Startup profile for Anyscale: latest funding, latest known valuation, total disclosed funding, investors, status, sources, and why the company is interesting. Part of the Venture Capital Tracker startup directory.

Startup profile · funding coverage

Anyscale funding, valuation and investors

Commercial platform for Ray distributed computing — scale AI/ML workloads on any cloud.

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Funding, valuation & investors

Answer-first snapshot

Latest funding

Series C

$100M · 2022

Latest known valuation

Not publicly disclosed

Total disclosed equity funding

$121M

Excludes debt, grants, acquisitions, secondaries, and IPO proceeds.

Current status

private

growth · San Francisco, California

Investors in latest funding

Other: Andreessen Horowitz

Sources: latest funding: Series C (2022): $100M co-led by a16z and Addition at $1B valuation (Anyscale press) Last verified: 2026-07-25

Overview

Anyscale offers a managed platform built on Ray for distributed Python workloads — training, tuning, serving, and batch processing AI models at scale. Ray is one of the most widely adopted open-source projects for distributed machine learning, and Anyscale provides enterprise tooling, support, and cloud orchestration on top.

Why Anyscale is interesting

Ion Stoica and Berkeley RISELab team commercializing Ray, the open-source framework behind much of production distributed AI training and inference.

Product & use cases

Anyscale offers a managed platform built on Ray for distributed Python workloads — training, tuning, serving, and batch processing AI models at scale.

  • Enterprise platform deployment for core workflows

Key facts

  • Series C (2022): $100M co-led by a16z and Addition at $1B valuation (Anyscale press)
  • Series A (2019): $20.6M led by a16z with NEA, Intel Capital (Anyscale press)
  • Product: managed Ray platform for distributed training, hyperparameter tuning, and model serving
  • Co-investors not in VCT directory: NEA, Intel Capital

Funding history (newest first)

Series C

2022 $100M

Source: Series C (2022): $100M co-led by a16z and Addition at $1B valuation (Anyscale press)

Series A

2019 $20.6M

Source: Series A (2019): $20.6M led by a16z with NEA, Intel Capital (Anyscale press)

Investors in our directory

Funds linked from Anyscale's profile — open a fund page for stage focus and related deal articles.

Competitive landscape

Edge: Ion Stoica and Berkeley RISELab team commercializing Ray, the open-source framework behind much of production distributed AI training and inference.

Anyscale competes in enterprise-saas, ai-ml. Incumbents have distribution; startup edge depends on product depth and buyer references. See competitors for specific tradeoffs — no invented market share claims.

  • Salesforce incumbent

    Enterprise platform incumbent.

  • Microsoft incumbent

    Platform bundling competitor.

  • OpenAI adjacent

    Foundation model platform.

  • Scale AI adjacent

    AI data/eval infrastructure.

Notable stories

  • Series C (2022): $100M co-led by a16z and Addition at $1B valuation (Anyscale press)
  • Series A (2019): $20.6M led by a16z with NEA, Intel Capital (Anyscale press)

Industries

Enterprise SaaS AI & Machine Learning

FAQs about Anyscale

Practical answers founders, operators, and investors typically search for.

Anyscale offers a managed platform built on Ray for distributed Python workloads — training, tuning, serving, and batch processing AI models at scale.
Directory-linked investors include /fund/addition, /fund/andreessen-horowitz.
growth stage, private status. Series C (2022): $100M co-led by a16z and Addition at $1B valuation (Anyscale press)
Private as of July 2026 (status: private).
See fundraisingRounds for verified dates and disclosed amounts.
See competitors section — named alternatives with honest positioning notes.
Not publicly disclosed unless noted in highlights.
San Francisco, California

By Venture Capital Tracker

Last updated:

Editorial note: AI tools assisted with research, structure, or drafting. Venture Capital Tracker retains human editorial responsibility for factual accuracy, relevance, and source quality before publication.