is.team vs Zero: Features, Pricing & Which Is Better (2026)

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

is.team

IS.TEAM LLC

Freemium

An infinite-canvas project board where AI coding agents connect over MCP, subscribe to cards and reply in chat alongside the team.

MCP Agent Boards: Claude, Cursor and ChatGPT connect over MCP, subscribe to a board and reply in card chat while they work, so agents behave like teammates rather than external tools.
Infinite Canvas Workspace: Tasks, notes and planning share one zoomable surface, replacing separate tracker, whiteboard and chat tools.
AI Workflow Planner: Generates and sequences the work for a board so a project can be broken down without manual ticket writing.
AI Card Assistant: A per-card helper that drafts, summarizes and answers questions inside the context of a single task.
Meeting Note Taker: Captures meeting notes using one-time workspace credits and extracts actionable tasks straight onto the board.
Per-Workspace Pricing: A flat workspace fee covering up to 15 seats on the Pro plan, so adding an engineer never triggers a surprise invoice.
Integrations and Webhooks: HMAC-signed webhooks plus Zapier and Make connections, with API access and LLM API tokens on higher tiers.
Real-Time Collaboration: Live multi-user editing with voice chat, screen sharing, sprints, time tracking and a timeline view.

Agent-Assisted Development: Letting a coding agent pick up a card, do the work and report progress in the same thread the team is reading.
Tool Consolidation: Replacing a Jira, Slack and Miro combination with a single canvas for engineering leads tired of context-switching.
Small Team Planning: Running sprints, timelines and time tracking for a startup team on a flat monthly workspace fee.
Meeting-to-Backlog Workflow: Turning recorded meeting notes into extracted, assigned board tasks without manual transcription.
Automated Intake: Collecting work through embeddable forms that create cards automatically on the right board.
Cross-Tool Automation: Wiring board events to Zapier or Make through signed webhooks so downstream systems stay in sync.

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.