Jasper's core is a language model told who you are: a brand voice, saved facts and an audience, wrapped in templates for posts, emails and ads. One person gets the same effect from an app that stores those three things, adds the right ones to each request and streams the draft into an editor, and an agent builds that in one sitting against any LLM API. What stays behind is the team layer: shared voices with permissions, bulk generation from a table, research agents, and the connectors into Word, Google Drive and Workfront. Plenty of reviewers also point out that ChatGPT with custom instructions already covers much of the solo case.
Before you buildJasper has no free plan, only a 7-day trial. If you write alone, a saved style sheet in ChatGPT or Claude custom instructions may already be enough.
Build me a local writing studio to replace Jasper. Requirements: - Node.js 22 + TypeScript: a Fastify server and a React editor built with Vite on TipTap. `npm start` serves everything on http://localhost:4600. - LLM: any OpenAI-compatible endpoint through the `openai` SDK, configured in .env (LLM_BASE_URL, LLM_API_KEY, LLM_MODEL). Stream drafts to the editor over server-sent events and let me cancel a run. - Storage: better-sqlite3 in ./data/studio.db for brand voices (description, do and don't rules, banned words, up to five sample texts), audiences, knowledge notes, documents and every generation with its inputs. - Knowledge: paste text or drop .md, .txt or .pdf files (`pdf-parse`); split into chunks of about 300 words and retrieve the best 4 per request with SQLite FTS5 (bm25), no embeddings. - Templates: YAML files in ./templates (blog outline, LinkedIn post, product description, cold email, three ad variants, meta description, press release) with named inputs and instructions; adding a file adds a template without a rebuild. - Each request is the chosen voice + audience + retrieved chunks + template. Show the exact prompt in a "what was sent" panel and keep it with the document version. - Voice check after each draft, with no extra LLM call: banned words found, average sentence length against the samples, and every sentence containing a number so I can verify it. - Export Markdown, HTML and .docx (`docx`); every document keeps its last 20 versions. - Single user. No accounts, no telemetry, local-first: only the LLM call leaves the machine, and the README must say exactly which text is sent. - Out of scope: teams and permissions, bulk generation from tables, image generation, a browser extension, SEO scoring. - README: setup, the .env keys, where the data lives and how to see what the model API costs.
Opens your agent with the prompt filled in, you press enter. Or copy it and paste it into an empty folder.
The first prompt builds the core. Run these one at a time in the same folder to grow it into your own Jasper.
Add voice import to the writing studio in this folder. Requirements: - A command `studio import-voice <name> <folder|url-list|feed-url>` that reads Markdown files, a list of URLs or an RSS feed, extracts the main text (`@mozilla/readability`) and keeps the 5 best samples by length and variety. - Compute simple style facts: average sentence length, paragraph length, share of questions, most used openers and the 30 most characteristic words; store them with the voice. - Show the facts in the voice editor and add them to the prompt as short rules. - Never fetch more than 20 pages and wait 1 second per host. Update the README.
Add selection rewrites to the writing studio in this folder. Requirements: - In the editor, selecting text shows a menu: shorter, longer, simpler, more direct, and a free-text instruction. - The request uses the document's voice and audience and sends the selection plus 300 characters of context on each side; the result replaces the selection and can be undone. - Record each rewrite in the generation history with the prompt that was sent. - Update the README.
Add a bulk mode to the writing studio in this folder. Requirements: - `studio bulk <template> <file.csv>` runs one generation per row, mapping CSV columns to template inputs, with 3 concurrent requests and a retry on rate limits. - Write results to an output CSV and show them as a table in the UI with accept and reject buttons. - Stop when a spend limit of MAX_RUN_TOKENS from .env is reached and say how many rows are left. - Update the README.
Marketing teams want on-brand copy without everyone learning prompt craft. Jasper keeps the voice, facts and audience in one place, so a junior writer gets a decent first draft from a template and the manager sees the same setup used across projects.
Estimate · Runs on your machine; the running cost is model tokens and depends on how much you write. The build is token usage on your agent plan.
| Plan | Monthly | Annual, per month | What you get |
|---|---|---|---|
| Pro | $69 | $59 | Canvas and agents for core marketing workflows, 2 Brand Voices, 5 Knowledge assets and 3 Audiences; chat and Canvas use no credits. |
| Business | custom | — | Everything in Pro plus agents for GEO, translation and deep research, a no-code agent builder, Jasper Grid, unlimited Brand Voices, Knowledge and Audiences, API access, admin controls and groups, and a dedicated account manager. |
No free plan; Pro has a 7-day free trial.
Yes. An AI coding agent such as Claude Code, Codex or Cursor can build a usable personal version in one session with the prompt on this page, running on your own machine.
About $69 a month on the Pro plan per seat, $828 a year (checked 2026-10-19).
Shared brand voices, audiences and knowledge with roles for a team; Jasper Grid for generating content in bulk from a table; Agents for deep research, GEO and translation on the Business plan; The Word add-in and exports to Google Drive, SharePoint, Box and Adobe Workfront; A hosted app that stays up and gets new templates without your effort. If any of those matter to you, keep paying.
Yes: Open WebUI (open source), LibreChat (open source), AnythingLLM (open source).