ARBR vs dif.sh: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of ARBR and dif.sh — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
ARBR
Gyde & Domkundwar Foundation
Open-source, MIT-licensed AI gateway and control plane that routes, governs and observes every LLM request behind one OpenAI-compatible endpoint.
Key features
- OpenAI-Compatible Routing: A single drop-in endpoint over every major provider, with rules, difficulty-aware selection, cost guardrails and automatic fallback choosing the model per request.
- In-Path Governance: Budgets, rate limits, output guardrails, prompt-injection checks and kill switches enforce policy before inference rather than auditing it afterwards.
- Structured Observability: Cost, latency, tokens and routing decisions are emitted as structured events attributed by application, team, model and user, viewable in local dashboards or exported to OpenTelemetry backends such as Datadog, Grafana and Prometheus.
- LLM-Judge Evaluation: A sample of live traffic is scored for quality so requests can be routed to the cheapest model that provably clears the bar, rather than optimising on price alone.
- Safe Model Deployment: Canary and shadow new models against real traffic with regression gates that block promotion until evaluations pass, plus instant rollback.
- Broad Provider Coverage: One layer over Anthropic, OpenAI, Google Gemini, Amazon Bedrock, Azure OpenAI, Vertex AI, Groq, DeepSeek, Moonshot, xAI and Mistral, plus LiteLLM and NVIDIA NIM, with pricing and benchmark data for over 3,000 models.
- Drop-In SDK Compatibility: Change only the base URL and existing OpenAI SDKs, agent frameworks and chat UIs keep working, gaining streaming chat completions, embeddings, a realtime voice proxy and JavaScript and Python SDKs.
- Self-Hosted and MIT Licensed: The full control plane runs inside your own infrastructure under an MIT licence, with a hosted option available for teams that do not want to operate it.
Best for
- LLM Cost Reduction: Route summarisation and extraction traffic to cheap small models while reserving frontier models for analysis, cutting spend without hand-editing every call site.
- AI Spend Attribution: Give finance and engineering a per-application, per-team and per-user breakdown of token spend so AI budgets can be owned by the groups that generate them.
- Enterprise AI Governance: Enforce departmental budgets, rate limits and kill switches in the request path so a runaway agent cannot exhaust a quarter's inference budget.
- Provider Risk Mitigation: Keep applications provider-neutral behind one endpoint with automatic fallback, so a single vendor outage or price change does not require a code change.
- Model Migration Testing: Shadow or canary a newly released model against production traffic and let regression gates decide whether it is promoted.
- Prompt-Injection Defence: Apply output guardrails and prompt-injection checks centrally for every application instead of reimplementing them per service.
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.