AlliHat vs ARBR: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of AlliHat and ARBR — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
AlliHat
AlliHat
A Safari sidebar extension that integrates Claude, ChatGPT, Gemini and Apple's on-device model for page-aware Q&A and browsing assistance.
Key features
- Multi-Model Integration: Connects and switches between Claude, ChatGPT, Gemini, and Apple's on-device model directly from a Safari sidebar so users can compare outputs or choose a local/private model.
- Page-Aware Question Answering: Understands the content of the active webpage and answers questions in context without leaving the page, enabling fast fact checks and summaries tied to the current content.
- Highlight-to-Answer: Convert selected text into immediate queries or summaries — highlight any passage to get concise explanations, clarifications, or rewritten text.
- Automated Browsing Mode: Can follow links and browse pages on the user's behalf to gather information, synthesize findings, and produce consolidated summaries or action items.
- Inline Sidebar UI: Persistent, non-intrusive sidebar interface inside Safari that provides continuous access to assistants while keeping the browsing context visible.
- Privacy-Focused Option: Supports Apple's on-device AI for local processing when users prefer to keep data off cloud models, offering a privacy-friendly workflow.
- Integrates multiple providers: Claude, ChatGPT, Gemini, plus Apple's on-device model(s)
- Safari sidebar UI surfacing model responses alongside pages
- Page-aware question answering (use current page content as context)
- Highlight text to get instant, contextual answers or summaries
- Automated browsing mode that can follow links and collect context
- Option to leverage on-device models for local processing / privacy
- Supports switching between model providers within the sidebar
Best for
- Web Research and Summarization: Quickly summarize long articles or research pages and obtain concise takeaways without switching apps or copying content.
- Fact-Checking and Source-Based Q&A: Ask targeted questions about a page's content (e.g., verify claims, extract dates or figures) with answers grounded in the current page.
- Drafting Responses with Context: Compose email replies, comments, or notes that reference specific webpage content by using the sidebar to pull relevant passages and craft text.
- Information Aggregation: Use automated browsing to traverse related links, compile data from multiple pages, and produce a single consolidated report or summary.
- Model Comparison and Tuning: Compare outputs from Claude, ChatGPT, Gemini, and on-device models side-by-side to choose the best phrasing or perspective for a task.
- Quick Clarification while Reading: Highlight confusing paragraphs or technical passages to receive immediate, plain-language explanations without disrupting reading flow.
- Research and summarization of web pages without leaving the browser
- Quick inline answers by highlighting page text
- Assisted browsing where the tool follows links and aggregates information
- Content drafting or editing with contextual page references
- Customer support or knowledge work that needs page-specific LLM assistance
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.