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What is Together AI?

Together AI provides infrastructure for building and running large language models and other AI systems. The platform offers access to open-source models alongside tools for fine-tuning, inference, and deployment at scale. It's designed for teams who want to work with AI models without relying solely on closed commercial services. The service focuses on speed and cost efficiency, letting you run models on distributed hardware. Whether you're experimenting with different models or putting something into production, Together AI handles the infrastructure complexity so you can focus on your application.

Key Features

Access to open-source models

Run popular models like Llama, Mistral, and others without licensing restrictions

Fine-tuning tools

Customise models on your own data using the platform's training infrastructure

Inference API

Deploy models with low latency and high throughput for production applications

Distributed computing

use multiple GPUs and hardware configurations for faster processing

Model management

Version control, monitoring, and performance tracking for deployed models

Cost monitoring

Transparent pricing and usage analytics to track spending

Pros & Cons

Advantages

  • Open-source focus means no vendor lock-in; you're working with freely available models
  • Generally more affordable than proprietary model APIs for high-volume inference
  • Flexible infrastructure lets you choose hardware configurations suited to your workload
  • Good option for teams wanting fine-grained control over their models and data

Limitations

  • Requires more technical knowledge than simple chat interfaces; you'll need familiarity with APIs and model deployment
  • Performance and reliability depend on your own infrastructure choices and configuration decisions
  • Smaller community and ecosystem compared to larger cloud providers

Use Cases

Fine-tuning open-source models on proprietary datasets for specialise tasks

Running inference at scale for chatbots, content generation, or classification systems

Building applications where data privacy is important and you want models running in your own environment

Experimenting with different model architectures and comparing their performance and cost

Cost-conscious production deployments where open-source models meet your performance requirements