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

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

Prelint

Paid

AI reviewer that checks every pull request against your ADRs, docs and past decisions to catch product drift before it ships.

Context-Aware Review: Reviews each PR against your ADRs, product docs and past decisions instead of only linting style or syntax.
Product Drift Detection: Flags conflicts, gaps and drift between the PR's intent and your product context inline in the diff.
Agent Self-Correction: The Prelint agent can self-correct against detected violations before a human reviewer opens the PR.
GitHub & GitLab Integration: Runs on both major hosted git platforms with native PR/MR integration.
Isolated Infrastructure: Every organization runs on isolated per-organization infrastructure and Prelint does not train on customer code.
Usage-Based Pricing: $1 per completed review with $10 in free credits, no seats or subscription.

AI-Written PR Triage: A team using coding agents like Copilot or Claude Code lets Prelint gate agent PRs against product intent before humans look.
Architecture Enforcement: A platform team encodes architecture decisions in ADRs and uses Prelint to catch PRs that violate them.
Onboarding Safety Net: A new engineer's early PRs get reviewed against product context they haven't fully internalized yet.
Open Source Maintenance: A maintainer of a public repo enables Prelint (free for OSS) to review incoming contributions for product fit.
Cost-Controlled Review: A startup wants automated review without paying per developer seat and pays $1 per PR instead.

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.