LlamaIndex vs Ray
On the evidence we track, Ray leads this comparison with a composite score of 71/100. Scores are only directly comparable because these tools share a category; the full breakdown and every source is below.
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.
LlamaIndex
A platform that uses AI models to parse and extract data from documents across 130+ file formats, supporting complex layouts, multiple languages, and multi-step extraction agents. Optimized for accuracy, cost, and latency at enterprise scale.
Ray
LeaderOpen-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.
LlamaIndex
Free (10K credits/month), Starter $50/month (400K credits), Pro $500/month (400K + 800K bonus), Pay-as-you-go up to $5,000/month, Enterprise custom
- FreeFree
- Basic support
- 100 users
- 130+ file formats
- 80+ languages supported
- Starter$50/mo
- Basic support
- Multiple parse tiers
- Auto Mode with up to 80% savings
- Saved parse configs
- Advanced table/chart extraction
- +1 more
- Pro$500/mo + $1000 one-time
- Priority Slack support
- All parsing and extraction capabilities
- Extraction agents
- Webhooks & API callbacks
- Smart result caching
- Pay-as-you-go$0.00 per credit
- Priority Slack support
- Flexible credit usage up to $5,000/month
- EnterpriseContact sales
- Volume discounts on credits
- 5x higher rate limits
- Enterprise SSO
- SaaS or Hybrid cloud deployment
- Dedicated account manager
- +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.
- AWS
LlamaIndex
- Microsoft Azure
- GitHub
Ray
Leader- PyTorch
- TensorFlow
- AIBrix
- AReaL
- Cosmos Curate
- Daft
- Data-Juicer
- DeltaCAT
- Modin
- NeMo Curator
- NeMo-RL
- OpenRLHF
- RayDP
- ROLL
- SkyRL
- SLIME
- Syftr
- verl
- vLLM
- Kubernetes
- GCP
- Azure
- Databricks
- DeltaLake
- +2 more
LlamaIndex 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.