Together AI vs Anyscale: Quick Verdict
Together AI vs Anyscale is a decision most ML teams face when they need more than the OpenAI or Anthropic API — when they want to fine-tune their own models, run inference at scale, or train on proprietary data without sending it to a closed-source provider.
Choose Together AI if your primary need is fast, affordable inference on open-source models with an easy fine-tuning API. Together has built one of the best developer experiences for teams that want to run Llama, Mistral, or custom models without managing their own GPU cluster.
Choose Anyscale if you need full distributed training control, complex Ray-based workload orchestration, or you're already embedded in the Ray ecosystem for large-scale ML pipelines.
Most startups and mid-sized ML teams will find Together AI sufficient and significantly easier to operate. Anyscale is the right choice when you need the full power of Ray.
Company Overview
| | Together AI | Anyscale | |-|-------------|----------| | Founded | 2022 | 2019 | | Focus | Inference + fine-tuning platform | Ray-based distributed compute | | Open-source | Together-computer, model library | Ray framework | | Primary use case | Run & fine-tune OSS models | Distributed training & serving | | Pricing model | Per-token inference + training compute | Compute + platform fee | | Target user | Developers, ML engineers | ML platform teams |
Inference: Speed, Models, and Pricing
Together AI's core product is a fast inference API over a large catalog of open-source models. As of 2026, they support 100+ models including all major Llama variants, Mistral, Mixtral, Qwen, and DBRX. Pricing starts around $0.10–0.20 per million tokens for smaller models, with larger models in the $0.80–$1.50 range.
Anyscale also offers model serving via its RayServe-based infrastructure, but the experience is more infrastructure-oriented. You're not calling a hosted endpoint — you're deploying and managing your own serving stack on Anyscale compute. More control, more work.
For teams that simply want to swap OpenAI calls with open-source model calls behind a compatible API, Together AI wins clearly. Their API is OpenAI-compatible, meaning a one-line change to your base URL is often enough to migrate.
Fine-Tuning Capabilities
This is where the comparison gets interesting.
Together AI fine-tuning:
- Managed fine-tuning via API — upload data, trigger a job, get a deployed model endpoint
- Supports LoRA and full fine-tuning depending on model size
- Typical fine-tuning jobs complete in 30–90 minutes for instruction-tuning on small datasets
- Pricing: compute costs billed at GPU-hour rate + inference cost after deployment
- No infrastructure management required
Anyscale fine-tuning:
- Full Ray Train + DeepSpeed/FSDP integration for large-scale distributed training
- You control batch size, parallelism, checkpointing strategy, and hardware selection
- Better for large models (70B+) where you need fine-grained control over multi-node training
- Requires more ML infrastructure expertise to operate effectively
- More appropriate for research teams or teams training foundation models, not adapting them
For most production teams doing task-specific fine-tuning on Llama-sized models, Together AI's managed approach is faster to ship and easier to maintain. See our overview of what is fine-tuning to understand the tradeoffs before committing to either approach.
Developer Experience
Together AI invests heavily in developer experience. Their documentation covers the most common use cases with working code examples. The playground lets you compare model outputs side by side. The CLI and SDKs are actively maintained.
Anyscale's developer experience is more complex by necessity — Ray is a powerful framework with a steep learning curve. The platform assumes you know how to write distributed Python, configure clusters, and debug Ray actor failures. For teams that know Ray well, this is fine. For teams new to it, expect a significant onboarding period.
Scalability and Infrastructure Control
Anyscale's core advantage is its depth of infrastructure control via Ray:
- Multi-node training with automatic fault tolerance
- Heterogeneous hardware clusters (mix CPU and GPU nodes)
- Custom autoscaling policies
- Full observability via Ray Dashboard and metrics export
- Support for extremely large models via pipeline and tensor parallelism
Together AI's infrastructure is mostly abstracted. You call an API; they handle provisioning. This is excellent for 90% of use cases. For teams with complex multi-stage training pipelines, proprietary orchestration requirements, or frontier model training workloads, Anyscale's control floor is necessary.
Data Privacy and Security
Both platforms offer enterprise agreements with data isolation. Key differences:
- Together AI: Your fine-tuning data is processed on their infrastructure. Enterprise agreements include data deletion guarantees and non-training commitments.
- Anyscale: Can deploy in your own VPC on AWS or GCP, giving you full data residency control. Better for regulated industries where third-party data processing is restricted.
If data sovereignty is a hard requirement, Anyscale's VPC deployment option is a meaningful differentiator.
Pricing Comparison
| Workload | Together AI | Anyscale | |----------|-------------|----------| | Llama 3 8B inference | ~$0.10/M tokens | Compute rate + overhead | | Fine-tuning (small dataset) | ~$10–50 per job | $1–6/hour GPU + setup | | Long-running training | Compute rate | Compute rate + platform fee | | Storage | Included | S3 or GCS (separate) |
Together AI is generally more cost-effective for inference-heavy workloads. Anyscale can be more cost-efficient for long training runs where you're maximizing GPU utilization across many nodes — but only if you have the expertise to operate it efficiently.
Which Should You Choose?
Use Together AI if:
- You want to run open-source models without managing infrastructure
- You need fine-tuning with a managed, API-first workflow
- Your team is small and you can't afford deep ML infrastructure expertise
- You're prototyping and need to move fast
Use Anyscale if:
- You're training models at scale (70B+ parameters, multi-node runs)
- You need full Ray integration for complex orchestration pipelines
- You require VPC-level data isolation
- You already have deep Ray expertise on your team
For most teams building AI applications on top of open-source models, Together AI is the better starting point. You can always migrate to Anyscale as your training needs grow more complex.
Related: What is Fine-Tuning? · AWS Bedrock vs Azure OpenAI · What is AI Observability?
[Not sure which ML infrastructure fits your team's needs? Book a 15-min scope call → and we'll help you decide.]
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