TogetherAI云平台为训练、微调和服务开源生成性AI模型

4.8 (6)

审阅者 Daniel Nikulshyn·更新 2026年7月

概览

TogetherAI 是一个基于云的平台，为开发者和研究人员提供丰富的开源大语言模型库以及构建生成式 AI 应用的工具。平台支持推理、微调和完整模型训练，基础设施在大规模下针对性能和成本效率进行优化。平台强调开放与协作，允许团队尝试领先的社区模型，部署自定义变体，并通过标准 API 进行集成。它面向希望在 AI 堆栈上拥有比封闭模型供应商更多控制权的初创企业和大型企业。

主要功能

对开源LLMs的推理API
在自定义数据集上微调
专用和无服务器GPU端点
图片和代码模型的支持
与OpenAI兼容的SDK和端点
可伸缩的训练基础设施

价格

模型: Free
分类: AI Agents Platform
评分: 4.8 / 5 (6)

使用场景

无服务器推理

快速运行开源模型的最佳方法，通过前沿的推理研究驱动

批处理推理

通过异步推理，降低成本并可扩展到30亿个令牌，用于任何无服务器模型或私有部署

微调

使用最新研究技术微调开源模型，改进准确性，减少幻觉和控制行为

优点 & 缺点

优点

开源模型的广泛目录
竞争性价格的推理和训练
支持微调和自定义部署
OpenAI兼容的API简化了迁移

缺点

需要技术专长才能有效地使用
与一些封闭平台相比，工具化可能不够成熟
性能与模型选择中的性能会有所不同

评测

4.8

6 个评分的平均值。

登录以留下评测。

Carlos Mendoza

Mar 28, 2026

Use it every day

Honestly didn't expect to like it this much. Dedicated and serverless GPU endpoints is exactly what I needed, and broad catalog of open-source models. but I reach for it almost every day now and it just clicks.

Camille Laurent

Dec 7, 2025

Years in this space

I've evaluated a lot of these over the years. What stands out here is support for image and code models — handled better than most — and openAI-compatible API simplifies migration. Requires technical expertise to use effectively is my one real gripe. Worth the time if this is your use case.

Priya Nair

Aug 26, 2025

Use it every day

Honestly didn't expect to like it this much. Scalable training infrastructure is exactly what I needed, and openAI-compatible API simplifies migration. but I reach for it almost every day now and it just clicks.

Hiroshi Tanaka

Jul 22, 2025

Compared a few options

Evaluated this against two competitors. Where it wins: openAI-compatible SDK and endpoints and openAI-compatible API simplifies migration. On balance the feature set — especially inference API for open-source LLMs — justifies the 5 stars for our use case.

Gunnar Eriksson

Jun 22, 2025

Use it every day

Nadia Petrova

Jun 7, 2025

Solid for our team

We rolled this out across the team last quarter and competitive pricing for inference and training. Dedicated and serverless GPU endpoints fits neatly into how we already work, and inference API for open-source LLMs removed a step we used to do by hand. Performance varies across model selections, which is the main caveat, but it has held up under daily use.