Caveman vs moar: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Caveman and moar — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Caveman

Julius Brussee

Freemium

Efficiency stack that caches, compresses, and routes AI traffic to cut LLM output tokens by up to 65% with verified savings.

Caveman Skill: MIT-licensed Claude Code skill that teaches 30+ agents (Claude Code, Codex, Cursor, and more) to answer in a compressed dialect, cutting output tokens ~65% while keeping code and errors byte-exact.
Local Proxy Wrap: One command (`caveman claude`) launches your agent with recoverable local context compression — no account required, BYOK, engine stores original bytes before lossy replacement.
Recoverable Context Compression: Engine recognizes logs, JSON, code, diffs, and tables, then sends smaller eligible context to the model and can restore the originals on demand.
Agent SDK: `@caveman-ai/agent` TypeScript SDK adds catalog-price guards, per-request token bills, and eval-gated context plans to production agents.
Cave Score & Ledger: Inferred local savings score and a verified 'causal-cache' ledger on paid tiers so you can prove cut tokens and cut dollars.
Managed Cloud Gateway: Point traffic at one URL and caching / compression / routing run eval-gated on autopilot, with a synced savings dashboard.
Browser Extension: Ships for ChatGPT, Claude, and Gemini so end-user chats benefit from the same output compression without any code changes.
Enterprise & OEM: Same stack self-hosted in your cloud or datacenter with signed savings receipts, zero data retention, and OEM embed options.

LLM Bill Reduction: Cap OpenAI, Anthropic, or Google spend without changing model choice by cutting output tokens per response across your agent fleet.
Coding Agent Efficiency: Install the skill to make Claude Code, Codex, Cursor, and other CLI agents produce terse, byte-exact answers so long tasks fit in context.
Provider Wrap for Production Agents: Use the SDK to add per-call token bills, catalog-price guards, and eval-gated context plans to LangChain / custom agents.
Central Cost Gateway: Point every agent in the org at Caveman Cloud so caching and routing are enforced from one URL with a shared dashboard.
On-Prem or OEM Embed: Ship the Enterprise stack inside a regulated network or embed it in your own AI product with signed savings receipts and zero data retention.
Chat-App Compression: Install the browser extension for ChatGPT, Claude, or Gemini to keep casual chats short, cheaper, and inside the context window.

moar (getmoar.ai)

Free

Privacy-first Chrome extension that converts documents to AI-ready Markdown, reducing size up to 95% for more conversations across major chat models.

Document Compression: Converts arbitrary documents into AI-ready Markdown, reducing size by up to 95% to fit more content into model context windows.
Meaning Preservation: Uses transformation techniques that maintain semantic content and intent so compressed documents retain zero loss of meaning for downstream tasks.
Multi-Model Compatibility: Output is formatted to work seamlessly with ChatGPT, Claude, Gemini and other conversational LLMs, enabling consistent results across models.
Browser Integration: Privacy-first Chrome extension that performs conversion in-browser with zero setup, letting users optimize content directly where they work.
Conversation Density Increase: By reducing document size, enables up to 5× more conversational turns or more documents per single model session, avoiding context truncation.
Zero Setup Workflow: Immediate usability without configuration—install the extension and start converting documents into compact, chat-ready Markdown.
Converts documents into AI-ready Markdown
Reduces document size up to 95% while aiming to preserve meaning
Increases number of chat conversations per document (advertised 5×)
Zero-setup usage model (instant conversion)
Free Chrome extension for in-browser conversion
Designed to work with ChatGPT, Claude, Gemini and other chat models

Feeding Long Documents to Chatbots: Convert manuals, reports, or whitepapers into compressed Markdown so ChatGPT/Gemini can consume the full content in a single session.
Research and Q&A: Prepare academic papers and technical documents for fast question answering and summarization without losing critical details.
Knowledge Base Compression for Support: Shrink internal knowledge articles to allow conversational agents to reference complete answers within model context limits.
Sales and Product Enablement: Condense product sheets and pricing documents into compact formats that sales assistants can query during live customer interactions.
Personal Note Consolidation: Compress and organize large personal notes or meeting transcripts into chat-ready snippets for follow-up queries and summaries.
Cross-Model Workflows: Standardize document input for workflows that switch between ChatGPT, Claude, Gemini, or other LLMs to ensure consistent comprehension.
Feeding long documents into chat models for Q&A without hitting context limits
Reducing token/context usage when interacting with ChatGPT, Claude, Gemini
Preparing knowledge-base or documentation for conversational assistants
Research and note preparation to maximize chatbot interaction per source document
Faster prototyping of chat integrations by compressing source documents