Experiment tracking for ML teams

Ship ML faster.
Without the W&B bill.

Forge gives your team experiment tracking, model monitoring, and dataset versioning — at a price that doesn't require CFO approval.

Start free See pricing

Track every run. Compare every variant. Ship the model that actually works.

Features

Everything your team needs to ship ML with confidence

Experiment Tracking

Log metrics, params, and artifacts in 3 lines of code. No configuration required.

Model Monitoring

Watch training progress in real time. Alert on degradation before it becomes a fire.

Dataset Versioning

Tag, diff, and roll back datasets. Know exactly which data trained which model.

Fast Setup

Up and running in minutes. Python SDK, REST API, no infrastructure to manage.

Developer Experience

Clean API. Open source where it matters. No vendor lock-in.

Pricing

Pricing that doesn't need a meeting.

Free

$0/mo

3 seats
1,000 experiments/mo

Start free

Pro

$49/mo

10 seats
Unlimited experiments

Start Pro

Team

$149/mo

Unlimited seats
Priority support

Talk to us

	Forge	W&B	Comet
Pricing	Transparent, starts at $0	Opaque, enterprise-first	Opaque
Setup time	Minutes	Hours	Hours
Self-hosted option	Yes	Yes (enterprise)	Yes (enterprise)
Open source	Core SDK open	No	No

What you can do

→

Experiment Tracking. Log metrics, params, and artifacts in 3 lines of code. No configuration required.

→

Model Monitoring. Watch training progress in real time. Alert on degradation before it becomes a fire.

→

Dataset Versioning. Tag, diff, and roll back datasets. Know exactly which data trained which model.

→

Fast Setup. Up and running in minutes. Python SDK, REST API, no infrastructure to manage.

→

Developer Experience. Clean API. Open source where it matters. No vendor lock-in.

Developer Quickstart

Log your first Forge run in under a minute

Three lines of code. No configuration.

curl

curl -X POST https://forge.example.com/api/runs \
  -H 'Content-Type: application/json' \
  -d '{
    "model_name": "claude-opus-4-7",
    "scores": { "accuracy": 0.94 },
    "latency_ms": 412,
    "cost_usd": 0.0021,
    "quality_flags": {}
  }'

python

import requests

payload = {
    "model_name": "claude-opus-4-7",
    "scores": { "accuracy": 0.94 },
    "latency_ms": 412,
    "cost_usd": 0.0021,
    "quality_flags": {},
}

r = requests.post(
    "https://forge.example.com/api/runs",
    json=payload,
    timeout=10,
)
r.raise_for_status()
run = r.json()
print(run["id"], run["created_at"])

POST /api/runs responds 201 and echoes back the run's id and created_at so you can correlate it with downstream artefacts.

Ship ML faster.Without the W&B bill.