Local vs siift: Features, Pricing & Which Is Better (2026)

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

Local

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.

Freemium

An agentic AI operating system that helps founders map, validate and execute business strategy on one intelligent canvas.

Intelligent Business Canvas: A visual workspace that maps ideas, assumptions, actions and results into decision-ready filters so the whole business can be seen at once.
Living Memory System: A scalable agentic memory that learns as the business evolves and keeps context aligned across tools, data and teammates.
AI-Scored Validation: Automated, continuous research that grades assumptions into evidence so founders know what is validated and what is still risky.
Five-Stage Execution Loop: Guided progression through Ideate, Validate, Build, Go To Market and Scale, each with its own AI-driven workflow.
Safe Stack Automations: Human-in-the-loop actions across 80+ popular applications so approved work executes without leaving the canvas.
Shareable Workspaces: Collaborative views that let teammates, advisors and other stakeholders work from the same strategy context.
Proactive Next-Step Guidance: Personalized, iterative advice that surfaces the highest-leverage action rather than a generic checklist.
Credit-Based AI Usage: Monthly request credits scaled by plan and weighted by task complexity, with unlimited projects even on the free tier.

Idea Validation: De-risking a new business concept by turning founder assumptions into automatically researched, scored evidence before building.
Strategy Mapping: Turning a cloud of unstructured ideas into a visual mind-map that exposes blindspots across the business model.
Go-To-Market Planning: Iterating on sales and marketing with an AI-native loop that tests which channels actually produce revenue.
Product Prioritization: Helping product leaders decide what to build next based on verified market opportunities rather than intuition.
Scaling Diagnostics: Systematizing an existing business to surface its current growth constraints and reverse-engineer fixes.
Advisor Collaboration: Sharing a single live strategy workspace with co-founders, advisors and investors instead of static decks.