Ollama vs Ray
No clear leader: Ollama (73.3) and Ray (70.8) are within the 5-point margin; treat as a tie. The attribute-by-attribute breakdown below, with a source and date on every value, is the honest way to compare them.
Capabilities
Feature-by-feature on the axes that matter for mlops & llmops tools. “-” means undocumented, not absent.
What each one is
The product in its own terms, so the numbers below have context.
Ollama
Ollama enables developers to run open-source language models either on their own computers or through Ollama's cloud infrastructure, with a guarantee that user prompts and responses are never logged, stored, or used for training. Models can run locally without internet (limited by hardware) or in the cloud with no performance constraints.
Ray
Open-source framework for building and scaling distributed ML and Python applications, supporting the full pipeline from data processing and model training to inference and serving with simple APIs.
Pricing
List pricing as published by each vendor, with the date we read it. Always verify at the source before you buy.
Ollama
Free to start; Pro from $20/mo with $60 usage credits; Max at $100/mo with $300 usage credits; Team at $500/mo with shared $1,000 usage credits; Enterprise with custom pricing
- FreeFree
- Run models locally
- Starter usage credits included
- Access to starter models
- Add credits to unlock all models
- No service fees
- Pro$20/mo or $200/yr billed annually
- Everything in Free, plus
- $60 of usage credits per month
- Access to larger pro models
- Run multiple models concurrently
- Fast mode (coming soon)
- Max$100/mo
- Everything in Pro, plus
- $300 of usage credits per month
- Early access to the newest models
- 10 concurrent requests
- Team$500/mo
- Unlimited users
- $1,000 of usage credits per month, shared across the team
- Centralized billing and administration
- Priority support
- Shared projects, skills, and instructions
- EnterpriseContact sales
- Everything in Team, plus
- Model access controls
- Limit team access to specific models
- Set cost budgets for users and API keys
- Private Slack channel with dedicated support
- +1 more
Platform & deployment
Where each product runs and how it can be hosted. A dash means undocumented, not unsupported.
Integrations
What each product connects to. Counts come from the vendor's own integration directory where one exists.
Ollama
- Claude Code
- OpenClaw
- OpenCode
- Codex
- Copilot
- Droid
- Open WebUI
- Onyx
- LibreChat
- Lobe Chat
- NextChat
- Perplexica
- big-AGI
- Lollms WebUI
- ChatOllama
- Bionic GPT
- Chatbot UI
- Hollama
- Chatbox
- Ollama RAG Chatbot
- Dify.AI
- AnythingLLM
- Maid
- Witsy
- +48 more
Ray
- PyTorch
- TensorFlow
- AIBrix
- AReaL
- Cosmos Curate
- Daft
- Data-Juicer
- DeltaCAT
- Modin
- NeMo Curator
- NeMo-RL
- OpenRLHF
- RayDP
- ROLL
- SkyRL
- SLIME
- Syftr
- verl
- vLLM
- Kubernetes
- AWS
- GCP
- Azure
- Databricks
- +3 more
Ollama or Ray: which one depends on you
A composite score cannot know your constraints. Describe them and both get re-weighted against what you actually need, with the evidence behind every position.
Free to run, no account needed to start. How the evaluation works
Comparison generated from independently-sourced facts. Every value links to its source and retrieval date. See the method.