© 2026 AI-Buzz. Early access — data updated daily.
© 2026 AI-Buzz. Early access — data updated daily.
Open-source model inference platform. Fast and cost-effective.
Founded by: Ce Zhang, Kun Zhang, Yuandong Tian
Metrics computed from HN discussion, GitHub activity, and funding data.
According to AI-Buzz, Together AI ranks #8 in AI Infrastructure for HN discussion share (out of 70 tracked with 2.4% of HN discussion), with 59% positive developer sentiment (17 HN comments analyzed), with 151,797 npm downloads in 30 days, with 3,163,202 PyPI downloads in 30 days.
Source: https://ai-buzz.com/companies/together-ai?utm_source=citation&utm_medium=referral&utm_campaign=cite_this_data
Metrics derived from public APIs (HN Algolia, GitHub, npm/PyPI). Sentiment classified by AI. See methodology for details →
Description
Open-source model inference platform. Fast and cost-effective.
Estimated Company Size
51 - 100 employees
Website
together.aiFounded
2022
Description
Together AI provides fast inference for open-source models. Powers thousands of AI applications. Raised $225M Series A at $1.25B valuation.
Community engagement metrics that indicate developer traction and interest.
Last updated: 1 day ago
Mentions in HN discussions. Source: Hacker News Algolia API.
Sentiment analysis of Hacker News comments only. Does not include Reddit, Discord, or other platforms.
Total stars indicate project popularity and developer adoption.
Forks indicate active developer engagement and contribution interest.
Package download volume indicates real-world adoption and integration into production projects.
Provides fast and cost-effective inference for a wide range of open-source large language models and generative AI models.
Enables users to fine-tune open-source models with their own data for specialized tasks and improved performance.
Offers access to various embedding models for tasks like semantic search, retrieval-augmented generation, and data analysis.
Provides on-demand access to GPU clusters and optimized infrastructure for training and running large AI models efficiently.
An interactive web-based environment for users to explore, test, and compare different open-source models.
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