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

A side-by-side comparison of Caveman and siift — 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.

Freemium

An agentic AI operating system that helps founders map, validate and execute business strategy on one intelligent canvas.

Intelligent Business Canvas: A visual workspace that maps ideas, assumptions, actions and results into decision-ready filters so the whole business can be seen at once.
Living Memory System: A scalable agentic memory that learns as the business evolves and keeps context aligned across tools, data and teammates.
AI-Scored Validation: Automated, continuous research that grades assumptions into evidence so founders know what is validated and what is still risky.
Five-Stage Execution Loop: Guided progression through Ideate, Validate, Build, Go To Market and Scale, each with its own AI-driven workflow.
Safe Stack Automations: Human-in-the-loop actions across 80+ popular applications so approved work executes without leaving the canvas.
Shareable Workspaces: Collaborative views that let teammates, advisors and other stakeholders work from the same strategy context.
Proactive Next-Step Guidance: Personalized, iterative advice that surfaces the highest-leverage action rather than a generic checklist.
Credit-Based AI Usage: Monthly request credits scaled by plan and weighted by task complexity, with unlimited projects even on the free tier.

Idea Validation: De-risking a new business concept by turning founder assumptions into automatically researched, scored evidence before building.
Strategy Mapping: Turning a cloud of unstructured ideas into a visual mind-map that exposes blindspots across the business model.
Go-To-Market Planning: Iterating on sales and marketing with an AI-native loop that tests which channels actually produce revenue.
Product Prioritization: Helping product leaders decide what to build next based on verified market opportunities rather than intuition.
Scaling Diagnostics: Systematizing an existing business to surface its current growth constraints and reverse-engineer fixes.
Advisor Collaboration: Sharing a single live strategy workspace with co-founders, advisors and investors instead of static decks.