ARBR vs Promptfoo: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of ARBR and Promptfoo — 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.
Promptfoo
Promptfoo
CLI and web tool for testing, evaluating, red‑teaming, and monitoring LLM prompts and outputs to catch regressions and vulnerabilities.
Key features
- Red-Teaming & Vulnerability Scanning: Declarative red‑team tests and automated scans to surface prompt injections, unsafe completions, and other model security risks across providers.
- Evaluations & Regression Detection: Run reproducible eval suites and compare outputs before/after changes to detect regressions, with CI/CD and GitHub Action integration for automated checks on PRs.
- Multi-Provider Model Comparison: Execute the same tests across multiple model providers and families (e.g., OpenAI, Claude, Gemini, Llama) to compare quality and safety consistently.
- CLI and Web UI: Command‑line tools for running tests and a web 'view' UI to inspect prompts, final rendered prompts, outputs, and structured results in tabular form.
- Declarative Configs & Templating: Use promptfooconfig.yaml with Nunjucks templating and custom filter plugins to generate complex prompts and test permutations programmatically.
- Extensible Provider & Plugin System: Add or customize providers, local execution, or custom filters (JS/Python) to adapt tests to specific stacks or private model endpoints.
- Docker Distribution & Local Execution: Official container images and local execution modes enable isolated, reproducible runs and CI friendliness.
- GitHub & CI Integrations: Official GitHub Action and CI-friendly tooling to automatically post evaluations on PRs and enforce prompt quality gates.
- Command‑line interface and library for running declarative evals and tests
- Red‑teaming and vulnerability scanning for LLM outputs
- Declarative configuration via promptfooconfig.yaml (prompts, providers, filters, tests)
- Support for templated prompts using Nunjucks and custom filter modules
- Providers for multiple model backends (OpenAI and others; compare GPT, Claude, Gemini, Llama, etc.)
- Docker images published to GHCR (multi‑arch support: linux/amd64, linux/arm64, etc.)
- Web UI (src/app) that integrates with `promptfoo view` for inspecting outputs and final prompts
- CI/CD integrations including an official GitHub Action for evals on PRs
- Developer productivity features: live reload, caching, npm scripts for local dev
- Configurable Python executable (PROMPTFOO_PYTHON) and language‑agnostic test data (supports Python, JavaScript, others)
Best for
- Red‑teaming LLM integrations to find prompt injections, unsafe outputs, and info‑leakage before release.
- Regression testing in CI to automatically detect when a prompt or model update degrades output quality or safety on pull requests.
- Comparing model performance across providers and model families to choose the best model for a given task or guardrail requirements.
- Building test-driven prompt development workflows where prompts are versioned, evaluated, and iterated using reproducible eval suites.
- Adding automated before/after eval diffs on GitHub PRs to give reviewers quantitative and qualitative signal about prompt edits.
- Validating agents, RAG pipelines, and LLM apps end‑to‑end by running scenario-based tests and inspecting final rendered prompts and outputs.
- Test‑driven prompt engineering and automated evaluation of model outputs
- Red‑teaming and security testing of language model behavior
- Regression testing of prompts and model changes via CI/CD and GitHub Actions
- Comparing performance across multiple model providers
- RAG (retrieval augmented generation) and agent testing in local/dev environments
- Integrating automated evals into PR workflows to produce before/after views of prompt edits