Together AI

Imagine you’re a data scientist tasked with deploying a high-performance AI model for large-scale inference tasks. Your current infrastructure is sluggish and the costs are spiraling, leaving you struggling to meet project deadlines. You need a solution that can rapidly deploy models on reliable GPU clusters without breaking the bank. This is where you start looking for a platform that can offer accelerated compute capabilities and flexible deployment options to optimize performance and costs for your AI workloads.

Together AI positions itself as a full-stack AI platform designed to accelerate the deployment of open-source models. One particularly useful feature is their Serverless Inference, which allows you to deploy models without managing any infrastructure. You can run models on demand, and it scales to handle massive workloads, making it ideal for applications that require quick and dynamic scaling. The process is straightforward: select an open-source model from their library, deploy it with the serverless inference feature, and start processing data efficiently with minimal setup time.

Key Features

• Serverless Inference — Enables high-performance inference as APIs without infrastructure management, crucial for dynamic scaling needs.
• Accelerated Compute GPU Clusters — Offers reliable GPU clusters at scale, optimizing performance for compute-intensive tasks.
• Dedicated Container Inference — Purpose-built GPU infrastructure for generative media workloads, boosting performance for video, audio, and image models.

Pros & Cons

• ✓ High-performance inference options that reduce setup complexity and operating costs
• ✓ Comprehensive model library with top open-source models available for deployment
• ✓ Sandbox environments for secure and scalable AI development

• ✗ Pricing details are not fully transparent, potentially complicating budgeting for large-scale operations
• ✗ May not be suitable for smaller teams that need simpler, more cost-effective solutions

While Together AI shines in optimizing large-scale AI deployments, it may pose challenges for smaller teams or individual developers. The lack of transparent pricing details can make it difficult to forecast costs accurately. Additionally, its feature set may be overwhelming for users simply looking for basic model hosting without the need for extensive compute power or infrastructure management.

When comparing Together AI to platforms like AWS SageMaker, SageMaker offers a broader range of machine learning services and might be more accessible for those already embedded in the AWS ecosystem. However, Together AI’s specialized focus on GPU acceleration and serverless deployments makes it a strong choice for teams heavily focused on efficiency and performance. If pricing transparency is a primary concern, Google Cloud’s AI Platform might offer more clarity and flexibility.

Best For

This platform is best suited for mid to large-sized AI research teams and data science departments that require scalable infrastructure to deploy and fine-tune open-source models. The freemium pricing model allows for experimentation, while paid plans offer enhanced features for more intensive workloads.

Together AI is a compelling choice for organizations seeking to boost their AI capabilities with minimal infrastructure overhead. It’s ideal for those who prioritize performance and scalability in their inference tasks. Together AI offers a strong proposition for AI teams ready to move fast with open-source models and scalable deployments.