Replace Grammarly and Notion AI with a Local LLM (2026)

Quick Facts

• Grammarly cost: Free (100 AI prompts/month), Pro $12/month billed annually, $30 month to month (2,000 AI prompts/month), Enterprise custom.
• Notion AI cost: included in the Business plan (about $20/user/month billed annually); Free and Plus plans get a trial only.
• Local LLM cost: Free (open-source models + Ollama) — hardware electricity only.
• Small-model starting points: Qwen3.5 4B (qwen3.5:4b) or Phi-4 Mini (phi4-mini) on 8 GB; Gemma 4 12B (gemma4:12b) on 16 GB; Qwen3.8 27B (qwen3.8:27b) on 24 GB and up.
• Grammarly privacy: Terms of Service Section 7.2 licenses your content for operating, improving, and developing Grammarly's services.
• Notion AI privacy: requests go to third-party model providers; by default neither Notion nor its providers train on customer data.
• Obsidian cost: free for personal and work use since February 2025.

What You Are Replacing (and What You Are Not)

Replacing Grammarly: Grammar and Style Correction

• System prompt for writing sessions: set your local AI app's system prompt to "You are a professional editor. Return only corrected or rewritten text — no preamble, no explanation, no commentary." This prevents the model from adding "Great text! Here is my correction..." before the output.
• Keyboard shortcut integration: use Raycast (macOS) or AutoHotkey (Windows) to create a hotkey that sends selected text to Ollama and pastes the result. This reduces the copy-paste workflow to a single keystroke. Note that Raycast lists local-model support as a Pro feature in its manual; an AutoHotkey script calling the Ollama API is free.
• Grammar-only vs. style rewrite: use separate prompts for grammar-only and full style rewrites. Grammar-only is safer for legal, technical, or structured documents where changing phrasing could change meaning. Style rewrite is appropriate for emails, blog posts, and general correspondence.
• For academic writing: add "Preserve all citations, technical terms, and domain vocabulary unchanged" to the style rewrite prompt. Without this instruction, models will sometimes simplify or paraphrase technical language.
• For business email: add "The sender is [Name], [Role] at [Company]. The email should reflect their professional voice without personalisation details in the output." This anchors the register to the sender's professional context.
• Analyse your own style locally: the style-profile prompt above turns three writing samples into a reusable system prompt. Because the samples never leave your machine, it is safe to use on private drafts, which a cloud assistant would first have to receive.

Best Small LLMs for Local Grammar Correction

• No grammar-specific benchmark ranks these models. Vendors publish general instruction-following results, not grammar-correction accuracy. Judge by your own text.
• What to check in the test: over-correction (wording that was fine got changed), changed meaning (numbers, names, negations), kept spelling variant (British vs. American), and whether the model returns only the text.
• Languages: Qwen3.5 and Gemma 4 are multilingual; Phi-4 Mini is aimed mainly at English. For non-English text, test in your language first.
• Keep it deterministic: a low temperature (around 0.2) reduces creative rewording. The Modelfile below bakes the editor instructions and temperature into a reusable model.
• Long documents: small models drift on long inputs. Correct section by section instead of pasting a whole document.

FROM qwen3.5:4b
PARAMETER temperature 0.2
SYSTEM """You are a copy editor. Fix grammar, spelling, and punctuation only. Keep the author's wording, meaning, and spelling variant. Return only the corrected text."""

# then run:
#   ollama create grammar-fix -f Modelfile
#   ollama run grammar-fix

Google Docs, Gmail, and Inline Checking Without the Cloud

• Google Docs workflow: turn on Suggesting mode in Docs, select a paragraph, send it through your hotkey or sidebar, and paste the corrected text. Suggesting mode keeps every change reviewable.
• Harper in Google Docs: Harper's contributor documentation says Docs support was built through a workaround, because Docs draws text on a canvas instead of an editable page element, and describes the extension as still early-stage. We have not tested it in Docs — try it on a copy of a document first.
• Gmail: Harper's site notes that Gmail is unsupported unless Gmail's built-in grammar checker is turned off. Use the select-and-rewrite workflow for email drafts.
• Best combination: a rule-based checker for inline underlines in editors it supports, plus a local LLM for rewrites, tone, and summaries. This is the closest local match to Grammarly's mix of underlines and AI rewriting.
• Self-hosting LanguageTool: download the LanguageTool server, start it on localhost, then choose "Local server" under Experimental settings in the browser add-on. The server is reachable only from your own machine unless you start it with the public option.

