Machine Learning · head to head
Ray vs Teable
Teable
Spreadsheets
Spreadsheet interface over real PostgreSQL tables, so the data stays queryable by anything that speaks SQL
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Ray windows support is beta and multi node Ray clusters are untested on Windows; Teable the automation builder is far less capable than the established commercial alternatives, so multi step workflows usually end up in an external tool that must be paid for and maintained separately.
- They diverge on capability: Ray covers Distributed computing, Teable covers PostgreSQL native storage.
- Prices and features above were last checked on 31 August 2026.
Where they differ
Only the attributes on which Ray and Teable actually diverge.
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in Ray
- Distributed computing
- Ray Train
- Ray Tune
- RLlib
- Ray Serve
- PyTorch
- TensorFlow
- Hugging Face
Only in Teable
- PostgreSQL native storage
- Multiple views
- Linked records and rollups
- Generated REST API
- Real time collaboration
- Self hosting via Docker
- Field level permissions
What people use each for
The jobs each tool is most often brought in to do.
Ray
- Distributed AI model training and servingnot Teable
- Large-scale data processingnot Teable
- Reinforcement learning workloadsnot Teable
- ML inference servingnot Teable
Teable
- A team that has hit the record ceiling of a hosted spreadsheet database and does not want to move to raw SQLnot Ray
- Operational data that a business intelligence tool must also read directly, without an export or a sync jobnot Ray
- A regulated or data resident organisation that needs the underlying database inside its own infrastructurenot Ray
- An internal tool where a grid interface and a REST API over the same table are both requirednot Ray
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Ray
- Windows support is beta and multi node Ray clusters are untested on Windows
- Windows lacks copy on write forking, which raises memory requirements, and Ray code assumes UNIX filenames
- Multi node clusters are untested on Apple Silicon Macs
- The Java API is experimental and community supported only, and requires matching Java and Python versions
- Python 3.13 support is beta
Teable
- The automation builder is far less capable than the established commercial alternatives, so multi step workflows usually end up in an external tool that must be paid for and maintained separately.
- Self hosting moves PostgreSQL backups, version upgrades, connection pooling and capacity planning onto your team, and the licence saving disappears if that time is costed honestly.
- The integration catalogue is small, so connecting to a common business system often means writing against the REST API rather than installing a connector.
- The project is young relative to the products it replaces, and interface and API changes still arrive at a pace that requires reading release notes before upgrading.
- Storing every table as a real PostgreSQL table means schema changes are real migrations, so a careless field type change on a large table can lock it far longer than a spreadsheet user would expect.
Pricing, plan by plan
Ray
Free- Open SourceFree
- Full Ray framework
- All libraries
- Community support
- Anyscale PlatformFree
- Managed infrastructure
- Enterprise support
- SLAs
Teable
Free- Self hosted open sourceFree
- No licence fee
- Unlimited rows subject to your PostgreSQL capacity
- Docker deployment
Which should you pick?
Choose Ray if
- You need distributed computing.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want ray train.
Choose Teable if
- You need postgresql native storage.
- You want to start without paying.
- You work on Web, Linux.
- You also want multiple views.
Questions people ask
- Is Ray or Teable better?
- Neither clearly leads. Ray starts at Free and Teable at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Ray or Teable?
- Ray starts at Free and Teable at Free.
- Does Ray or Teable run on more platforms?
- Ray runs on Linux, Mac, Windows. Teable runs on Web, Linux.
- Can I use Ray for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Ray best used for?
- Ray is most often used for distributed ai model training and serving, large-scale data processing, reinforcement learning workloads, ml inference serving. Of those, distributed ai model training and serving and large-scale data processing are not what Teable is typically brought in for.
- What can Ray do that Teable cannot?
- Ray covers Distributed computing, Ray Train, Ray Tune, RLlib. Teable covers PostgreSQL native storage, Multiple views, Linked records and rollups, Generated REST API.
Answered from the vendors’ own pages
Ray: Is Ray free?
Yes. Ray is free and open source software with over 34,800 GitHub stars and 1,000+ contributors. Users can download and use the Ray framework at no cost.
SourceTeable: How many rows can it actually hold?
As many as your PostgreSQL instance can serve. There is no product imposed record limit on the self hosted edition, which is the main reason to choose it.
Ray: Is there a paid option for Ray?
Yes. Anyscale, the managed platform built by Ray's creators, offers paid tiers with enterprise features like governance and advanced tooling. Specific Anyscale pricing details are not listed on the Ray website.
SourceTeable: Can I query the data with SQL directly?
Yes. Tables are real PostgreSQL tables, so any SQL client or reporting tool can read them.
Ray: Can I try Ray with credits?
Yes. New users can try Ray with $100 credit on Anyscale's managed platform to explore the service.
SourceTeable: Is self hosting genuinely free?
The licence is. The database server, backups and the engineer maintaining them are not.
Teable: Does it replace Airtable feature for feature?
No. Views and field types are close; automations, apps and the integration catalogue are not.
Teable: What happens to my data if the project stops?
It remains in a standard PostgreSQL database that you control, which is a materially better exit than a proprietary export format.
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