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

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

Premation

Freemium

Professional motion design studio with WebGPU rendering, rigging, and an AI assistant that edits your timeline through natural language.

WebGPU Deterministic Rendering: Preview and final export are rendered by the same frame loop, so the approved frame is bit-identical to the shipped frame and re-renders reproduce identical output.
Graph Editor with Keyframe Assistants: Edit value and velocity curves directly, with linear/ease/hold/custom bezier easing and independent X/Y position tracks.
2.5D Cameras and Lights: Promote any layer to 3D for Z position and rotation, with one- and two-node cameras, depth of field, cone-angle lights, and shadows editable in orthographic views.
Per-Glyph Text Animators: A real selector stack animates position, scale, rotation, opacity, tracking, or colour across characters, words, or lines with range and wiggly selectors.
Rigging with Bones and Puppet Pins: Forward kinematics with FABRIK IK and linear blend skinning, plus an ARAP puppet-pin solver on triangulated meshes.
AI Assistant Through Tool Registry: Describe motion in natural language and the assistant inserts layers, sets keyframes, and staggers timing through the same commands the UI uses.
Multi-Format Export Pipeline: One deterministic loop feeds MP4 H.264, WebM VP9+alpha, MOV ProRes 4444, GIF, PNG sequences, Lottie, and image sequences, with alpha-channel support where applicable.
Nestable Compositions: Multiple compositions per project, each with its own size, frame rate, and duration, nestable as sealed pre-comps with time remapping.

Title Card Production: Compose, animate, and export ten-second title cards without round-tripping between four separate applications.
Motion Design Studios: Deliver client work with deterministic re-renders that resume identical frames after crashes and produce identical output on two machines.
Character Rigging Shorts: Rig flat artwork with bones and IK plus puppet pins to animate characters or mascots for social and product videos.
Explainer Video Production: Build multi-layer explainers with cameras, lights, per-glyph text, and deterministic particles that reproduce exactly on re-render.
Web-Delivered Animations: Export vector Lottie JSON alongside raster WebM+alpha for the same shot from one project.
AI-Assisted Motion Iteration: Prompt the assistant to stagger cards, add cameras, or animate text and undo instantly if the result isn't right.

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.