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Ray Serve

Free

Scalable model serving library built on the Ray distributed framework.

Open SourceAPICloudSelf-hostedEnterprisePython SDK

Tool Info

Categories
Serving · Deployment
Official Website
docs.ray.io/en/latest/serve/index.html
Repository
ray-project/ray
Official Docs

Overview

Ray Serve deploys models as scalable microservices on Ray clusters.

Handles autoscaling, batching, and multi-model composition.

Pricing

Free tier available
Free (open source)

Pros

  • Horizontally scalable
  • Python-native
  • Composable deployments

Best For

Distributed model servingMulti-model deploymentsPython ML pipelines

When NOT to Use

  • Ray cluster complexity
  • Overkill for simple serving

Related Tools

Ollama
Local model runner with simple command and HTTP interface.
vLLM
High-throughput LLM inference engine with PagedAttention.
LM Studio
Desktop app for running local LLMs with a chat interface.

Alternatives

BentoML
Unified framework for packaging and deploying ML models as APIs.
KServe
Kubernetes-native serverless model serving platform.

Often Compared With

BentoML
Unified framework for packaging and deploying ML models as APIs.
KServe
Kubernetes-native serverless model serving platform.

Compare With

Compare with BentoMLCompare with KServe

Tags

#serving#distributed#mlops

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