Pachyderm (HPE)

Data-versioning and pipeline platform — Pachyderm File System tracks lineage across pipeline runs. Acquired by HPE in 2023.

Acquired · Verified 2026-10-29 · fresh as of 2026-10-29 · 18 cited facts, 3 with a third-party source · 1 recorded change · Suggest a correction

At a glance

Type: Data versioning + pipelines vendor fix
Tier: T2
Section: Training infrastructure
Created: 2014 vendor fix
Latest release: v2.12.2 (2025-01-15) repo fix
License: Apache-2.0 repo fix
GitHub: pachyderm/pachyderm — ~6.3k stars, last commit 2025-02 repo fix
Pricing: searched not found fix
Funding: $28.5M raised through Series B (2020-06) before HPE acquired in 2023-01 (terms undisclosed). vendor fix
Freshness: 2026-10-29 fix
Layer: model-infra repo fix
Licensing: open-source repo fix
Deployment locus: local repo fix
Platform vs product: platform repo fix
Accelerator: yc-w15 third-party fix
Developer activity: OpenRank 0.37 (12-mo avg 5.04) · 3 participants — 2026-01 third-party fix

Optimised for: searched not found

Anti-fit: searched not found

searched not found

searched not found

Acquired by HPE 2023; previously $42M raised.

Other training infrastructure in the catalog, ranked by inbound references.

MLflow T1
Databricks-incubated open platform for ML lifecycle — tracking, projects, models, model registry. Standard ML-ops baseline. MLflow 3.0 (2024) added LLM-eval features.
AWS SageMaker T1
AWS's managed ML platform — Studio, Training, HyperPod (large-scale clusters), Endpoints. Industry-standard for enterprise AWS-hosted training.
Weights & Biases T1
Experiment-tracking and ML-ops platform (metrics, artifacts, sweeps, Weave tracing/evals). Acquired by CoreWeave (completed 2025-05-05, ~$1.7B); now part of CoreWeave Forge.
Comet ML T2
ML experiment tracking + LLM evaluation platform (Opik for LLM eval). Comet competes head-to-head with Weights & Biases.
Azure Machine Learning T1
Microsoft Azure's managed ML platform — pipelines, AutoML, Designer, prompt flow (LLM). Heavy integration with Azure OpenAI and Azure AI Studio.
LoRA T3
Microsoft Research's Low-Rank Adapters method — trains rank-r matrices added to attention weights; 10000x parameter reduction. Foundation of all adapter-style fine-tuning.

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Changes to this record's lifecycle, licence, release, pricing and hosting facts, from the catalog's git history. Subscribe (Atom)

2026-10-27
- License first recorded as “Apache-2.0” source
- Latest release first recorded as “v2.12.2 (2025-01-15)” source
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