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

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

siift

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.

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.