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

A side-by-side comparison of Caveman and Ito — 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

AI code review tool that builds and runs your app on every PR, catches runtime bugs, and attaches a failing test as evidence.

Targeted Test Plans: Reads the PR diff and description on open, then focuses testing on the user flows the change affects — no test cases to write and no suite to maintain.
Containerized Test Execution: For every PR, Ito builds a real single-use copy of your app from source and navigates it like a real user against your real backend.
Self-Healing Codebase: Proposes a fix as its own PR for the bugs it finds so the fix loop starts before an engineer opens the ticket.
Product Demo Videos: Posts a polished walkthrough of every change to the PR so reviewers see the feature in action, not just the diff.
Automatic Smoke Testing: Maps each change to at-risk user journeys and builds a smoke-test plan without human authoring.
Full PR Findings with Evidence: Every failure includes a video replay, exact lines responsible, logs, and reproduction steps, with severity ratings to prioritize.
Auto Re-run on Fix: Push a fix and Ito re-runs the failed flows automatically to confirm the bug is resolved before merge.
Agent Sandboxes: Gives autonomous coding agents a live, managed copy of your app to build and test against without hitting production.

AI-Generated PR Verification: Teams whose engineers and coding agents open many PRs a day get every one built, exercised, and evidence-backed before merge.
Catching Runtime-Only Bugs: Surface authentication, concurrency, service-authorization, and data-migration bugs a static reviewer or diff-only AI can't see.
Removing Manual QA Bottlenecks: Reclaim the hours each developer spends manually verifying AI-written code by delegating the pre-merge runtime pass to Ito.
Regulated & Security-Sensitive Codebases: Financial, healthcare, and defense teams get SOC 2 Type II, isolated test runs, and zero data retention on every PR.
Enabling Agent Automerge: Give autonomous coding agents a runtime gate so they can merge safely without an added human review bottleneck.
PR Demo Artifacts: Reviewers and PMs get a runnable video walkthrough of the feature attached to the PR instead of guessing from the diff.