Which models can I fine-tune on Together AI?

The platform supports a broad catalogue of open-weight models, including Llama 3.x, Mistral, Qwen, and DeepSeek.

How is Together AI priced?

Pricing is pay-per-token for both inference and fine-tuning, with dedicated endpoints available for production workloads.

Does Together AI handle both fine-tuning and inference?

Yes, fine-tuning and inference both happen on the same platform, simplifying the operational story compared to using separate providers.

What are the potential downsides of using Together AI?

Latency and throughput vary by model and tier, with serverless options having cold-start characteristics. The developer experience is also less polished than OpenAI's API.

Together AI

Featured✓ Editorially verified

Fine-tune & serve open-weight models (Llama, Mistral, DeepSeek).

Paid· Pay-per-token; fine-tuning per-tokenFine-tuningLlama / Mistral / Qwen / DeepSeek and others8.6 / 10

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In short

Together AI provides a unified platform for fine-tuning and serving open-weight models such as Llama, Mistral, and DeepSeek. It is best for teams seeking competitive per-token pricing and simplified operations without managing their own GPU infrastructure.

Best for

Pick Together when you want open-weight FT + serving in one platform with sensible per-token pricing.

Skip if

Skip it if you need the polish of OpenAI's developer experience or single-vendor support across closed + open.

Together AI hosts and fine-tunes open-weight models at competitive rates. The catalogue is broad — Llama 3.x, Mistral, Qwen, DeepSeek, and many others — and fine-tuning + inference both happen on the same platform, which makes the operational story simpler than gluing together Modal + a separate inference provider.

For teams that want the cost and customisation advantages of open weights without operating GPU infrastructure themselves, Together is the natural pick. The dedicated inference endpoints scale up for production workloads with predictable per-token pricing.

Latency and throughput vary by model and tier; the serverless tier has cold-start characteristics worth measuring for your workload. The product polish is a step behind OpenAI's API surface — clean enough, but less of a one-stop developer experience.

Editor's take

Together is the cleanest commercial answer to "I want open-weight models with closed-weight ergonomics." The catalogue width and the FT + serve integration make it the default for serious open-model production.

— The AI Tool Bible editorial team

Pros

✅ Wide open-model catalogue
✅ Competitive inference pricing
✅ Fine-tune + serve in one place
✅ Dedicated endpoints for production

Cons

⚠️ Latency varies by model
⚠️ Less polish than OpenAI

Use cases

open modelsfine-tuninginference

Frequently asked

Which models can I fine-tune on Together AI?: The platform supports a broad catalogue of open-weight models, including Llama 3.x, Mistral, Qwen, and DeepSeek.
How is Together AI priced?: Pricing is pay-per-token for both inference and fine-tuning, with dedicated endpoints available for production workloads.
Does Together AI handle both fine-tuning and inference?: Yes, fine-tuning and inference both happen on the same platform, simplifying the operational story compared to using separate providers.
What are the potential downsides of using Together AI?: Latency and throughput vary by model and tier, with serverless options having cold-start characteristics. The developer experience is also less polished than OpenAI's API.