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

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

Prized

Freemium

Prized lets ops, support, and finance teams build secure internal tools with AI, using admin-approved company data connectors.

Natural-Language Tool Building: Describe an internal tool in a sentence and the agent writes the files, runs checks, and ships a working app connected to your live systems.
Admin-Approved Connectors: Administrators approve each data connector once and scope exactly what it can see, so every tool built afterward reuses that vetted connection instead of requesting fresh credentials.
Role-Scoped Access: Each deployed tool runs with its own role and grant list rather than blanket database access, limiting blast radius if a tool or user is compromised.
Workspace Audit Log: Every access is recorded — who ran which tool and what data it touched — giving compliance teams a full trail across all internal tooling.
Live Preview Sandboxes: Iterate on a tool in a sandboxed preview before publishing a new version to the team, so in-progress edits never touch production users.
Custom Domains for Every Tool: Deployed tools get their own address on your workspace subdomain, making them shareable internally like any other company app.
Credit-Based Usage Metering: Build and edit sessions draw from a monthly credit allowance shown live in the UI, while using an already-deployed tool never consumes credits.

Support Customer Lookup: Build a single view that searches by name, email, or order number and shows orders alongside open tickets so agents stop tab-hopping.
Finance Refund Approvals: Route refunds over a threshold into an approval queue with risk flags and an audit trail of every decision.
Rev Ops Billing Dashboard: Track MRR, subscriptions, churn, and revenue per user with 30-day, 90-day, and 12-month trend views.
Inventory and Stockroom Tracking: Monitor stock across multiple warehouses, flag SKUs below reorder point, and let ops staff update counts inline.
Renewal Risk Desk: Combine Salesforce, Postgres, and Zendesk data into a renewal dashboard that surfaces at-risk ARR and drafts follow-ups.
Replacing Shadow AI Tooling: Give teams already pasting company data into general AI chatbots a governed, audited place to build the same tools.

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.