vetch 0.1.3

Planet-aware observability for LLM inference

Vetch SDK

Planet-aware observability for LLM inference.

Vetch is a Python SDK that wraps LLM API calls to log energy consumption, cost, and carbon per inference using live grid data. It never reads prompt or completion content—only metadata from the response usage.

Features

• Fail-Open: LLM calls always proceed even if Vetch fails.
• Privacy-First: No prompt or completion data is ever read or buffered.
• Multi-tier Caching: Memory and file-based caching for grid intensity data.
• Observability-Transparent: Works seamlessly with Datadog, OpenTelemetry, and Sentry.
• Low Overhead: Under 5ms overhead for sync calls; zero TTFT latency for streaming.

Installation

pip install vetch

Quick Start

from vetch import wrap
from openai import OpenAI

client = OpenAI()

with wrap(region="us-east-1", tags={"team": "ml", "env": "prod"}) as ctx:
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "Hello world"}]
    )

# Access inference metadata
print(f"Energy: {ctx.event['estimated_energy_wh']} Wh")
print(f"Carbon: {ctx.event['estimated_carbon_g']} gCO2e")

CLI Usage

Estimate energy/carbon for a model without running code:

vetch estimate --model gpt-4o --input-tokens 1000 --output-tokens 500 --region us-east-1

Compare multiple models:

vetch compare --models gpt-4o,claude-3-opus,gemini-1.5-pro --tokens 1000

Analyze your token usage patterns:

vetch audit

Check your environment:

vetch check

Token Waste Audit

Vetch tracks token usage patterns across your session and provides actionable recommendations:

from vetch import wrap, get_session_stats, generate_advisories

# Make multiple LLM calls
for _ in range(10):
    with wrap() as ctx:
        response = client.chat.completions.create(...)

# Analyze patterns
stats = get_session_stats()
advisories = generate_advisories(stats)

for a in advisories:
    print(f"[{a.level.value}] {a.title}")
    print(f"  {a.description}")

What it detects:

• Static system prompts: Repeated input token counts suggest cacheable prompts
• High input:output ratios: Large inputs producing small outputs
• Expensive model usage: Opportunities to use smaller, cheaper models

GPU Calibration (Local Inference)

For local inference (Ollama, vLLM, llama.cpp), calibrate energy measurements using actual GPU power draw:

from vetch.calibrate import calibrate_model, format_calibration_result

def my_inference():
    # Run your inference workload
    # Return (input_tokens, output_tokens)
    response = ollama.generate(model="llama3.1:8b", prompt="Hello world")
    return 100, 50  # Your actual token counts

result = calibrate_model("ollama", "llama3.1:8b", workload=my_inference)
print(format_calibration_result(result))

# Use calibrated values for accurate tracking
with wrap(energy_override=result.to_override()) as ctx:
    response = ollama.generate(...)

Check calibration status:

vetch calibrate --status

Requirements: NVIDIA GPU with pynvml (pip install nvidia-ml-py3)

Historical Analysis & Reporting

Vetch can persist events to SQLite for historical FinOps analysis:

from vetch import configure_storage, query_usage, wrap
from datetime import datetime, timedelta

# Enable persistent storage
configure_storage()  # Uses ~/.vetch/usage.db

# Your LLM calls are now tracked
with wrap(tags={"team": "ml", "feature": "chat"}) as ctx:
    response = client.chat.completions.create(...)

# Query historical usage
summary = query_usage(
    start=datetime.now() - timedelta(days=7),
    tags={"team": "ml"}
)

print(f"Total cost: ${summary.total_cost_usd:.2f}")
print(f"Total energy: {summary.total_energy_wh:.2f} Wh")
print(f"Requests: {summary.total_requests}")

Generate reports from CLI:

# Weekly report
vetch report --days 7

# Filter by team
vetch report --tags team=ml

# Show top consumers
vetch report --top --top-by team --days 30

# JSON output for dashboards
vetch report --format json

Energy Tiers

Vetch uses a tiered system for energy estimate confidence:

Tier	Name	Uncertainty	Source
0	Measured	±10-20%	Direct GPU measurement (pynvml)
1	Vendor-Published	±20-50%	Official provider data
2	Validated	±50-100%	Crowdsourced aggregates
3	Estimated	order of magnitude	Parameter-based calculation

Run vetch methodology to see full methodology documentation.

Environment Variables

Variable	Description
VETCH_DISABLED	Set to true to completely disable Vetch (emergency kill switch)
VETCH_REGION	Default grid region (e.g., us-east-1, eu-west-1)
VETCH_OUTPUT	Output target: stderr (default), none, or file path
ELECTRICITY_MAPS_API_KEY	API key for live grid carbon intensity data
VETCH_CACHE_MODE	Set to memory-only for serverless/Lambda environments

Alpha Limitations

This is an alpha release. Please be aware of:

1. Energy estimates are uncertain: Most models use Tier 3 estimates (±10x uncertainty). See vetch methodology for details.

2. Region inference is approximate: Without explicit VETCH_REGION, timezone-based inference is ~30% accurate. Set the region explicitly for accurate carbon calculations.

3. Experimental modules: vetch.calibrate, vetch.storage, and vetch.ci emit FutureWarning and may change in future versions.

4. Provider support: Currently supports OpenAI, Anthropic, and Vertex AI. Other providers coming soon.

Troubleshooting

Vetch is blocking my LLM calls:

export VETCH_DISABLED=true  # Emergency kill switch

Too much output:

export VETCH_OUTPUT=none  # Silence all output

Need to debug:

import logging
logging.getLogger("vetch").setLevel(logging.DEBUG)

License

Apache License 2.0. See LICENSE and NOTICE for details.