Replacing Notion AI: Document Drafting and Notes

• Install Obsidian from obsidian.md. Free for personal and work use. Create a vault for your notes — this is the directory that the AI plugins will index.
• Install the Copilot plugin (Community Plugins → search "Copilot"; see the Copilot for Obsidian review). In plugin settings, select "Ollama" as the LLM provider, enter http://localhost:11434 as the base URL, and select your model. Copilot adds a chat sidebar to Obsidian where you can ask questions and generate content in context of the current note.
• Install the Smart Connections plugin for Q&A over your full vault. Smart Connections indexes your notes as embeddings with a local embedding model and lets you ask questions that retrieve relevant notes before sending to the LLM. This is the direct Notion AI "ask about my notes" replacement.
• Document drafting: in the Copilot chat, type "Draft a [document type] about [topic] based on these notes: [paste key points]". The plugin includes the current note context automatically. Output appears in the chat; copy-paste into the note.
• Content expansion: select a bullet-point outline in the note, open the Copilot command palette, and use "Expand selection" — the model converts the outline to prose in the note's writing register.
• Weekly review generation: "Summarise my notes from this week into a weekly review format: wins, blockers, and next actions." Smart Connections retrieves notes from the last 7 days and passes them to the LLM automatically.

Notion and Ollama: What Connects and What Does Not

• Option 1 — script through the Notion API: a small script reads a page through the Notion API, sends the text to Ollama on localhost:11434, and writes the result back as a new block. The model step is local; the page itself still lives on Notion's servers, so only the AI provider is removed.
• Option 2 — Notion MCP: Notion hosts an MCP server (OAuth sign-in) that MCP clients use to read and update pages. Notion names Claude Code, Cursor, and Codex as example clients; whether a given client can drive a local model is a question for that client.
• Option 3 — move AI-heavy notes to a local-first app: Obsidian (plain Markdown files) with Copilot, as above, or AppFlowy, an open-source (AGPL-3.0) Notion-style workspace with an offline mode and local AI options per its comparison page. Check AppFlowy's documentation for the exact local-model setup.
• What a local model does not change: content already in Notion is stored by Notion. Running a local model over it removes the AI-provider step, not Notion's storage.
• What Notion says about its own AI: by default neither Notion nor its AI providers use customer data to train models; on Enterprise, most models offer zero data retention, while on other plans providers retain data for up to 30 days.

import requests

NOTION = {"Authorization": "Bearer <NOTION_TOKEN>", "Notion-Version": "2022-06-28"}
PAGE_ID = "<page-id>"

# 1. read the page text through the Notion API
blocks = requests.get(f"https://api.notion.com/v1/blocks/{PAGE_ID}/children", headers=NOTION).json()["results"]
text = "\n".join(
    "".join(t["plain_text"] for t in b["paragraph"]["rich_text"])
    for b in blocks if b["type"] == "paragraph"
)

# 2. rewrite it with a local model (Ollama on localhost)
r = requests.post("http://localhost:11434/api/generate", json={
    "model": "gemma4:12b", "stream": False,
    "prompt": "Correct the grammar of this text. Return only the corrected text.\n\n" + text,
})
fixed = r.json()["response"]

# 3. append the result as a new paragraph block
requests.patch(f"https://api.notion.com/v1/blocks/{PAGE_ID}/children", headers=NOTION, json={
    "children": [{"object": "block", "type": "paragraph",
                  "paragraph": {"rich_text": [{"type": "text", "text": {"content": fixed[:2000]}}]}}]
})

