dif.sh vs Magnitude: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of dif.sh and Magnitude — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
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.
Magnitude
Magnitude
Open-source inference server that profiles your hardware, picks and tunes the best local models for it, and plugs them into the coding agent you already use.
Key features
- Hardware Profiling: Inspects your CPU, GPU, and memory and recommends only the local models that will actually run well on that machine.
- Automatic Model Setup: Downloads, tunes, and serves the models you select so you never hand-configure quantization, context length, or serving flags.
- Agent-First Installation: Send one prompt to your existing coding agent and it profiles the hardware, walks you through model choice, and switches itself over to the local endpoint.
- Broad Harness Compatibility: Works as a drop-in backend for Pi, OpenCode, Hermes, OpenClaw, Codex, Claude Code, Oh My Pi, and Cline.
- Built-In Harness: Ships its own agent harness for users who want local coding assistance without adopting a separate client.
- Fully Offline Operation: Models, prompts, and files stay on the machine, so there are no API keys, no token costs, and no rate limits.
- Cross-Platform Support: Runs natively on macOS and Linux, with Windows supported through WSL.
- CLI Distribution: Installed and managed through the @magnitudedev/cli npm package with an interactive setup flow.
Best for
- Private Codebase Work: Run an agent against proprietary source without sending any of it to a hosted model provider.
- Zero-Cost Development: Replace metered API calls with local inference for high-volume experimentation and long agent runs.
- Offline and Air-Gapped Environments: Keep a coding agent working on a plane, in a secure facility, or anywhere with no reliable network.
- Hardware Evaluation: Discover which local models a given laptop or workstation can realistically serve before committing to a setup.
- Existing Agent Migration: Point a harness you already use at a local backend without changing your workflow or tooling.
- Compliance-Constrained Teams: Satisfy data-residency requirements by keeping prompts and outputs on company-controlled machines.