Obsidian AI

Obsidian AI is the official AI integration for the Obsidian knowledge-base app, launched as an add-on to the core Markdown vault in 2023. It was introduced to let users leverage large language models without divorcing their AI workflow from their notes. The core value proposition is vault-aware generation: AI responses are explicitly rooted in the content of your vault, and outputs can be inserted back into notes as Markdown with links. Obsidian positioned this feature to balance productivity gains with data control, offering both local model execution and optional cloud API access so users choose where inference runs.

Feature-wise, Obsidian AI provides vault-aware Q&A that searches your selected scope (current note, folder, or entire vault) and surfaces cited excerpts inline. It offers a Write mode that generates or expands paragraphs directly into the editor, with a prompt UI to tweak length and tone. The Summarize tool creates condensed note summaries and extractive highlights, useful for turning long notes into bullet overviews. On the model side, Obsidian AI supports routing to OpenAI models (GPT-4, GPT-3.5) via API keys, Anthropic models where supported, and local LLMs compatible with Llama.cpp or the Obsidian Local Model path, letting users run inference offline for privacy.

Pricing is split between free/local usage and paid cloud access. There is a free option that permits the use of local models and basic AI features inside your vault, though cloud-model calls are disabled. The paid tier (priced at approximately $8/month or an annual equivalent) unlocks routing to OpenAI/Anthropic APIs from within the Obsidian UI and removes per-request gating; exact billed amounts may vary and enterprise purchasers negotiate custom plans. Teams and organizations can opt for business/enterprise arrangements (custom pricing) that include admin controls and centralized billing—verify current prices on Obsidian’s site as fees can change.

Obsidian AI is used by knowledge workers who need AI that understands their private notes: researchers use it to summarize literature and generate annotated bibliographies, and product managers use it to convert meeting notes into action items and RFC drafts. Two concrete examples: a research scientist using Obsidian AI to produce 300–500 word literature summaries per paper, and a product manager generating prioritized feature lists from meeting notes. Compared to competitors like Notion AI or Logseq’s AI plugins, Obsidian AI’s material advantage is vault-first privacy and optional local model execution, making it preferable where local control matters.