Together AI

Together AI is a cloud infrastructure platform that provides serverless APIs and GPU clusters optimized for running, fine-tuning, and deploying open-source large language models. The platform offers two main value propositions: a managed inference API that lets developers query models like Llama, Mixtral, DeepSeek, and Qwen through simple REST calls, and a GPU cloud service offering self-service access to enterprise-grade hardware (H100s, B200s, GB200s) across 25+ global data centers. They've differentiated themselves with ATLAS, a runtime-learning accelerator claiming 4x faster LLM inference, and a batch inference API offering 50% cost reductions for non-real-time workloads. The platform supports full fine-tuning and LoRA-based model customization, code execution environments, and model evaluation tools.

AI analysis from Feb 23, 2026

• Serverless Inference API with per-token pricing for models like Llama, DeepSeek, Qwen, and OpenAI's open-source variants
• ATLAS runtime-learning accelerators for up to 4x faster LLM inference with adaptive speculative execution
• Batch Inference API for processing large token volumes at 50% reduced cost compared to standard pricing
• Fine-tuning platform supporting both LoRA and full parameter fine-tuning for larger models with longer context windows
• Dedicated Endpoints and Reserved GPU Clusters for predictable, custom-configured infrastructure
• Instant Clusters self-service GPU provisioning with NVIDIA H100, H200, B200, and GB200 hardware
• Code Sandbox and Code Interpreter for building development environments and executing LLM-generated code
• Model evaluation tools and a model selector (WhichLLM) to help users choose appropriate models for their workloads

• 1Building production AI applications with open-source models while maintaining cost control through batch inference APIs
• 2Fine-tuning proprietary models on custom datasets for domain-specific tasks without vendor lock-in
• 3Running real-time inference at scale with sub-second latency for applications like voice AI and chat interfaces
• 4Developing and testing multiple LLM architectures simultaneously across different model families
• 5Deploying AI workloads on dedicated infrastructure with custom hardware configurations and reserved capacity
• 6Processing large-scale batch workloads like content generation, data classification, or embedding generation at reduced costs
• 7Running GPU-intensive development environments and code execution sandboxes for AI development

Pricing details not publicly available on the provided website content. The site references 'Per-token & per-minute pricing' for inference, mentions batch inference API at 50% lower cost for most models, and indicates hourly rates plus custom pricing for GPU clusters, but actual price points are gated behind the pricing page or require contacting sales.

ML engineers and AI product teams at startups and enterprises building production applications; developers requiring cost-effective inference at scale; organizations seeking to avoid proprietary LLM vendor lock-in; teams needing fine-tuning capabilities for domain-specific models; infrastructure teams operating custom GPU clusters who want managed alternatives

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