Fullstory vs Zero: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Fullstory and Zero — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Fullstory
FullStory
Behavioral data platform that records user sessions and surfaces sentiment and insights to improve product experience.
Key features
- Session Replay: Continuously records user sessions (clicks, scrolls, page transitions, input) into replayable, timestamped videos with direct links to review exact user interactions for debugging and UX analysis.
- Behavioral Event Capture: Captures rich event streams and structured custom events with properties and schemas, enabling teams to track actions, attributes, and conversion steps across web and mobile.
- SDKs & APIs: Provides official Browser, NodeJS, and mobile SDKs plus HTTP/server APIs for initializing capture, tracking custom events, creating users/events, and programmatic access to session and segment data.
- Searchable Segments & Event Querying: Lets users search and filter sessions by custom event properties, user attributes, and behavior to build segments of users who experienced specific flows or issues.
- Warehouse & Data Integration: Offers tooling and packages (including an official dbt package and export utilities) to move FullStory events into data warehouses and integrate behavioral data into BI workflows.
- Privacy Controls & Data Governance: Includes privacy and masking controls, allowlists/deny-lists, and per-session privacy settings to protect sensitive user data while still collecting actionable behavioral signals.
- Developer Tooling & Middleware: Supplies middleware (e.g., Segment middleware), SPA-friendly browser SDKs, and client/server libraries to simplify integration into single-page apps, mobile apps, and analytics stacks.
- Session replay capturing full user interactions (clicks, scrolls, inputs) for playback
- Custom event tracking with schema overrides and searchable events
- Browser SDK (@fullstory/browser) with init() and FullStory global/API (FS) usage
- Server-side NodeJS SDK for server-to-server HTTP API calls (users, events, batch import jobs)
- Mobile SDKs and mobile-specific APIs (iOS, Android, React Native)
- HTTP APIs and developer documentation for capturing data, retrieving sessions, and managing privacy
- Integrations and middleware (e.g., Segment middleware for Android) to forward events and embed replay links
- Data export and warehouse integrations (Anywhere: Warehouse) and a dbt package for downstream analytics
- Real-time activation integrations (Anywhere: Activation) for driving experiences
- Privacy controls and mobile-only methods (setting/removing attributes, resetting idle timer, class management)
Best for
- Bug reproduction and triage: Engineers and QA replay exact user sessions to reproduce errors, observe the sequence of actions leading to a bug, and correlate with console or custom event data.
- Conversion funnel optimization: Product teams identify where users drop off in multi-step flows by filtering sessions and events, then iterating on UX changes informed by real behavior.
- Product research and UX validation: Researchers observe real user interactions and sentiment patterns to validate hypotheses, discover friction points, and prioritize features or design changes.
- Analytics enrichment and BI: Data teams export FullStory events to warehouses using the dbt package and export utilities to join behavioral data with product and transactional datasets for deeper analysis.
- SPA and mobile instrumentation: Developers integrate the FullStory Browser SDK or mobile SDKs (and Segment middleware) to ensure accurate session capture in single-page apps and native mobile applications.
- Customer support augmentation: Support teams attach session replay links to tickets, quickly see what a user experienced, and provide faster, more context-aware resolutions.
- Feature rollout monitoring: Product and engineering monitor adoption and unexpected behaviors after releases by tracking custom events and segmenting affected sessions or cohorts.
- Product analytics and feature usage measurement via custom events and properties
- UX research and usability testing using session replay to reproduce user flows and issues
- Customer support and troubleshooting by locating sessions that match search criteria and viewing replays
- Conversion funnel analysis by correlating events and session behaviors
- Data engineering and analytics: exporting FullStory events to data warehouses and modeling with dbt
- Real-time personalization and activation by integrating FullStory session context into downstream systems
- Server-to-server automation for user and event management using the NodeJS SDK and HTTP APIs
Zero
Vercel Labs
An experimental graph-first programming language where agents edit a compiler-checked program graph instead of raw source text.
Key features
- 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.
Best for
- 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.