Together AI – AI Tool | HokAI
The AI Native Cloud—full-stack platform for training, fine-tuning, and deploying open-source AI models
About Together AI
Together AI is a research-driven cloud platform that empowers developers and enterprises to build, train, fine-tune, and deploy open-source AI models at scale. Founded in 2022, the company provides a comprehensive AI infrastructure layer offering serverless inference, dedicated model deployment, batch processing, fine-tuning, GPU clusters, and managed storage—all optimized with cutting-edge research breakthroughs like FlashAttention, Medusa, and speculative decoding. The platform supports 200+ open-source models including Llama, Mistral, Qwen, DeepSeek, and proprietary models like Mamba-3 and Dragonfly. Together AI's cost-effective token-based pricing and research-optimized infrastructure deliver 2x faster inference and 60% lower costs compared to alternatives. The company is backed by $534M in funding (Series B in Feb 2025 at $3.3B valuation) from investors including NVIDIA, Salesforce Ventures, Kleiner Perkins, and General Catalyst. Unlike closed-source alternatives, Together AI prioritizes open-source transparency and data privacy. Teams can deploy models on serverless infrastructure for variable workloads, dedicated endpoints for production scale, or GPU clusters for custom training—all without vendor lock-in. The platform powers AI startups like Cursor, Pika Labs, and NexusFlow in production.
Pricing
Serverless inference starts at $0.03/1M tokens (lowest-cost models like Gemma 3n); blended pricing up to $1.50/1M tokens for larger models. Batch API at 50% discount. Fine-tuning from $0.10/1M tokens processed. GPU clusters from ~$0.30-$4.25/hour per GPU. Free tier available with API credits.
Key Features
- Serverless Inference: Deploy 200+ open-source models on demand with pay-per-token pricing, no infrastructure management, and no long-term commitments. Powered by cutting-edge inference research.
- Dedicated Model Inference: Deploy models on reserved, isolated compute resources with guaranteed performance, full control, and best-in-market economics for production workloads.
- Fine-Tuning at Scale: Fine-tune open-source models (up to 100B+ parameters) using latest research techniques including SFT, DPO, and LoRA with 6x higher throughput than competitors.
- GPU Clusters & Accelerated Compute: Scale from 16 GPUs to thousands with instant self-serve clusters optimized via Together Kernel Collection, enabling fast pre-training and custom workloads.
- Batch Processing API: Process massive asynchronous workloads up to 30 billion tokens per model at up to 50% lower cost with flexible scheduling and error recovery.
- Managed Storage & Code Sandboxes: High-performance object storage and parallel filesystems colocated with compute, zero egress fees, plus secure code sandboxes for AI agent development.
Pros
- 2x faster inference and 60% lower costs vs. alternatives through research-optimized infrastructure
- Largest open-source model ecosystem (200+ models) with native support for Llama, Mistral, Qwen, DeepSeek
- True vendor independence—all models are open-source, fine-tuning output owned by user, can deploy anywhere
- Enterprise-grade compliance: SOC 2 Type II, HIPAA-compliant with dedicated endpoints and reserved capacity
- Cutting-edge research shipping to production—FlashAttention, speculative decoding, and custom kernels for measurable speedups
Cons
- Token-based pricing complexity—variable rates per model make budget prediction difficult; no fixed-price tier for unpredictable workloads
- Smaller ecosystem of integrations vs. OpenAI/Claude; LangChain/LlamaIndex support requires additional setup
- Developer overhead—users must select, benchmark, and integrate models themselves; no opinionated defaults for non-technical teams
- Learning curve for fine-tuning and GPU cluster management compared to fully managed services