Anyscale

From the creators of Ray, Anyscale helps teams build and run AI workloads at production-scale with speed, reliability, and cost-efficiency

Anyscale is a fully managed Ray offering from the creators of open source Ray. It helps build, scale, and deploy AI applications on Ray without building and managing complex infrastructure.

Ray RLlib is a scalable reinforcement learning library.

Ray Serve is a scalable model serving library for building online inference applications.

The Anyscale Blog publishes tutorials, product updates, and customer stories about Ray, AI infrastructure, distributed computing, machine learning, and LLMs.

Ray's Resource Isolation uses Linux cgroup v2 to prevent node and job failures during memory and CPU contention. Benchmarks show up to 1.5x faster completion.

Ray Data 2.56 improves batch inference and training pipelines with fewer OOMs, less object spilling, automatic memory tuning, and faster performance.

Ray Data LLM enables scalable batch LLM inference with vLLM, delivering high throughput, fault tolerance, and 2x performance for large workloads.

With the Anyscale on Azure public preview, enterprises can now run foundation-model-scale AI workloads, from multimodal data preparation to training and inference, entirely inside their own Azure tenancy all while achieving up to 90% cost savings.

Experienced AI platform leader to help organizations move from AI pilots to production-scale systems

Anyscale has open roles in engineering, product, sales, marketing, and operations.

Ray on Anyscale can be deployed for AI workloads in AWS, Azure, GCP, Nebius, or CoreWeave.

The page compares tools by use case and describes Ray for unstructured data processing and batch inference at scale.

Anyscale enables developers to build and scale AI and ML applications with Ray. Based in San Francisco, backed by a16z, NEA, Addition, and Intel Capital.

Powered by Ray, Anyscale helps AI builders run data-intensive workloads to build and deploy Foundation Models and AI at scale on any cloud.

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OpenAI and other well-funded AI startups are using acquisitions to broaden products, enter markets and add specialized teams, Crunchbase data shows. Through Sept. 29, the data tracks 195 acquisitions of AI startups by other venture-backed AI companies, 14% more than in all of 2025, while the number of buyers grows just 2%. Over three years, 67 repeat buyers account for about 42% of tracked deals; OpenAI leads with 20 acquisitions, including 10 this year.

Oct 2026 · Crunchbase NewsNews article

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