Together AI

Overview

Together AI was founded in 2022 and is headquartered in San Francisco, United States. It is an AI platform focused on inference for open-source LLMs, with roots in the UC Berkeley Together research project. The platform offers low-latency inference APIs for 200+ open-source models including Llama, Mistral, DeepSeek and Qwen, along with fine-tuning, training clusters and Together GPU Cloud, complementing local LLM deployment approaches.

Together AI turns open-source models into production-ready APIs: its proprietary inference engine is optimized for NVIDIA GPUs with FP8/INT4 quantization, dynamic batching and KV cache reuse, and the API is fully compatible with the OpenAI format, allowing zero-cost migration by simply changing the endpoint. Alongside Fireworks AI and DeepSeek, Together AI is driving the adoption of open models in LLM inference optimization.

Key Strengths

• 200+ open-source model catalog: Covers Llama 3/3.1 (8B-70B), Mistral, Mixtral MoE, DeepSeek, Qwen and CodeLlama, tracking the Hugging Face community closely for broad selection.
• Low-latency, high-throughput inference: The proprietary engine is optimized for NVIDIA GPUs with FP8/INT4 quantization and dynamic batching, ideal for real-time applications such as AI chatbots.
• Comprehensive fine-tuning: Supports LoRA/QLoRA and full fine-tuning with one-click deployment. See the AI model fine-tuning tutorial for the full workflow.
• OpenAI-compatible API: Supports Streaming, Function Calling and JSON Mode, so migrating from OpenAI requires only changing the endpoint and API key.
• Elastic GPU capacity: A100/H100 training clusters and provisioned throughput support both distributed training and high-concurrency production inference.

Product Ecosystem

Together Inference

The core product offers pay-as-you-go inference for 200+ open-source models, with streaming, batch inference and function calling. It suits text generation, code completion and agent tool calling, billed per token.

Together Fine-tuning

Built for enterprise customization, supporting LoRA, QLoRA and full fine-tuning. Users upload their own datasets, the platform handles formatting and preprocessing, and fine-tuned models deploy to API endpoints in one click.

Together GPU Cloud

An elastic GPU platform offering H100/A100 instances for distributed training, inference deployment and notebook development, billed by GPU hour.

Model Hosting and End-to-End Inference

Trained or fine-tuned models can be hosted directly as inference endpoints, forming a closed loop of train, fine-tune and deploy to reduce time to production.

Limitations

• Limited free tier: Free quotas are low, and high-frequency production calls require paid plans; plan costs with reference to AI infrastructure spending.
• Possible queuing under load: Popular 70B-class models may queue during traffic spikes; provisioned throughput is recommended for critical workloads.
• Dependence on the open-source community: Model quality and release cadence depend on community progress, limiting in-house differentiation.
• Higher latency in mainland China: US-hosted servers lead to higher latency; consider Cloudflare optimization or DeepSeek as a local alternative.

Use Cases

• Open-source model API inference (★★★★★): Low-latency, high-throughput pay-as-you-go APIs for Llama, Mistral, DeepSeek and more, with strong cost efficiency.
• OpenAI migration and replacement (★★★★★): Fully OpenAI-compatible API makes it easy to reduce cost and avoid closed-source lock-in.
• Model fine-tuning and customization (★★★★☆): LoRA or full fine-tuning on open models to build domain-specific models.
• AI agents and tool calling (★★★★☆): Native function calling supports multi-step reasoning and external tool integration.
• Large-scale distributed training (★★★★☆): GPU clusters for open-model training and fine-tuning, suited to research teams and AI companies.
• Mainland China low-latency needs （★★☆☆☆）: US-hosted nodes add latency; consider DeepSeek for local deployment.

Pricing

Note: Prices are public references; check the official website for the latest. For large non-real-time workloads, compare Fireworks AI and Replicate before committing.

FAQ

• Which models does Together AI support? It supports 200+ open-source models including Llama 3/3.1, Mistral, Mixtral, DeepSeek, Qwen and CodeLlama, updated closely with the community. See LLM inference optimization for guidance.

• Is the API compatible with OpenAI? Yes, fully compatible, supporting Streaming, Function Calling and JSON Mode; migrating from OpenAI requires only changing the endpoint and API key. See the OpenAI API platform guide.

• How do I fine-tune a model? Use LoRA/QLoRA or full fine-tuning, upload your dataset, and deploy the fine-tuned model as an endpoint in one click. The fine-tuning tutorial walks through the full workflow.

• How can I guarantee throughput in production? Provisioned throughput with elastic scaling is recommended for high-concurrency workloads, and you can monitor latency and cost through the AI platform ecosystem.

• Can I use it directly from mainland China? Servers are primarily in the United States, so latency is higher; optimize the network with Cloudflare or evaluate DeepSeek as a local option.