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

A side-by-side comparison of is.team and Local — 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.

Base Compute

Free

A macOS app that runs chat, coding and meeting AI entirely on your own Mac, with no cloud, no account and no per-token cost.

BaseRT Chip-Tuned Engine: Base Compute's inference runtime compiles for your specific Apple silicon on first launch, claiming up to 5.4x more tokens per second than engines other local apps ship.
Privacy Mode: Every request runs on the Mac and no data leaves the device — memories are stored locally, and facts marked sensitive are pinned to the machine permanently.
In-Folder Agentic Coding: Point Local at a project and it reads, edits and runs code in place without ever uploading the codebase.
On-Device Meeting Transcription: Meetings are transcribed locally with speakers labelled, so recordings and transcripts never reach a third-party service.
Memory You Can Edit: A short, fully visible list of lasting facts about you that you can review, edit or delete, rather than an opaque profile.
Memory-Aware Model Recommendations: Local reads your chip and RAM (8-16 GB, 24 GB, or 32-128 GB tiers) and suggests which open models will actually run well.
Office Mode: Serve the largest model from your fastest machine — Mac Studio, AMD Strix Halo, NVIDIA DGX Spark or an on-prem server — and reach it from Local on every laptop in the office.
Cost and Speed Analytics: A dashboard showing tokens processed, per-model throughput, and the equivalent cloud API cost you avoided.

Confidential Document Review: Drop a contract or PDF into chat and get a summary without the file ever touching a cloud provider.
Regulated-Industry Coding: Work agentically inside a proprietary codebase at a firm whose policy forbids uploading source to external AI services.
Private Meeting Notes: Transcribe internal calls with speaker attribution while keeping the audio on the laptop.
Zero-Marginal-Cost Experimentation: Run heavy prompt iteration without watching a per-token meter, since inference happens on hardware you already own.
Small-Office AI Server: Host a large open model on the one powerful Mac in the office and let the whole team query it from their own laptops.
Offline Fieldwork: Keep a full chat and coding assistant available on a flight or at a site with no reliable connectivity.
Hybrid Frontier Access: Keep everyday work local and connect your own OpenAI or Anthropic key only for the rare job that needs a frontier model.