Together AI
AI Acceleration Cloud for fast open-source model inference, fine-tuning, and training.
What is Together AI?
Together AI is a comprehensive AI Acceleration Cloud designed for fast inference, fine-tuning, and training of open-source generative AI models. It features the Together Inference Engine, which leverages custom FP8 kernels and speculative decoding to deliver 4x faster performance than standard setups. The platform serves as a powerful alternative to native setups like RunPod or closed ecosystems like the Gemini API, providing developers with OpenAI-compatible APIs to deploy leading language, vision, code, and image models. Through its infrastructure, users can seamlessly access high-throughput models like Qwen or deploy a fast DeepSeek API endpoint for advanced reasoning tasks, alongside specialized tools like Llama Coder setups for autonomous development.
Category
Best Together AI use cases by task, role, industry, and platform
These use cases show where Together AI fits best, ranked by fit score before popularity or pricing.
Together AI Pricing Plans
Compare Together AI free options, Together AI paid pricing plans, and usage notes before you choose the best way to use this AI tool in 2026.
Free endpoints available; Paid inference from $0.008/1M tokens; GPU Clusters from $1.30/hr
Per-token pricing for the 128-expert MoE powerhouse model
Per-token pricing for the 109B parameter model optimized for multi-document analysis
Flat rate per 1 million tokens for the open Mixture-of-Experts model
Per-token pricing for the state-of-the-art reasoning model
Optimized, high-throughput variant of DeepSeek-R1
Tiered performance options based on full precision, optimization, or lowest cost
Hybrid instruct + reasoning MoE model optimized for throughput
In-context image generation and editing endpoint yielding roughly 12.5 images per $1
Free serverless endpoint for the fastest state-of-the-art image generation model
Free serverless endpoint to experiment with distilled reasoning model capabilities
Customizable GPU endpoints billed per minute for deploying standard hardware instances
Dedicated single-tenant Hopper GPU deployment for demanding inference workloads
High-memory dedicated GPU endpoint for large scale deployment
Price per million tokens processed in the training dataset multiplied by the number of epochs
Fine-tuning rates for large-scale language model weights
Reserved training clusters with 80GB HBM2e memory and high-speed InfiniBand networking
Reserved training clusters with 141GB HBM3e memory
Next-generation training infrastructure clusters featuring 384GB or 192GB memory options
Custom VM sandbox environments for large automated AI development pipelines
Per 60-minute session execution cost for processing LLM-generated code
Pricing updated:Jun 11, 2026
Together AI AI Features
Together AI Pros and Cons
Pros
- Extremely fast inference speeds, running models like Llama-3 8B at up to 400 tokens/second
- Cost-efficient pricing, offering up to 11x lower costs compared to certain closed models like GPT-4o
- No vendor lock-in, granting complete model ownership and enterprise VPC deployment flexibility
- Introductory 50% discount on input and output tokens for batch inference workloads
- High-speed interconnects (InfiniBand and NVLink) with a 99.9% uptime SLA for dedicated clusters
Limitations
- Dedicated GPU endpoints require complex hardware orchestration configuration
- Fine-tuning pricing scales dynamically based on the multiple of dataset size, evaluations, and epochs, requiring careful cost tracking