Integration Options

Model Recommendations for Writing Tasks

Privacy Comparison

• Grammarly data licence: under Section 7.2 of Grammarly's Terms of Service, you keep ownership of your content but grant Grammarly a worldwide, non-exclusive, royalty-free licence to use it for stated purposes, including operating, providing, and improving its services and developing new products or features. The Terms also say accepting or rejecting suggestions may help train its suggestion engine. Grammarly states that its LLM partners are not allowed to use customer data to train their models.
• Notion AI data flow: Notion routes AI requests to a mix of model providers (the current list is on its subprocessor page). Notion states that by default neither it nor its AI providers use customer data to train models. Enterprise offers zero data retention with LLM providers; on other plans, providers retain data for up to 30 days.
• Local LLM data flow: no external transmission. Ollama listens on localhost by default and inference makes no outbound connection. The model weights are static files on disk; your text is processed in memory.
• GDPR / professional privilege implications: legal professionals, medical practitioners, and anyone subject to confidentiality obligations should check contractual protections before using Grammarly or Notion AI for client-related content. A local LLM avoids the third-party processor question because no data leaves the machine.
• Telemetry: cloud tools collect usage and interaction data in addition to the text itself. Local apps differ — check each app's telemetry settings, and if you need audit-grade assurance, monitor outbound traffic yourself.

Cost Comparison

Common Mistakes

• No system prompt for output format. Without a system prompt, models prefix corrections with "Certainly! Here is the corrected text:", add explanations, and use AI-register phrasing. Always set a system prompt that specifies "return only the corrected text".
• Using the smallest model for complex style rewrites. Small models handle grammar correction well but tend to produce more formulaic style rewrites. For style-heavy work, move up to Gemma 4 12B or larger.
• Expecting Notion UI equivalence from Obsidian. Obsidian is a Markdown editor, not a database. If your Notion workflow depends on databases, views, and relations, Obsidian is not a full Notion replacement — only the AI features transfer. Evaluate whether the database features are critical before switching.
• Not setting a word ceiling on style rewrites. Without a ceiling, the model pads rewrites. Add "Keep the rewritten text within 10% of the original word count" to any style rewrite prompt.
• Sending full documents to a small model. Small models drift on long inputs. For long documents, break them into sections and correct each section independently.
• Expecting inline underlines from an LLM. An LLM answers when asked; it does not watch your typing. For underlines as you type, add a rule-based local checker such as Harper.
• Assuming Notion AI can use a local model. Notion does not document support for it; use the Notion API with Ollama, or move AI-heavy notes to a local-first app.

Sources

• Grammarly Terms of Service — Section 7.2 (licence to user content) — grammarly.com/terms
• Grammarly plans — grammarly.com/plans
• Grammarly Pro pricing (monthly, quarterly, annual) — support.grammarly.com
• Grammarly responsible AI (partner data use) — grammarly.com/responsible-ai
• Notion pricing and AI plan access — notion.com/pricing
• Notion AI security practices (providers, training, retention) — notion.com/help/notion-ai-security-practices
• Notion MCP documentation — developers.notion.com/docs/mcp
• Obsidian free for work (February 2025) — obsidian.md/blog/free-for-work
• Harper grammar checker — writewithharper.com
• Harper Chrome extension notes (Google Docs support) — writewithharper.com/docs/contributors/chrome-extension
• LanguageTool HTTP server (self-hosting) — dev.languagetool.org/http-server
• Raycast local models — manual.raycast.com/ai/local-models
• Ollama model library — ollama.com/library/gemma4, ollama.com/library/qwen3.5, ollama.com/library/qwen3.8
• Obsidian Copilot plugin documentation — GitHub: logancyang/obsidian-copilot
• Obsidian Smart Connections plugin — GitHub: brianpetro/obsidian-smart-connections

Frequently Asked Questions

Related Reading

• Local LLM with Obsidian — full Obsidian + Ollama setup for note-taking and Q&A over your vault.
• Copilot for Obsidian Review — the Copilot plugin used in the Notion AI replacement workflow.
• Obsidian and Logseq AI Plugins Compared — plugin options for local AI in note-taking apps.
• Small Local LLM Models — current small models for 4–8 GB machines, relevant to local grammar correction.
• Local LLM Email and Calendar Automation — automating email drafting and calendar management with local LLMs, complementing the writing assistant setup.
• Local LLM Prompts for Fiction Writers — prompt templates for creative writing tasks that go beyond grammar correction into scene-writing and character development.
• Replace Zapier with Local AI Agents — local workflow automation that connects writing tools to other business processes.
• Local RAG for Private Business Data — Q&A over private business documents, extending the note Q&A use case to larger document libraries.
• Easiest Local AI App for Windows, Mac, and Linux — if you need to set up your first local AI app before implementing the writing assistant workflow.
• Local AI Agents for Business Workflows: EU Compliance — GDPR and EU AI Act context for deploying local AI writing assistants in European business settings.