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

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

BaseRT

Base Compute

Free

BaseRT is a high-performance LLM runtime for Apple Silicon that runs open-source models locally, faster than llama.cpp and MLX.

Apple Silicon Optimized Runtime: A native inference engine tuned for M-series chips that outperforms llama.cpp and MLX on decode and prefill benchmarks.
One-Line Install: BaseRT ships as a single curl-piped install script, so users can go from download to serving a model in seconds.
Broad Open-Source Model Support: Runs Qwen3, Llama 3.1/3.2, Gemma 3/4, Mistral, Phi-3, and Nomic BERT out of the box, with quantized (Q4/Q8) weights.
Local Serving for Coding Agents: `basert serve <model>` exposes a local endpoint that pairs with the pi plugin so coding agents run fully on-device with no API keys.
Privacy by Default: All inference happens on the user's machine, so prompts, code, and outputs never leave the device.
Benchmark-Driven Performance: Publishes tokens/sec comparisons on Apple M5 Pro against MLX and llama.cpp for reproducibility.

On-Device Coding Assistant: Engineers pair BaseRT with a local coding agent to get autocomplete and refactoring without sending source code to a cloud API.
Private Model Evaluation: ML practitioners benchmark open-source models on their own laptop without renting GPUs or exposing test data.
Offline LLM Applications: Developers ship desktop apps that call a locally served model, avoiding rate limits and per-token costs.
Prototyping on Apple Silicon: Researchers experiment with new quantizations and open-weight models on M-series Macs at high throughput.
Enterprise On-Prem Inference: Teams with data-residency constraints run production inference on employee devices instead of external APIs.

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.