© 2026 AI-Buzz. Early access — data updated daily.
© 2026 AI-Buzz. Early access — data updated daily.
Enterprise-focused LLM company. Strong in embeddings and RAG.
Founded by: Aidan Gomez, Nick Frosst, Ivan Zhang
Metrics computed from HN discussion, GitHub activity, and funding data.
According to AI-Buzz, Cohere ranks #16 in Foundation Models for HN discussion share (out of 33 tracked with 0.2% of HN discussion), with 54% positive developer sentiment (13 HN comments analyzed), with 1 commits/week (steady), with 1,598,548 npm downloads in 30 days, with 14,688,154 PyPI downloads in 30 days.
Source: https://ai-buzz.com/companies/cohere?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
Enterprise-focused LLM company. Strong in embeddings and RAG.
Estimated Company Size
251 - 500 employees
Website
cohere.comFounded
2019
Description
Cohere builds LLMs for enterprise. Strong in embeddings, search, and RAG applications. Co-founder Aidan Gomez co-authored the Transformer paper. Raised $270M Series C.
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.
Weekly commit activity indicates ongoing development momentum and team productivity.
Package download volume indicates real-world adoption and integration into production projects.
Cohere's $240M year sets stage for IPO
Cohere surpassed $240 million in annual recurring revenue in 2025, highlighting strong enterprise AI demand as the Canadian startup positions itself for a potential IPO amid intensifying competition from OpenAI and Anthropic.
Elon Musk is getting serious about orbital data centers
We’re starting to see the idea of Musk-owned orbital AI data clusters cohere into an actual plan.
A powerful large language model designed for a wide range of enterprise applications and business use cases.
Generates high-quality vector embeddings for text, enabling advanced search, retrieval-augmented generation (RAG), and semantic understanding.
Optimizes search results and improves the relevance of retrieved documents for RAG applications by reordering them.
Enables the development of robust conversational AI experiences and chatbots for enterprise use.
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Package downloads and ecosystem metrics — 30-day window
Insufficient data for trend chart
Need at least 2 data points (currently have 1)
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