Together AI

Full-stack AI platform designed for production workloads, combining serverless inference, GPU clusters, fine-tuning, and custom training capabilities.

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Overview

Together AI is a full-stack AI platform designed for production workloads, combining serverless inference, GPU clusters, fine-tuning, and custom training capabilities. Built on cutting-edge systems research, the platform enables teams to accelerate inference by 2x, reduce costs by 60%, and speed up pre-training by 90% through workload-specific optimizations.

The platform supports a wide range of open-source models including DeepSeek, Qwen, Llama, Gemma, and others, with flexible deployment options ranging from serverless APIs to dedicated infrastructure. Teams can start with serverless inference and scale to dedicated endpoints, provisioned throughput, or custom GPU clusters as their needs grow.

Together AI emphasizes research-driven optimization, offering tools for model shaping, fine-tuning, evaluations, and sandbox environments for AI development. The company is backed by Series C funding and partners with organizations like Y Combinator to deliver specialized infrastructure solutions.

Key features

Serverless inference API with 2x faster performance
Provisioned throughput with token-based pricing and SLAs
Dedicated model inference on custom hardware
Fine-tuning for open-source models
GPU clusters from instant to thousands of GPUs
Managed storage with zero egress fees
Code sandbox environments for AI development
Batch inference for asynchronous workloads
Support for 30+ open-source models
Research-backed kernel optimizations

Pros

Significant performance improvements (2x faster inference, 90% faster pre-training)
Transparent, model-specific pricing
Flexible deployment options from serverless to dedicated
Strong research foundation with published optimizations
Support for diverse open-source models
No long-term commitments for serverless
99% uptime SLA on provisioned throughput

Cons

Pricing varies significantly by model and use case
Requires technical expertise for optimization
Dedicated infrastructure requires custom quotes
Learning curve for advanced features like fine-tuning

Use this if

You need scalable, cost-optimized inference infrastructure; want to fine-tune open-source models; require dedicated GPU capacity; or are building production AI applications with specific performance requirements.

Skip this if

You need a simple, no-code AI solution; prefer proprietary closed-source models; or require minimal technical setup and configuration.

Best for

Teams building production AI applicationsOrganizations needing scalable inference infrastructureCompanies fine-tuning open-source modelsDevelopers requiring GPU cluster accessProjects requiring cost-optimized AI workloads