Together AI

Together AI is an AI-native cloud platform that provides open-source model inference, fine-tuning and GPU clusters. It features easy migration with OpenAI-compatible interfaces, and the company claims it offers 2x faster inference, 60% lower costs and 90% faster pre-training.

AI Text Tools#Cloud Platform#Inference#Fine-tuning

Updated: 2026-10-26

What is Together AI?

Together AI positions itself as an "AI-native cloud," offering full-stack capabilities ranging from inference and model fine-tuning to GPU clusters. The company emphasizes that these speedups stem from its cutting-edge research accumulation rather than simply stacking hardware. Publicly available figures include a 2x boost in inference speed, a 60% cost reduction under specific workloads, and a 90% acceleration in preprocessing, covering every stage of AI development from experimentation to large-scale deployment. The company states it is adopted by numerous AI-native companies. For developers, it packages the "acceleration capabilities only affordable by research teams" into a cloud service, making the out-of-the-box experience of open-source models close to that of closed-source APIs. The company also provides pay-as-you-go pricing and cluster solutions for large enterprise clients, adapting to teams of different sizes.

Who is it for?

Teams that need to perform inference with APIs, possibly conduct fine-tuning, and also consider long-term training investments; developers who hope to replace closed-source APIs with open-source models to control costs and gain autonomy; product teams that need to smoothly scale from experiments to large-scale production workloads; and evaluation teams that need to quickly compare multiple open-source models horizontally to find the best cost-performance combination.

Key features

Inference Service

Hosts hundreds of open-source models, provides OpenAI-compatible interfaces, features low migration costs, and enables quick replacement of closed-source APIs.

Fine-tuning

Supports custom training on open-source models, shaping model behavior with business data without having to train from scratch.

GPU Clusters

Provides cluster rental services for large customers with training needs to support pre-training and large-scale experiments.

Together Kernel Collection

An official collection of kernel optimizations developed from the company's research, serving as the core source for its pre-training acceleration and cost optimization capabilities.

Research-driven

Continuously releases open-source models and research results, has long-term participation in the construction of underlying ecosystems such as FlashAttention, and its capabilities evolve synchronously with the ecosystem.

Security and Governance

Provides identity, API key and usage management capabilities for enterprises, suitable for team collaboration scenarios.

How to use Together AI

Plan a path centered around "inference API + fine-tuning + cluster": First use the inference API to verify the effect.
if customization is indeed needed, proceed to fine-tuning.
and when the scale expands, consider a cluster.
For actual cost-effectiveness, it is recommended to conduct stress tests with the load of your own business instead of just looking at the official benchmark figures.
As the official community continuously releases open-source models and kernel optimizations, it is advisable to include "re-evaluating candidate models" in the regular checklist to keep up with ecological updates.

Things to know

Figures such as 2x, 60%, and 90% are based on benchmarks under specific official loads. The performance in your actual scenario needs to be measured with real prompt words and context lengths, and should not be taken as a direct commitment.

It excels in the open-source model domain. If your business heavily relies on closed-source flagship models, there is no need to force a migration just to save costs—prioritize performance instead.

GPU clusters and large-scale training are toB businesses that require sales contact; pay-as-you-go inference can be activated self-service, and the two processes are different.

Fine-tuning and training incur additional data and computing power costs; assess whether the benefits are worth it before getting started.

The actual cost-effectiveness of the inference service varies with the load type; scenarios with long contexts and high concurrency should be tested separately.