Input legal discovery files
AI Models & Assistants / Knowledge library
Together AI
inference, fine-tuning, compute and evaluations for AI builders
Why learn Together AI?
Useful for understanding AI infrastructure that serves open models, fine-tunes models and scales inference for production workloads.
Where it came from
Together AI describes products for serverless and dedicated inference, accelerated compute, fine-tuning, sandboxes and evaluations.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Summarize the official positioning for Together AI in one paragraph.
- Identify the buyer, developer, or user workflow Together AI is built for.
- Compare Together AI with a neighboring catalog topic and name the boundary.
- List two implementation risks or adoption questions for Together AI.
- Design a five-question evaluation that would test whether someone understands Together AI.
Documentation and sources
- Together AI products page (www.together.ai)
Catalog research notes
- Research snapshot: September 20, 2026. This entry summarizes the linked public documentation and should be refreshed before making version-specific claims.
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Real World Example
Automated Legal Document Summarization Pipeline
A boutique law firm managing thousands of discovery documents that require rapid, consistent summarization for litigation support.
The business problem
The firm faces high latency and inconsistent formatting when using generic, non-specialized model endpoints to process sensitive legal discovery files, leading to bottlenecks in case preparation.
How this topic solves it
By utilizing Together AI's fine-tuning capabilities, the firm can adapt an open-source model to recognize specific legal terminology and document structures. This is paired with dedicated model inference to ensure consistent, low-latency access to the specialized model, bypassing the variability of shared serverless environments.
- Curate a high-quality dataset of annotated legal summaries for model fine-tuning.
- Upload the dataset to the Together AI platform to initiate the fine-tuning process on a base open-source model.
- Deploy the resulting fine-tuned model weights to a dedicated inference instance for consistent performance.
- Integrate the dedicated endpoint into the firm's document management system via API calls.
This diagram illustrates the transition from raw legal documents to summarized output using specialized infrastructure.
Adapting model to legal domain
Hosting the specialized model
Final structured legal summary
- Raw Documents → Training Data → Fine-Tuning
- Fine-Tuning → Deploy Weights → Dedicated Inference
- Dedicated Inference → Inference Request → Summarized Output
Expected business outcome
The firm achieves highly accurate, domain-specific summaries that maintain a consistent tone and structure, significantly accelerating the attorney review process.
Limitations and checks
Dedicated inference instances require continuous resource allocation; the firm must monitor utilization rates to ensure the cost-to-performance ratio remains favorable compared to serverless alternatives.