI learns optimal patterns from your successful calls: best prompts, token usage, and response quality.

3

Auto-Optimization

Get suggestions to reduce costs, improve accuracy, and catch issues before they impact users.

Zero-Config WrapperRecommended
With Schema Validation

Real Engineering Wins

Built by engineers, for engineers. No marketing fluff.

Universal wrap() Function

Auto-detects your provider (OpenAI, Anthropic, Gemini, Mistral, Cohere). All calls are automatically tracked with full context extraction.

example.py
💡 One wrap() call enables full observability—no config needed
from deadpipe import wrap
from openai import OpenAI
from anthropic import Anthropic
 
# Universal wrap() - wrap once with app context
openai = wrap(OpenAI(), app="my_app")
anthropic = wrap(Anthropic(), app="my_app")
 
# Pass prompt_id per call to identify each prompt
response = openai.chat.completions.create(
prompt_id="checkout_agent", model="gpt-4", messages=[...]
)
# → Context auto-extracted • Baselines auto-computed
40+
metrics per call
Auto
baseline detection
6
anomaly types
0
config required

Official SDKs

Universal wrap() auto-detects your provider. Zero-config. 40+ metrics per call.

OpenAI

GPT-4o, o1, o1-pro

Anthropic

Claude Opus 4, Sonnet 4

Google AI

Gemini 2.0, 1.5 Pro

Mistral

Large, Codestral

Cohere

Command R+

OpenAI

GPT-4o, o1, o1-pro

Anthropic

Claude Opus 4, Sonnet 4

Google AI

Gemini 2.0, 1.5 Pro

Mistral

Large, Codestral

Cohere

Command R+

Multi-Provider Supportv4.0
from deadpipe import wrap
from openai import OpenAI
from anthropic import Anthropic
# Universal wrap() auto-detects provider
openai = wrap(OpenAI(), app="my_app")
anthropic = wrap(Anthropic(), app="my_app")
# Pass prompt_id per call
openai.chat.completions.create(prompt_id="checkout", ...)

Simple, Transparent Pricing

No enterprise contracts. No hidden fees. Just monitoring that works.

Frequently Asked Questions

Everything you need to know about Deadpipe.

Deadpipe is laser-focused on optimization: we learn from your successful calls to automatically improve performance, reduce costs, and prevent issues. We build statistical models per prompt that suggest optimizations, validate inputs, and ensure consistent quality—all with one line of code.
Use our universal wrap() function: pip install deadpipe, then wrap(client, app="your_app") and pass prompt_id per call. Auto-detects your provider (OpenAI, Anthropic, Gemini, Mistral, Cohere). Tracks all calls with full context. Baselines build after ~10 calls.
Your LLM calls continue running normally. Deadpipe is fail-safe by design—if our servers are unreachable, your application doesn't fail. Telemetry is fire-and-forget, asynchronous, and never blocks your critical path. We never break your app.
The wrap() function extracts prompts, tools, and system messages from every call. Creates hashes for change detection—know exactly what changed when behavior shifts. Zero manual work.
After approximately 10 calls per prompt_id, we establish statistical baselines for latency (mean, p50, p95, p99), token counts, schema pass rate, empty output rate, and more. Each new call is compared against the baseline, and we alert when metrics exceed thresholds (e.g., latency > p95 × 1.5).
We can't detect hallucinations directly, but we track practical signals that something went wrong: JSON parse failures, schema validation errors, out-of-range enum values, out-of-bounds numeric fields, empty outputs, refusals, and output pattern changes. These proxies catch many real-world failures.
Yes! For streaming, call tracker.mark_first_token() when your first chunk arrives to capture time-to-first-token (TTFT). Then call tracker.record() with the final assembled response. We capture both TTFT and total latency automatically.
The SDKs support 5 major providers with the universal wrap() function: OpenAI (GPT-4o, o1, o1-pro), Anthropic (Claude Opus 4, Sonnet 4, Haiku 4), Google AI (Gemini 2.0, 1.5), Mistral (Large, Medium, Codestral), and Cohere (Command R+, Command R). Each has automatic provider detection, response parsing, and cost estimation. You can also use track() for any other provider.

Still have questions? Check the docs →

5
LLM providers
0
dependencies
<5min
setup
2
SDKs

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