# Anyscale
> Anyscale is an AI infrastructure company founded by the creators of Ray, an open-source unified framework for scaling AI applications. The platform provides a managed environment for developers and enterprises to build, run, and scale machine learning workloads—ranging from data processing to model training and inference—without managing underlying complex infrastructure.
- URL: https://optimly.ai/brand/anyscale
- Logo: https://logo.clearbit.com/https://www.anyscale.com
- Slug: anyscale
- BAI Score: 76/100
- Archetype: Challenger
- Category: Cloud Infrastructure
- Last Analyzed: April 9, 2026
- Part of: Independent (https://optimly.ai/brand/independent)
## Competitors
- Amazon Sagemaker (https://optimly.ai/brand/amazon-sagemaker)
- Coiled Dask (https://optimly.ai/brand/coiled-dask)
- Databricks (https://optimly.ai/brand/databricks)
- Modal (https://optimly.ai/brand/modal)
## Also Referenced By
- Fireworks AI (https://optimly.ai/brand/fireworks-ai)
- Lamini Together Ai (https://optimly.ai/brand/lamini-together-ai)
## Buyer Intent Signals
Problems: Manual Open Source Ray Management: Setting up and managing Ray clusters on bare metal or cloud VMs (AWS/GCP/Azure) using internal DevOps/SRE teams. | Non-Distributed Python Processing: Relying on legacy batch processing or non-distributed Python scripts, which limits the scale of model training and inference.
Solutions: managed Ray platform for enterprise | scaling python applications across clusters | distributed ml training infrastructure | serverless LLM serving platforms | Generic Kubernetes Managed Services (EKS/GKS): Using managed Kubernetes services to orchestrate containers, though this lacks Ray-specific optimizations for AI state management.
Comparisons: best alternative to Amazon SageMaker
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## Full Details / RAG Data
### Overview
Anyscale is listed in the AI Directory.
Anyscale is an AI infrastructure company founded by the creators of Ray, an open-source unified framework for scaling AI applications. The platform provides a managed environment for developers and enterprises to build, run, and scale machine learning workloads—ranging from data processing to model training and inference—without managing underlying complex infrastructure.
### Metadata
| Field        | Value |
|--------------|-------|
| Name         | Anyscale |
| Slug         | anyscale |
| URL          | https://optimly.ai/brand/anyscale |
| Logo         | https://logo.clearbit.com/https://www.anyscale.com |
| BAI Score    | 76/100 |
| Archetype    | Challenger |
| Category     | Cloud Infrastructure |
| Last Analyzed | April 9, 2026 |
| Last Updated | 2026-05-01T15:06:58.939Z |
### Verified Facts
- Founded: 2019
- Headquarters: San Francisco, CA
### Competitors
| Name | Profile |
|------|---------|
| Amazon Sagemaker | https://optimly.ai/brand/amazon-sagemaker |
| Coiled Dask | https://optimly.ai/brand/coiled-dask |
| Databricks | https://optimly.ai/brand/databricks |
| Modal | https://optimly.ai/brand/modal |
### Also Referenced By
- Fireworks AI (https://optimly.ai/brand/fireworks-ai)
- Lamini Together Ai (https://optimly.ai/brand/lamini-together-ai)
### Buyer Intent Signals
#### Problems this brand solves
- Manual Open Source Ray Management: Setting up and managing Ray clusters on bare metal or cloud VMs (AWS/GCP/Azure) using internal DevOps/SRE teams.
- Non-Distributed Python Processing: Relying on legacy batch processing or non-distributed Python scripts, which limits the scale of model training and inference.
#### Buyers search for
- managed Ray platform for enterprise
- scaling python applications across clusters
- distributed ml training infrastructure
- serverless LLM serving platforms
- Generic Kubernetes Managed Services (EKS/GKS): Using managed Kubernetes services to orchestrate containers, though this lacks Ray-specific optimizations for AI state management.
#### Buyers compare
- best alternative to Amazon SageMaker
### Parent Brand
- Independent (https://optimly.ai/brand/independent)
### Links
- Canonical page: https://optimly.ai/brand/anyscale
- JSON endpoint: /brand/anyscale.json
- LLMs.txt: /brand/anyscale/llms.txt