Causal vs dif.sh: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Causal and dif.sh — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Causal
Causal Software Limited
An infinite AI canvas for creative planning, where notes, files, images and links sit in one spatial workspace an agent can read and build on.
Key features
- Infinite Spatial Canvas: A freeform, unbounded board where notes, images, links and files are arranged by meaning, so layout itself becomes the organisation rather than a folder hierarchy.
- Context-Aware Agent: The AI reads the whole canvas and understands how ideas connect, then answers questions and researches topics with the surrounding board as context.
- Native Output Generation: Prompts are turned into canvas content directly, with the agent creating notes, files and web-link cards and placing them where they belong instead of returning plain text.
- Rich File Previews: PDFs, Word and Adobe documents, markdown, spreadsheets, images and video up to 20 MB open fullscreen in-app, and markdown and CSV files can be edited in place and saved back to the file.
- Dual Text Editing: Quick notes live directly on the canvas while longer pieces open into a full-page editor, both sharing headings, lists, checkboxes, quotes, code blocks, highlights, images and links.
- Structure Tools: Collections pack related nodes into tidy columns, nested canvases give a sub-topic its own space, and an unsorted tray parks anything not ready to be placed.
- One-Click Sharing: Any canvas becomes a read-only link that recipients open without an account, covering nested canvases too, and sharing can be revoked at any time.
- Template Library: Ready-made boards for app flows, app plans, brand research, branding boards, competitor research, onboarding, storyboards, video briefs and plans, website moodboards and website plans.
Best for
- Product Planning: Map every screen in an app and the routes between them, then keep features, screens and shipping order in one view instead of three separate documents.
- Brand Development: Collect the brands, palettes and voices you are borrowing from, then settle type, colour and marks in one place the whole team works from.
- Competitive Research: Put rival products side by side with your own on a single board and find the gap you can actually take.
- Video and Film Pre-Production: Block out a shoot frame by frame, hand an editor references, tone and deliverables on one canvas, and follow a video from script to final cut with every asset attached to its step.
- Website Design Prep: Gather reference sites, type and colour a build should feel like, then lay out every page and its contents before the first component is built.
- Team Onboarding: Walk a new starter through the tools, files and people one frame at a time on a shareable board.
dif.sh
Dif
Feature flags and A/B tests defined as markdown files in your repo, with a generated context file coding agents read on session start.
Key features
- Markdown-Defined Experiments: One .md file per flag or test holds id, status, owner, surface, hypothesis, audience, variants, metrics and guardrails in frontmatter, with the brief and rationale written below it.
- Agent Context File: Every dif build regenerates dif/context.json listing active flags, experiments, variants and recent learnings, which coding agents pick up on session start.
- Build-Time Conflict Detection: The build resolves an exclusion graph and refuses to compile when two live tests would bucket one user into both, so clashes break in CI instead of production.
- One Shape for Four Concepts: Feature flags, A/B tests, holdouts and staged rollouts are the same file format with different frontmatter, reducing the number of concepts and failure modes.
- Generated Typed Client: dif build emits a small typed client you import once at boot, then call per flag with control and variant branches — supported for web server, React and Svelte.
- Privacy-Preserving Targeting: Audience attributes such as country, plan or returning_visitor are declared in config.yaml while values arrive at runtime from your app's user context, so no customer data is committed.
- Structured Conclusion Workflow: dif conclude archives a finished experiment, drafts its Decision block and appends a line to the surface log so the next test on that screen starts informed.
- Flexible Result Routing: Add a Dif Cloud key and dif.track() computes lift with no join code, or run dif init --events custom to own the handlers that forward events to Segment, Amplitude or your warehouse.
Best for
- Keeping Experiments in Code Review: Ship flag and experiment changes through the same pull request flow as the code they gate, using git history as the audit trail.
- Giving Coding Agents Experiment Context: Let an AI coding agent see which experiments are live on a surface and what prior tests taught before it edits that screen.
- Preventing Overlapping Tests: Use exclusion groups to guarantee no user is bucketed into two conflicting experiments, caught at build time rather than discovered in the results.
- Running Staged Rollouts: Ramp a new feature to a growing share of traffic using the same file format as an A/B test, without learning a separate rollout tool.
- Instrumenting Without a Vendor Lock-In: Forward exposure and result events to an existing analytics warehouse instead of adopting a hosted experimentation database.
- Small-Team Experimentation on a Budget: Use the free CLI and SDK with self-owned event handlers before deciding whether the hosted metrics layer is worth paying for.