Together AI

What is Together AI?

Together AI is a cloud platform for running, fine-tuning, and deploying open-source AI models at scale. Founded in 2022 by a team including former Meta AI researchers and engineers, Together AI provides infrastructure and tools for developers and enterprises to work with hundreds of open-source large language models, image generation models, and embedding models. The platform offers a cost-effective alternative to proprietary API services like OpenAI, with competitive pricing and the flexibility of open-source model selection.

Together AI's platform includes a comprehensive API compatible with the OpenAI SDK, making it easy to switch between models without changing code. The platform supports popular models like Llama 3, Mistral, Mixtral, Stable Diffusion, and many others. Together AI also offers model fine-tuning services, allowing organizations to customize models on their own data for specialized use cases.

How Together AI Works

Getting started with Together AI is straightforward. Users can access the platform through its web interface or API, depending on their needs. The platform is designed to minimize setup time while maximizing productivity. New users typically find the interface intuitive, with clear workflows guiding them through each step of their chosen task. The platform handles complex backend processing automatically, allowing users to focus on their creative or business goals rather than technical configuration.

For teams and organizations, Together AI offers collaborative features that enable multiple users to work together efficiently. The platform's architecture supports scaling from individual use to enterprise deployment without significant changes to the workflow. Regular updates and improvements ensure that users always have access to the latest AI capabilities and features.

Key Features

• Open-Source Model API: Access to 200+ open-source models through a single API compatible with the OpenAI SDK.
• Model Fine-Tuning: Fine-tune open-source models on custom datasets with managed infrastructure and optimized training pipelines.
• Llama 3 Support: First-class support for Meta's Llama 3 family including 8B, 70B, and 405B parameter models.
• Image Generation: API access to Stable Diffusion XL, SD 3, and other open-source image generation models.
• Embedding Models: Generate text embeddings for search, clustering, and RAG applications.
• Competitive Pricing: Significantly lower costs than proprietary APIs, especially for high-volume inference.
• GPU Infrastructure: Access to high-performance GPU clusters (H100, A100) for training and inference workloads.

Who Should Use It

Together AI is ideal for AI developers building applications with open-source models, startups looking to reduce AI infrastructure costs, enterprises exploring model customization through fine-tuning, and researchers experimenting with different model architectures. It is particularly valuable for teams that want model flexibility without being locked into a single proprietary provider.

Pricing

Together AI offers pay-as-you-go pricing based on compute usage. Llama 3 8B inference starts at $0.10 per million tokens. Fine-tuning pricing varies by model size and training duration. The platform offers a free tier with limited credits for experimentation. Custom enterprise pricing is available for dedicated GPU clusters.

Pros & Cons

Pros: Cost-effective alternative to proprietary APIs. Wide model selection with easy switching. OpenAI-compatible API simplifies migration. Strong fine-tuning capabilities for customization.

Cons: Open-source model quality varies. Less mature than established providers like OpenAI. Documentation can be sparse for newer models. No SLA guarantees on free tier.

Conclusion

Together AI represents a significant advancement in its category, offering powerful AI capabilities that were previously unavailable or prohibitively expensive. Whether you are an individual creator, a growing business, or a large enterprise, the platform provides tools that can meaningfully improve your workflow and output quality. The combination of ease of use, powerful features, and flexible pricing makes it a compelling choice for anyone looking to leverage AI in their work.

As AI technology continues to evolve, Together AI is well-positioned to incorporate new advancements and maintain its relevance in a rapidly changing landscape. Users can expect ongoing improvements to existing features and the introduction of new capabilities that push the boundaries of what is possible with AI-powered tools.

Alternatives

Replicate: Cloud platform for running open-source models with a similar API. Groq: Ultra-fast inference platform with LPU hardware acceleration. Anthropic API: Proprietary API with Claude models for enterprise use. View Together AI on PureAINav.

Why Choose Open-Source Models with Together AI

The shift toward open-source models is one of the most significant trends in AI in 2026. Organizations increasingly prefer open-source models for several reasons: data privacy (no data leaves your infrastructure), cost efficiency (60-80% cheaper than proprietary APIs), model flexibility (switch between models as new ones are released), and customization (fine-tune on your data). Together AI makes this practical by providing the infrastructure needed to run open-source models at production scale without the operational complexity.

For teams using CrewAI for multi-agent applications, Together AI's structured output and function calling capabilities are essential. Agents need to produce reliable, parseable outputs for tool calling and inter-agent communication, and Together AI's JSON mode ensures that model outputs are always valid and structured. Combined with Tavily for web search and Langfuse for observability, Together AI provides the inference foundation for a complete production AI agent stack.

The platform's fine-tuning capabilities are also noteworthy. Developers can fine-tune Llama 4 or DeepSeek models on their specific domain data — customer support transcripts, code repositories, legal documents — and deploy the fine-tuned model as a dedicated endpoint. This is dramatically cheaper than fine-tuning proprietary models, and the resulting model is fully owned by the developer with no data sharing concerns.