Together AI
Run, fine-tune, and scale open-source models: start on cheap shared inference and graduate to dedicated GPUs (including on-demand B200s) as traffic grows.
The verdict on Together AI: Developers who want to run, fine-tune, or deploy open-source models via API without managing GPU infrastructure Together AI's pitch is full open-model lifecycle through one API: run inference, fine-tune, and scale to production on 200+ models across major open-source families, with dedicated GPU options including on-demand B200s when shared infrastructure isn't enough. Pricing: Pay-as-you-go from $1.04/M tokens / dedicated GPUs from $6.49/hr.
Best For
Developers who want to run, fine-tune, or deploy open-source models via API without managing GPU infrastructure
Standout Feature
A catalog of 200+ open models across major families, all accessible through one OpenAI-compatible interface
TL;DR
Competitive pricing and real production scale, usage costs are worth modeling before committing to heavy workloads.
Alternatives
Fit Score: use-case reference
How this is scoredTogether AI scored 1 to 5 on five dimensions for the named use cases below. Part of our catalog-wide use-case reference, not a product-quality review rating.
Developers wanting the breadth and control of 200+ open-source models via one OpenAI-compatible API without managing GPU infrastructure
4.4/5- Capability
- 5/5
- Reliability
- 4/5
- Value
- 4/5
- Ease
- 4/5
- Compatibility
- 5/5
Directly the tool's bestFor and standout: a catalog of 200+ open models across major families accessible through one OpenAI-compatible interface, matching pros on a broad open-model catalog that scales for production.
A heavy production workload without first modeling expected token volume
2.8/5- Capability
- 3/5
- Reliability
- 3/5
- Value
- 2/5
- Ease
- 3/5
- Compatibility
- 3/5
The tool's own ourTake and con state usage costs add up at heavy workloads and it's worth modeling token volume before committing as a production platform.
Editorial judgment grounded in this tool's own published pricing, features, and our existing pros and cons, not a certification or a measured benchmark. Product quality is assessed separately under our review methodology.
Overview
Not yet reviewedProduct quality is assessed from original evidence. How reviews work.
Together AI is a cloud platform for running, fine-tuning, and deploying open-source AI models, offering an API-first approach to the full open-weight model lifecycle at competitive pricing compared to closed-model providers. Its inference catalog includes 200+ models across major open families, Llama, Mistral, Qwen, DeepSeek, Stable Diffusion, FLUX, accessible through an OpenAI-compatible API that enables switching to open models without application code changes. Together's fine-tuning service handles supervised fine-tuning, LoRA fine-tuning, and full model fine-tuning on custom datasets, with managed training infrastructure that eliminates the need for GPU cluster management. The fine-tuned model is then deployable through the same inference API for seamless production use.
Together's pricing undercuts OpenAI and Anthropic on comparable capability tiers by 5–10x in many cases, making it cost-effective for high-volume inference that would be prohibitively expensive on closed-model APIs. The Playground provides model comparison and prompt testing across the full catalog. Dedicated endpoints provide reserved capacity for production applications with guaranteed throughput. Together is commonly used by companies that want frontier-quality inference at open-model prices, teams fine-tuning domain-specific models for specialized applications, and developers prototyping with multiple model families before committing to a production architecture.
The source of the legacy catalog rating and review count has not been substantiated. Neither is presented here as a verified review rating or included in review structured data.
Workflow Fit Scores are separate, use-case-specific editorial judgments, not user-review averages. Read the Fit Score methodology.
- • Broad open-model catalog
- • Scales for production
- • Competitive pricing
- • Usage costs add up
- • Less consumer-facing
Key Features
- Inference for 200+ open models
- Fine-tuning and training
- OpenAI-compatible API
- Dedicated endpoints
Trust & Data
Verified 2026-09
- Training on your data
- NoThe policy explicitly states no data is used to train Together's own foundation models without explicit opt-in consent, and users can enable Zero Data Retention to prevent storage of prompts and outputs entirely; this does not separately address customer fine-tuning workflows, which train a customer-specified model under the same framework.
- Compliance
- Not published
- Retention
- Not published
- Export
- Not published
Source: together.ai
As published by the vendor. Verify independently before purchase decisions.
Our Take
The OpenAI-compatible interface means low switching friction for existing code. Usage costs add up at heavy workloads, so it's worth modeling your token volume before committing to it as a production platform. The right fit is a developer or team who wants the breadth and control of open-source models without building or managing GPU infrastructure themselves.