Draft vs Zero: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Draft and Zero — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Draft

InnoSage

Freemium

Local-first, block-based text editor for capturing, editing, searching, and sharing notes with privacy-focused hybrid storage.

Block-based Editor: Edit content as modular blocks that can be rearranged, edited, and composed to build structured notes and drafts.
Hybrid Storage Architecture: Local-first storage model that keeps data in the browser or local workspace by default, with managed remote storage options defined by the service terms.
Browser Extension Capture: A companion extension that captures web page content and AI-chat outputs directly into the Draft workspace for quick clipping and reuse.
Privacy-Focused Defaults: Default local storage and privacy-centric policies reduce cloud exposure of user text unless the user opts into remote features.
Searchable Workspace: Fast search across local content and captured items to quickly locate snippets, notes, and drafts.
Selective Sharing & Export: Controls to share or export content when the user chooses, enabling private drafting with optional collaboration or distribution.
Local-first storage: content stored in browser/local workspace by default
Hybrid Storage architecture combining local storage with optional remote/workspace options
Block-based text editing interface
Browser extension for capturing web or AI-chat content into Draft workspace
Search across stored content
Share functionality to export or share content when chosen

Research Capture: Clip excerpts, quotes, and web content via the browser extension into a local workspace for organized research and reference.
Drafting and Writing: Compose articles, blog posts, and longform drafts using block-based editing that simplifies rearranging sections.
Saving AI-Chat Outputs: Store and organize responses from AI chat sessions by capturing them directly into Draft for review and reuse.
Private Journaling: Maintain personal journals or notes stored locally for privacy-sensitive writing without default cloud exposure.
Offline-First Note Access: Work on notes and drafts offline with local browser storage, syncing or backing up only when the user opts in.
Controlled Sharing: Prepare and selectively share documents or export content when collaboration or publication is required.
Privacy-sensitive note-taking and drafting where local storage is required
Capturing and archiving snippets from web pages or AI chat sessions via browser extension
Research and writing workflows that need quick capture, edit, and search locally
Offline-first editing and content management in the browser

Vercel Labs

Free

An experimental graph-first programming language where agents edit a compiler-checked program graph instead of raw source text.

Graph as the Program: A compiler-owned semantic graph of symbols, calls, types, effects and node IDs is the source of truth, so agents reason over program structure rather than parsing and regenerating text.
Hash-Guarded Patches: Every edit carries an expected graph hash and expected field values, so a stale or conflicting patch is rejected before it reaches the store instead of silently corrupting the program.
Compiler in the Loop: Shape, type, stale-state and repository metadata checks run as part of applying a patch, collapsing the write-build-test-inspect cycle into a single checked operation.
Readable Text Projections: The graph renders to reviewable .0 source projections so humans can read diffs, audit what an agent changed and make rare manual edits.
Structured JSON Diagnostics: The compiler emits machine-readable diagnostics rather than prose error text, so agents can act on failures without parsing terminal output.
Explicit Effects via World: Side effects are passed through an explicit World capability parameter, making what a function can touch visible in its signature.
Runtime Constraints by Design: Targets token efficiency, low memory, fast startup, fast builds, low latency and zero dependencies rather than relaxing systems goals for agent ergonomics.
Query and Patch CLI: zero init, zero query, zero patch and zero run give agents a direct command surface over the graph, with agent skills carrying the graph discipline instead of rigid human prompts.

Reliable Agent Code Edits: Let a coding agent make semantic changes that are rejected outright if its view of the program is stale, instead of producing plausible-looking but broken text diffs.
Reducing Agent Token Spend: Query the specific symbols, types and nodes relevant to a task rather than feeding whole files into context on every turn.
Outcome-Driven Development: Describe a desired result in conversation — add auth, fix a failing route, build a CRM API — and review the resulting projection rather than writing the code.
Auditable AI-Written Code: Review what changed through readable .0 projections and graph hashes, keeping a human checkpoint over agent-authored programs.
Language and Tooling Research: Explore what a compiler and program representation look like when machine editors, not human typists, are the primary writers.
Sandboxed Experimentation: Prototype agent-driven codebases in an isolated environment where breaking changes and pre-1.0 churn are acceptable.