Now in private beta

Stop fighting infrastructure.
Start training models.

The end-to-end platform for fine-tuning LLMs. Data curation, multi-cloud GPU orchestration, experiment tracking, and evaluation — one CLI, one system of record.

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Join 400+ ML engineers on the waitlist

~/project — epoch

40–60%

of ML engineering time wasted on infra

5–8

tools duct-taped together per team

20–40%

of GPU spend wasted on failed runs

$100B+

annual AI training compute market

The Problem

Training LLMs is the most important
work in tech. The tooling is broken.

Every ML team duct-tapes together cloud consoles, bash scripts, scattered dashboards, and manual checkpointing. It's the pre-AWS era for model training.

⚡
40–60%

Wasted Engineering Time

ML engineers spend more time on infrastructure plumbing than actual research and model improvement.

💥
3 AM

Silent Failures

GPU nodes crash, training runs die silently, and hours of compute are lost with no automatic recovery.

📈
0

Reproducibility

Experiment results scattered across W&B dashboards, S3 buckets, and spreadsheets. No single source of truth.

Platform

One platform. The entire
training workflow.

From raw data to deployed model. Everything tracked, versioned, and reproducible.

01

Data Engine

Ingest from anywhere. Built-in deduplication, quality scoring, PII removal, and versioning. Trace any model back to the exact data that trained it.

MinHash Dedup Quality Scoring Versioning Lineage
02

Training Orchestrator

Multi-cloud GPU procurement. Automatic distributed setup. Fault-tolerant training that recovers from node failures without human intervention.

Multi-Cloud Auto FSDP Fault Recovery Spot Handling
03

Experiment Intelligence

Every run tracked automatically. Compare runs side-by-side. Cross-run analysis surfaces patterns and recommendations to improve results.

Auto Tracking Comparison Recommendations
04

Eval & Deploy

Built-in benchmark suite. Human eval integration. One-click deployment to inference endpoints. A/B testing between model versions.

MMLU HumanEval vLLM Deploy A/B Testing
Developer Experience

Four commands. Raw data
to deployed model.

CLI-first and config-driven. Everything tracked, versioned, and reproducible.

01

Prepare your data

Connect to any source. Clean, deduplicate, and version automatically.

epoch data create --source s3://bucket --pipeline clean,dedup
02

Launch training

One config file. Epoch handles GPUs, distribution, and fault tolerance.

epoch run --model meta-llama/Llama-3-8B --data v1
03

Evaluate

Run standard benchmarks and custom evals at every checkpoint.

epoch eval --run latest --benchmarks mmlu,humaneval
04

Deploy

One-click inference endpoint. A/B test against your baseline.

epoch deploy --run latest --endpoint my-model-v1
Why Epoch

Everything you need.
Nothing you don't.

Purpose-built for the complete LLM training workflow. Not a general compute platform. Not just a dashboard.

Capability Modal W&B HuggingFace Epoch
Multi-cloud GPU orchestration ✓ ✕ ✕ ✓
Training-aware fault recovery ✕ ✕ ✕ ✓
Data curation & versioning ✕ ✕ Partial ✓
Experiment tracking ✕ ✓ ✕ ✓
Built-in evaluation suite ✕ ✕ Partial ✓
One-click deployment Partial ✕ ✓ ✓
Cross-run intelligence ✕ ✕ ✕ ✓
Early Access

Train better models.
Ship faster.

Join the waitlist for early access. We're onboarding teams now.

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