Comparison

Arize AI vs Ray

No leader: the top candidate Arize AI has only 0.32 confidence (low), below the 0.35 needed to declare a winner. The attribute-by-attribute breakdown below, with a source and date on every value, is the honest way to compare them.

Machine formatsJSONMarkdownGraphQLor send Accept: application/json
Arize AI71
Ray67
Score
Vioscale score
Arize AI71 / 100low · 32%updating
Ray67 / 100medium · 68%updating
Pricing
Free tier
Arize AI
Ray
Model
Price level
Arize AI
Rayfree
Integrations
Count
Arize AI25
Ray17
Security
Gdpr
Arize AI
Ray
Hipaa
Arize AI
Ray
Iso27001
Arize AI
Ray
Pci
Arize AI
Ray
Scorecard
Arize AI
Ray5.7
Soc2
Arize AI
Ray
Reliability
Status page
Arize AI
Ray
Adoption
Dependent repos
Arize AI
Github stars
Arize AI
Package downloads weekly
Arize AI
Activity
Commits last 30d
Arize AI
Ray100
Release
Cadence days
Arize AI
Ray18
History
Arize AI
License
Spdx
Arize AI
Language
Primary
Arize AI

Capabilities

Feature-by-feature on the axes that matter for mlops & llmops tools. “-” means undocumented, not absent.

Core
Tool role
Arize AIEnd-to-end ML platform
Ray-
Deployment
Self-hostable / OSS core
Arize AI
Ray
Managed cloud available
Arize AI
Ray
On-prem / VPC deployment
Arize AI
Ray
Observability
LLM tracing / observability
Arize AI
Ray-
Evaluation (offline / LLM-judge / human)
Arize AI
Ray-
Dev
Prompt management + versioning
Arize AI
Ray-
Tracking
Experiment tracking / model registry
Arize AI
Ray
Serving
Model serving / inference endpoint
Arize AI-
Ray
Interop
OpenTelemetry / OpenLLMetry compatible
Arize AI
Ray-
Framework-agnostic
Arize AI
Ray
Gateway
Multi-provider model support
Arize AI
Ray
Data
No-train-on-customer-data guarantee
Arize AI-
Ray-

What each one is

The product in its own terms, so the numbers below have context.

Arize AI

An AI engineering platform that enables teams to observe agent behavior end-to-end, run evaluations at scale, and systematically improve agents through testing and experimentation, available as both a managed cloud service and self-hosted deployment.

Independently observed

Ray

Ray is an open-source unified framework for scaling Python and machine learning applications across any infrastructure. It provides distributed compute primitives, specialized AI libraries for data processing, model training, tuning, and serving, with seamless scaling from development environments to large clusters.

Independently observed

Pricing

List pricing as published by each vendor, with the date we read it. Always verify at the source before you buy.

Arize AI

Pricing not documented yet.

Ray

FreeFree tier
as of verify ↗

Platform & deployment

Where each product runs and how it can be hosted. A dash means undocumented, not unsupported.

Platforms
Web
Arize AI
Ray
CLI
Arize AI
Ray
Deployment
Cloud / SaaS
Arize AI
Ray
Self-hosted
Arize AI
Ray
On-premise
Arize AI
Ray
Hybrid
Arize AI
Ray

Integrations

What each product connects to. Counts come from the vendor's own integration directory where one exists.

Arize AI

25 total
  • OpenAI
  • Anthropic
  • Azure OpenAI
  • AWS Bedrock
  • Vertex AI
  • Google GenAI
  • NVIDIA NIM
  • Gemini
  • OpenRouter
  • LiteLLM
  • Claude Code
  • Cursor
  • OpenCode
  • LangGraph
  • Vercel AI SDK
  • Mastra
  • CrewAI
  • LlamaIndex
  • DSPy
  • OpenAI Agents SDK
  • Claude Agent SDK
  • BigQuery
  • Databricks
  • Snowflake
  • +1 more
Independently observed

Ray

19 total
  • PyTorch
  • TensorFlow
  • AIBrix
  • AReaL
  • Cosmos Curate
  • Daft
  • Data-Juicer
  • DeltaCAT
  • Modin
  • NeMo Curator
  • NeMo-RL
  • OpenRLHF
  • RayDP
  • ROLL
  • SkyRL
  • SLIME
  • Syftr
  • verl
  • vLLM
Independently observed

Comparison generated from independently-sourced facts. Every value links to its source and retrieval date. See the method.

Arize AI vs Ray: an evidence-based comparison · Vioscale