Together AI

What is Together AI?

Together AI is a cloud platform purpose-built for running, fine-tuning, and deploying open-source AI models at scale. The platform provides access to over 200 leading open-source models including Llama 4, DeepSeek R1, Mixtral, DBRX, and Stable Diffusion through a simple, OpenAI-compatible API. Unlike proprietary AI providers that lock you into their specific models, Together AI gives developers the flexibility to use any open-source model with consistent pricing and infrastructure, making it one of the most important platforms in the open-source AI ecosystem.

The platform operates across three main product lines. Serverless Inference provides pay-per-token access to 200+ models with no infrastructure management — you send API requests and pay only for the tokens processed. Dedicated Endpoints offer reserved GPU capacity for production workloads that require consistent latency and throughput guarantees. And GPU Cloud provides raw compute access to NVIDIA H100, H200, and B200 GPUs for teams that want to run custom training jobs or deploy models on their own terms. This layered approach lets developers start with serverless inference during prototyping and scale to dedicated infrastructure as their needs grow.

Fine-tuning on Together AI is designed to be straightforward. You upload a dataset, select a base model, and launch a fine-tuning job with a few API calls or through the web dashboard. The platform handles distributed training across multiple GPUs, manages checkpointing, and provides evaluation metrics. Fine-tuned models can be deployed immediately as serverless endpoints or on dedicated infrastructure. Together AI also supports reinforcement learning from human feedback (RLHF), direct preference optimization (DPO), and parameter-efficient methods like LoRA for teams that want to customize model behavior without the cost of full fine-tuning.

Pricing is one of Together AI's strongest competitive advantages. The platform consistently offers some of the lowest per-token prices for inference, with models like Llama 3.1 8B available at $0.18 per million output tokens — often 5-10x cheaper than equivalent proprietary models. New accounts receive $25 in free credits to experiment, and the startup accelerator program offers $15,000 to $50,000 in credits for qualifying companies. For developers and companies building on open-source models, Together AI provides the most cost-effective and developer-friendly infrastructure available.

Together AI Pricing

Together AI uses pay-as-you-go pricing with no monthly subscriptions. You pay only for what you use, with rates varying by model and compute type.

• Serverless Inference: Pay-per-token pricing varies by model. Llama 3.1 8B from $0.18/M output tokens, Llama 4 Maverick $0.27/$0.85 per M input/output tokens, DeepSeek R1 at $7.00/M output tokens. 71+ free models available at no cost.
• Fine-Tuning: Priced per token processed during training, varying by base model size and method (full fine-tuning, LoRA, DPO). Includes distributed training, checkpointing, and evaluation.
• GPU Cloud: NVIDIA HGX H100 at $3.49/hour, HGX H200 at $4.19/hour, HGX B200 at $7.49/hour. Commitment discounts available for reserved capacity.
• Free Credits: New accounts receive $25 in free credits. Startup Accelerator program provides $15,000-$50,000 in credits for qualifying companies.

Minimum credit purchase is $5 after free credits are exhausted. Some premium models and dedicated endpoints require Build Tier 2+, which unlocks after $5 in actual spend. No monthly fees or minimum commitments.

Key Features

• 200+ Open-Source Models: Access to the widest selection of open-source models including Llama 4, DeepSeek R1, Mixtral, DBRX, and Stable Diffusion through a unified API
• One-Line Fine-Tuning: Fine-tune any supported model on custom data with minimal code, supporting full fine-tuning, LoRA, DPO, and RLHF methods
• OpenAI-Compatible API: Drop-in replacement API format that works with existing OpenAI SDK integrations, requiring minimal code changes to switch
• Serverless & Dedicated Endpoints: Choose between pay-per-token serverless inference or reserved GPU capacity with guaranteed latency for production workloads
• GPU Cloud: Direct access to NVIDIA H100, H200, and B200 GPUs for custom training, inference, and research workloads with flexible hourly pricing
• Embedding API: Generate text embeddings using open-source embedding models for semantic search, RAG pipelines, and similarity matching
• Image Generation: Access to Stable Diffusion, FLUX, and other open-source image models through the same API with consistent pricing
• Function Calling: Structured output and tool-use support for building AI agents and applications that interact with external systems
• Batch Processing: Submit large batches of inference requests at discounted rates for non-time-sensitive workloads like dataset annotation and evaluation
• 71+ Free Models: Extensive selection of models available at zero cost for experimentation, prototyping, and low-volume production use

Pros & Cons

Best For

AI Application Developers: Engineers building production applications on open-source models who need reliable, cost-effective inference infrastructure with fine-tuning capabilities.

ML Teams Fine-Tuning Models: Data science teams customizing open-source models on proprietary data who need an accessible fine-tuning platform without managing GPU infrastructure.

Startups Optimizing AI Costs: Early-stage companies that need GPT-4-class model performance at a fraction of the cost by leveraging open-source alternatives through Together AI's infrastructure.

AI Researchers: Academics and research labs that need access to a wide range of open-source models for benchmarking, evaluation, and experimentation without managing their own GPU clusters.

FAQ