Ray vs Together AI
On the evidence we track, Together AI leads this comparison with a composite score of 73/100. 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.
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.
Together AI
LeaderTogether AI provides cloud infrastructure for hosting and serving open-source language, image, audio, and video models through serverless inference, reserved capacity, and dedicated GPU instances. The platform includes fine-tuning capabilities, batch processing, managed storage, and GPU cluster support for custom model development and training—eliminating the need for users to manage underlying infrastructure.
Pricing
List pricing as published by each vendor, with the date we read it. Always verify at the source before you buy.
Together AI
LeaderUsage-based pricing starting from $0.00014/1M tokens for serverless inference. Reserved capacity and dedicated GPU instances available; on-demand GPU pricing from $3.69/hour.
- Serverless InferenceFrom $0.00014–$15/1M tokens depending on model. Pay only for usage.
- 50+ open-source models
- Variable pricing by model and token type
- Batch API support
- Private endpoints
- Provisioned ThroughputReserved throughput capacity with 99% SLA. PTU-based pricing structure.
- Reserved token capacity
- 99% uptime SLA
- Token-based pricing model
- Production-grade reliability
- Dedicated Inference$3.69–$8.99 per GPU per hour (on-demand); reserved discounts available.
- Single-tenant GPU instances
- Guaranteed performance (no resource sharing)
- Custom model support
- Autoscaling for traffic spikes
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.
Ray
- PyTorch
- TensorFlow
- AIBrix
- AReaL
- Cosmos Curate
- Daft
- Data-Juicer
- DeltaCAT
- Modin
- NeMo Curator
- NeMo-RL
- OpenRLHF
- RayDP
- ROLL
- SkyRL
- SLIME
- Syftr
- verl
- vLLM
Together AI
LeaderNot documented yet.
Comparison generated from independently-sourced facts. Every value links to its source and retrieval date. See the method.