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About
Together AI is an AI-native cloud platform designed to accelerate every stage of the AI development lifecycle, from inference to model shaping to large-scale pre-training. The platform provides serverless and dedicated inference APIs for hundreds of top open-source models — including Llama 4, DeepSeek V3, Qwen3, and more — at up to 2× faster speeds and 60% lower cost compared to standard deployments, thanks to proprietary research like FlashAttention-4 and ATLAS runtime-learning accelerators. Developers can run batch inference jobs processing billions of tokens at 50% reduced cost, spin up dedicated GPU clusters (H100, B200, GB200, GB300) via Together Instant Clusters, and fine-tune models on custom data with support for large models and long contexts. The platform also includes model evaluation tooling, managed storage for weights and datasets, and sandbox developer environments. Together AI is particularly well-suited for AI startups, enterprise ML teams, and researchers who need reliable, high-throughput infrastructure without the overhead of managing raw GPU hardware. The company publishes open research — including FlashAttention, ThunderKittens, and DSGym — directly powering platform performance. With a model playground, cookbooks, voice agent tooling, and a startup accelerator program, Together AI supports teams at every stage of building AI-powered products.
Key Features
- Serverless & Dedicated Inference APIs: Access hundreds of top open-source models via high-performance serverless or dedicated inference endpoints, with up to 2× faster throughput powered by FlashAttention-4 and ATLAS accelerators.
- Batch Inference at Scale: Process billions of tokens asynchronously through the Batch Inference API at 50% lower cost than real-time inference, ideal for large offline workloads.
- Fine-Tuning Platform: Shape open-source models with your own data using Together's fine-tuning platform, with support for large models and long context windows.
- GPU Clusters & AI Factory: Provision self-service NVIDIA GPU clusters (H100, B200, GB200, GB300) on demand via Together Instant Clusters, or build custom frontier-scale infrastructure with AI Factory.
- Research-Backed Performance: Platform performance is driven by open research including FlashAttention, ThunderKittens, ATLAS, and the Together Kernel Collection — delivering up to 4× faster LLM inference and 90% faster pre-training.
Use Cases
- Deploying production LLM APIs using open-source models like Llama 4 or DeepSeek with high throughput and low latency.
- Running large-scale offline batch inference jobs to process millions of documents or generate embeddings at reduced cost.
- Fine-tuning open-source foundation models on proprietary datasets to build domain-specific AI applications.
- Training or pre-training custom AI models at scale using dedicated GPU clusters without managing raw infrastructure.
- Prototyping and evaluating multiple open-source LLMs side-by-side using the model playground and evaluation tooling.
Pros
- Cutting-Edge Research Integration: Proprietary research like FlashAttention-4 and ATLAS is directly integrated into the platform, giving users state-of-the-art performance without extra configuration.
- Wide Open-Source Model Library: Supports a broad selection of leading open-source models including Llama 4, DeepSeek, Qwen3, and Kimi K2.5, with regular additions of new frontier models.
- Cost-Efficient at Scale: Workload-specific optimizations and batch inference options deliver up to 60% cost savings compared to standard cloud GPU deployments.
- Full-Stack Platform: Covers the complete AI workflow — inference, fine-tuning, evaluation, storage, and compute — within a single unified platform.
Cons
- Primarily Pay-As-You-Go: The platform is usage-based and can become expensive for very high-volume or large-scale GPU workloads, especially for early-stage teams with tight budgets.
- Focused on Open-Source Models: Together AI specializes in open-source model hosting and does not provide access to proprietary models like GPT-4 or Claude, limiting options for teams requiring those.
- Complex Pricing for Enterprise Tiers: Dedicated infrastructure, AI Factory, and custom cluster pricing requires contacting sales, which may slow procurement for some enterprise teams.
Frequently Asked Questions
What types of inference does Together AI support?
Together AI supports serverless inference (on-demand, pay-per-token), batch inference (async processing at lower cost), dedicated model inference (on reserved hardware), and dedicated container inference for custom models.
Which GPU hardware is available on Together AI?
Together AI offers access to NVIDIA H100, H200, B200, GB200, and GB300 GPUs through its Instant Clusters product and AI Factory infrastructure.
Can I fine-tune my own models on Together AI?
Yes. The Fine-Tuning Platform allows you to train custom versions of open-source models using your own data, with support for large models and extended context lengths.
How does Together AI achieve faster inference speeds?
Together AI integrates its own open research — including FlashAttention-4 (up to 1.3× faster than cuDNN on Blackwell), ATLAS runtime accelerators (up to 4× faster LLM inference), and the Together Kernel Collection — directly into platform infrastructure.
Is Together AI suitable for startups?
Yes. Together AI runs a startup accelerator program specifically designed to help early-stage AI companies build and scale, offering resources, credits, and support alongside the core platform.
