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

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

Freemium

Non-destructive compression gateway that drops between coding agents and LLMs to cut input tokens by up to 85% without changing the agent.

Drop-In Gateway: One environment variable (ANTHROPIC_BASE_URL) reroutes your agent through Paritok — no agent, prompt, or tool changes.
Tool Schema Compression: 46-schema tool blocks (~29K tokens) drop to ~8K per turn by keeping relevant tools and stubbing the rest, frozen per conversation for cache stability.
Code-Native 4B Model: A 4B compression model trained on 45K real agent trajectories keeps identifiers, paths, and errors while shrinking file reads and outputs to ~26% of original.
read_original Recall: Every compressed segment is tagged; the agent asks read_original(ref) and gets the exact bytes locally without spending an extra turn.
Stale History Summarization: Turns beyond a configurable recent window get summarized once when your context budget fills, so recent turns stay pristine and overflows drop to zero.
Multi-Agent Compatibility: Works today with Claude Code, Cursor, Codex, OpenHands, and any OpenAI-compatible upstream — Anthropic and OpenAI both supported.
Compounding Savings: Saved share grows across a session — 25% at 1 turn, 54% at 10 turns, 63% at 20 turns — against a 96,500-token baseline.
Open Weights and Benchmark: SWE-bench Lite floor of 86.5% quality retained at 25.7% compression rate, with weights and training pipeline published.

MCP-Heavy Workflows: Cut input bills for agents that ship 70+ MCP tool schemas on every turn.
Long Coding Sessions: Run 3× longer coding-agent sessions before context saturation forces a hard compact.
Bill Reduction: Estimate 54% off input tokens on a 5-developer team at 20-turn Claude Sonnet sessions (~$6,550/year).
Self-Hosted Privacy: Route agent traffic through your own hardware with no data leaving your network — 8GB GPU is enough.
Enterprise Cost Governance: Add compression at the gateway layer so all coding agents on the team benefit without engineering per-agent.
Cursor/Codex/Claude Code Fleet: Standardize compression across a mixed toolchain of coding agents behind one gateway.