Together AI

Together AI is an AI-native cloud platform for building, training, fine-tuning, and deploying open-source AI models. Rather than operating as a single chatbot, Together AI provides infrastructure and developer services that cover several stages of the AI development lifecycle, including model inference, fine-tuning, GPU compute, evaluation, storage, and AI application development. The company describes its platform as a full-stack environment for production AI, powered by systems research focused on improving inference speed, efficiency, and scalability.

Together AI is operated by Together Computer, Inc. Its services include programmatic APIs and web interfaces for hosting, using, fine-tuning, and training large AI models.

The platform’s main focus is open-source and open-weight AI models. Together AI provides access to a large model library covering chat, language, coding, vision, image generation, video generation, audio, speech-to-text, text-to-speech, embeddings, reranking, and moderation. Its current model catalog lists 200+ models, giving developers a broad selection for different AI workloads.

One of Together AI’s core services is inference. Developers can run supported models through managed APIs without having to provision and maintain their own GPU infrastructure. Serverless inference is designed for variable workloads, rapid development, and applications that do not require dedicated hardware. Dedicated inference options are available for workloads requiring more predictable performance, capacity, or control.

Together AI supports serverless inference, batch inference, provisioned throughput, dedicated model inference, and dedicated container inference. Serverless endpoints charge according to actual usage, while dedicated endpoints are charged based on reserved hardware. Batch inference can be used for large asynchronous workloads and can provide lower pricing for eligible serverless models.

The platform also provides fine-tuning for open-source models. Developers can train models on their own datasets to improve accuracy, adapt model behavior, or specialize a model for a particular application. Together AI supports techniques including LoRA, full fine-tuning, and Direct Preference Optimization (DPO). This allows teams to customize models without having to build their own complete training infrastructure.

Another major component is GPU compute. Together AI provides accelerated compute that can scale from self-service clusters to large GPU deployments. The platform also provides managed storage designed for AI workloads. This makes Together AI useful for teams that need infrastructure for model training, experimentation, and large-scale AI workloads in addition to inference.

Together AI also provides Sandbox, a secure code-execution environment intended for AI applications and agents. Developers can use sandboxes to create development environments and run workloads at scale. The platform therefore extends beyond model APIs into infrastructure for building AI-powered applications and agentic systems.

For developers building AI agents, Together AI supports function calling and tool use. Its documentation specifically identifies tool use and agentic loops as supported capabilities. Together AI also provides integrations with agent frameworks including CrewAI, LangGraph, DSPy, PydanticAI, AutoGen, Agno, and Composio.

Together AI provides OpenAI-compatible APIs, which can make migration easier for applications already built around OpenAI’s API format. Developers can point compatible OpenAI Python or TypeScript clients toward Together AI’s API and use supported Together models without completely rewriting their application.

The platform also provides official Python and TypeScript SDKs. Developers can interact with Together models through these SDKs or call the REST API directly from other programming environments. This makes Together AI suitable for integrating AI capabilities into web applications, backend services, developer tools, and other software products.

Together AI has a growing ecosystem of third-party integrations. Its documentation lists integrations with Hugging Face, Vercel AI SDK, LangChain, LlamaIndex, and several agent frameworks. These integrations allow developers to combine Together’s model infrastructure with existing AI development frameworks rather than building every component from scratch.

The platform supports multimodal AI as well. Depending on the model and endpoint, developers can work with text, images, video, and audio. Together AI’s documentation lists image generation, video generation, speech-to-text, text-to-speech, vision, embeddings, and reranking among its supported capabilities.

For generative media, Together AI provides dedicated container inference designed for image, video, and audio workloads. This allows teams to deploy media models on managed GPU infrastructure without operating the underlying infrastructure themselves.

Together AI also supports RAG and search workflows through embeddings and rerankers. Its documentation includes guides for retrieval-augmented generation, contextual RAG, and improving search with reranking models. These capabilities can be used to build AI applications that retrieve relevant information before generating responses.

Pricing is primarily usage-based. Serverless inference is billed according to the amount of work performed, such as input and output tokens for language models, megapixels for image generation, seconds of output for video generation, and audio duration for speech services. Dedicated endpoints are billed according to reserved hardware and running time.

Together AI currently does not offer a free trial. Its support documentation states that access requires a minimum $5 credit purchase, after which customers use a prepaid credit balance across Together AI services.

The platform is best suited to developers, AI startups, enterprises, researchers, and organizations building AI products that require access to open models and scalable infrastructure. It is particularly relevant for applications involving AI agents, coding, model serving, fine-tuning, RAG, multimodal generation, and large-scale inference.

Overall, Together AI is more accurately described as an AI infrastructure and cloud platform than a consumer chatbot. Its combination of model access, high-performance inference, fine-tuning, GPU compute, managed storage, code sandboxes, APIs, agent integrations, and multimodal capabilities makes it a comprehensive environment for developing and deploying production AI applications