What kind of AI models can I run on Together AI?

Together AI supports a broad range of open-source models including large language models (LLMs) like Llama, Mistral, CodeLlama, and various generative AI models for text, code, and image generation.

How does Together AI achieve faster inference and lower costs?

Together AI achieves this through highly optimized software and hardware infrastructure specifically designed for AI workloads, custom kernel development, and efficient GPU utilization, leading to significant performance gains and cost reduction compared to general-purpose cloud services.

Can I fine-tune my own models on the Together AI platform?

Yes, Together AI provides robust capabilities for fine-tuning existing open-source models with your proprietary datasets, allowing you to adapt models to specific tasks and improve their performance for your unique applications.

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Together AI

Freemium

aigpu

Together AI offers a high-performance, cost-effective cloud platform for training, fine-tuning, and deploying open-source AI models.

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Open-source AI cloud.

Together AI provides an optimized cloud infrastructure specifically designed for large language models (LLMs) and other generative AI. It differentiates itself by focusing on open-source models, offering faster inference speeds and lower costs compared to proprietary alternatives. Users can access a catalog of pre-trained models, fine-tune them with their own data, or train models from scratch on their scalable GPU clusters, all while maintaining control and transparency over their AI stack.

Pros

Significantly faster inference and training speeds due to highly optimized infrastructure.
Cost-effective solution compared to major cloud providers for large-scale AI model deployment and fine-tuning.
Strong support for a wide range of open-source models, promoting flexibility and avoiding vendor lock-in.

Cons

Requires some technical expertise to fully leverage the platform's capabilities for model deployment and optimization.
While supporting many models, the ecosystem might not yet be as extensive as some established proprietary platforms for niche use cases.
Reliance on open-source models means users are subject to the development cycles and community support of those individual models.

Key features

High-performance inference API for various open-source LLMs.
Fine-tuning capabilities for custom model adaptation with proprietary datasets.
Scalable computing infrastructure for large-scale AI model training.
Comprehensive model catalog including Llama, Mistral, CodeLlama, and more.
Low-latency and high-throughput processing for demanding AI applications.

Integrations

RESTful API access for programmatic interaction.Python SDK for easy integration into existing workflows.Hugging Face Hub for model sharing and access.Compatible with standard ML frameworks like PyTorch and TensorFlow.Supports Docker for containerized deployments.

Target audience

AI developers, researchers, and enterprises looking to efficiently build, fine-tune, and deploy open-source large language models and other generative AI applications at scale.

Ratings & Reviews

0.0

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Key Metrics

Active Users

10K+

Founded

2022

Headquarters

San Francisco, California/USA

Pricing Tiers

On-Demand Inference

Pay-as-you-go pricing for accessing pre-trained open-source models via API, billed per token or per image generation.

Varies by model and usage

Fine-tuning

Pricing based on GPU hours consumed for training and fine-tuning custom models, plus storage costs.

From $2.25/hour for A100 GPU

Dedicated Instances

Reserved compute capacity for consistent performance and cost optimization for high-volume or specific project needs.

Custom

Frequently Asked Questions

Top Alternatives to Together AI

Banana

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Lambda Labs

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RunPod

Trusted option for teams comparing capabilities and pricing.

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