Together AI
FreemiumFast open-source model inference
About Together AI
Together AI is an AI Acceleration Cloud providing an end-to-end platform for the full generative AI lifecycle. It offers fast inference, fine-tuning, and training capabilities for generative AI models using easy-to-use APIs and highly scalable infrastructure. Users can run and fine-tune open-source models, train and deploy models at scale on their AI Acceleration Cloud and scalable GPU clusters, and optimize performance and cost. The platform supports over 200 generative AI models across various modalities like chat, images, code, and more, with OpenAI-compatible APIs.
How to Use
Users can interact with Together AI through easy-to-use APIs for serverless inference or deploy models on custom hardware via dedicated endpoints. Fine-tuning is available through simple commands or by controlling hyperparameters via API. GPU clusters can be requested for large-scale training. The platform also offers a web UI, API, or CLI to start or stop endpoints and manage services. Code execution environments are available for building and running AI development tasks.
Together AI's
Key Features
- Serverless Inference API for open-source models
- Dedicated Endpoints for custom hardware deployment
- Fine-Tuning (LoRA and full fine-tuning)
- Together Chat app for open-source AI
- Code Sandbox for AI development environments
- Code Interpreter for executing LLM-generated code
- GPU Clusters (Instant and Reserved) with NVIDIA GPUs (GB200, B200, H200, H100, A100)
- Extensive Model Library (200+ generative AI models)
- OpenAI-compatible APIs
- Accelerated Software Stack (e.g., FlashAttention-3, custom CUDA kernels)
- High-Speed Interconnects (InfiniBand, NVLink)
- Robust Management Tools (Slurm, Kubernetes)
Use Cases
- Accelerating AI model training and inference for enterprises (e.g., Salesforce, Zoom, InVideo)
- Building AI customer support bots that scale to high message volumes (e.g., Zomato)
- Developing production-grade AI applications by unlocking data for developers and businesses
- Creating next-generation text-to-video models (e.g., Pika)
- Building cybersecurity models (e.g., Nexusflow)
- Achieving simpler operations, improved latency, and greater cost-efficiency for AI models (e.g., Arcee AI)
- Developing custom generative AI models from scratch
- Performing multi-document analysis, codebase reasoning, and personalized tasks
- Managing complex tool-based interactions and API function calls
- Generating and debugging code with advanced LLMs
- Executing visual tasks with advanced visual reasoning and video understanding
- Data tasks such as classification and structured data extraction
Key Features
Pros & Cons
- Fast inference speeds (up to 2× faster) courtesy of research optimizations like FlashAttention
- Potential for significant cost savings (up to 60% lower cost) compared to standard cloud GPU providers
- Full-stack platform covering inference, fine-tuning, compute, storage, and sandbox development
- Supports a wide variety of open-source models, with flexibility to deploy serverless, batch, or dedicated
- No infrastructure management required for serverless inference, lowering operational overhead
- Code sandbox enables building and testing AI applications in a secure, scalable environment
- Free tier access and its specific limits should be verified on the current pricing page
- Optimal performance and cost benefits depend on workload type and model selection; results may vary
- Full-stack platform may present a learning curve for users wanting only a single inference endpoint
- Dedicated and batch pricing for high-volume usage may require contacting sales for exact quotes
- Some advanced features (e.g., fine-tuning larger models, dedicated GPUs) may require more technical expertise