Alternatives · Updated 2026-10-21
Best Caveman Cloud alternatives in 2026
Caveman Cloud bundles two jobs: it shows a team what its agents cost, and it adds tools to bring that cost down. Most teams need one of those more than the other, so the right alternative depends on which. This guide splits the options by job.
The short answer
If you want to send fewer tokens per request, the strongest alternatives are SuperCompress (hosted, keeps only the lines that answer the question), Headroom (open source, runs locally) and RTK (shell output only). If you only need to see agent spend, Langfuse (open source, MIT) or your provider's own usage console; for caps and governance across a team, Caveman Cloud itself remains the more complete option. The free, open-source Caveman skill remains an option if you only want shorter replies.
What Caveman Cloud is
Caveman Cloud is the hosted team product from the makers of the open-source Caveman skill. Per its pricing page, it includes cost-per-task observability across agents, evals, monitors, an inbox, an "Autopilot" for suggested fixes, model routing and a semantic cache (the cache on the Team plan). There is a free plan for one person and a Team plan at $300 a month that includes $300 of usage, with no per-seat charge. Its docs and pricing page do not agree on every allowance, so check the current numbers there.
How we picked
We picked tools that replace a real part of what Caveman Cloud does, can be tried in an afternoon, and publish enough documentation to describe accurately. Four reduce tokens; two give you spend visibility, though neither adds team caps or governance. We left out anything we could not verify from the vendor's own docs.
The alternatives
1. SuperCompress
SuperCompress replaces the cost-cutting half of Caveman Cloud at the source: the bulky context in each request. Your agent or app sends a log, diff, file read or tool result together with the question being asked, and the hosted API returns only the original lines that answer it. Nothing is paraphrased, so error messages, file paths and IDs come through exactly as they were, and every call reports original and kept tokens so you can see the effect on your own traffic.
- Best for: cutting the input tokens an agent or app sends: tool output, file reads, logs, MCP results, retrieved docs
- How it works: a hosted, query-aware model keeps the original lines that answer the current question, word for word, and drops the rest
- Runs: hosted API (
POST /api/v1/compress) plus an MCP tool and hooks for coding agents - Price: 5M free tokens a month, then $0.10 per million, per the site
2. Headroom
Headroom is the open-source option for compressing the same kinds of context locally. It picks a compressor for each content type and keeps the originals retrievable, so the model can ask for what was cut. It suits teams that want everything on their own machines and are happy to run and tune a proxy or library.
- Best for: teams that want an open-source compression layer they run themselves
- How it works: content-type compressors for JSON, logs, code, search results and history, with the originals kept so the model can ask for them back
- Runs: locally, as a Python or TypeScript library, a proxy, an MCP server or an agent wrapper (
headroom wrap claude) - Licence: Apache 2.0
3. RTK (Rust Token Killer)
RTK is narrow and effective: it filters the output of common shell commands before the agent reads it. If most of your waste is git, test and build output, it is a cheap first step, and it pairs well with a tool that handles everything else.
- Best for: noisy shell commands such as
git status, test runs and builds - How it works: a hook rewrites Bash commands to run through command-specific output filters
- Watch out for: it only sees Bash calls, so built-in Read, Grep and Glob tools are not filtered; its 60–90% figures are the project's own estimates
4. Caveman (open-source skill)
If what you liked about Caveman Cloud was the Caveman skill itself, the skill is free and open source. It makes replies shorter rather than inputs smaller, so measure it on your own tasks before relying on headline figures.
- Best for: shorter agent replies, at no cost
- How it works: a skill that makes the agent answer in terse, caveman-style prose, plus local shrinking of some tool output
- Watch out for: an independent JetBrains A/B test measured an 8.5% output saving against the advertised 65%, and the skill itself adds input tokens every turn
5. Langfuse
Langfuse replaces the visibility half. It traces every LLM call with token and cost tracking by user, feature and model, adds evals, and is free to self-host under the MIT licence. It does not shrink requests itself, so pair it with one of the tools above.
- Best for: seeing what your LLM calls cost, per user, feature and model
- How it works: open-source tracing with token and cost tracking for every generation, plus evals
- Runs: self-hosted for free under the MIT licence, or as Langfuse Cloud
- Watch out for: it shows spend; it does not shrink requests on its own
6. Your provider's usage console
Before paying for any console, open your provider's usage page. Anthropic, OpenAI and Google all break spend down by key, model and day. It will not attribute cost to individual agent tasks, but it is enough to tell whether you have a problem worth solving.
- Best for: a free first look at spend before buying anything
- How it works: Anthropic, OpenAI and Google each show usage and cost by key, model and day
- Watch out for: no per-task or per-agent breakdown, and no evals or caps across providers
Side by side
| Tool | Job | Runs | Cost to start |
|---|---|---|---|
| SuperCompress | Fewer input tokens per request | