Together AI

Categories: Coding & Developer Tools, Data Analysis, Automation / Agents | Pricing: Pricing unavailable | Official Website ↗

Together AI provides a full-stack AI platform for accelerating inference, model shaping, and pre-training on a research-optimized cloud.

Together AI offers a comprehensive AI-native cloud platform designed to support the entire AI development lifecycle, from experimentation to large-scale deployment. It provides various services including high-performance serverless inference, batch inference for massive workloads, and dedicated inference options for custom models and hardware. The platform is optimized for speed and cost-efficiency, leveraging cutting-edge research like FlashAttention and ATLAS runtime-learning accelerators. Beyond inference, Together AI also offers accelerated compute with GPU clusters, developer environments (Sandboxes), and managed storage solutions. Its model shaping capabilities include fine-tuning open-source models with advanced research techniques to improve accuracy and control behavior, alongside tools for model evaluation. The platform is backed by foundational systems research, with numerous publications on topics like inference acceleration, model architectures, and AI agents.

Key Features

Serverless Inference for open-source models
Batch Inference API for large workloads
Dedicated Model Inference on custom hardware
Dedicated Container Inference for generative media
Accelerated GPU Clusters
Developer Sandboxes for AI apps
Managed Storage for model weights & data
Fine-Tuning platform for open-source models

Pros

Offers high-performance inference with research-backed accelerators
Provides cost-effective solutions for batch processing
Supports fine-tuning of open-source models without managing infrastructure
Offers a full-stack platform covering compute, storage, and model shaping
Backed by active systems research for continuous optimization

Cons

Pricing is complex and varies significantly by model and usage
Requires technical expertise for optimal utilization
Specific pricing for dedicated compute and storage is not transparently listed
No explicit free plan or trial mentioned for direct testing
Focuses heavily on developers and enterprises, potentially less accessible for individuals

Use Cases

Deploying large language models (LLMs) for various applications
Fine-tuning open-source models for specific tasks or datasets
Running high-throughput batch inference jobs
Developing and testing AI agents in sandboxed environments
Accelerating generative media workloads (video, audio, image)

Best For

AI developers
Machine learning engineers
Research teams
Startups building AI applications
Enterprises deploying large-scale AI models

Platforms: Web

Watch demo on YouTube ↗

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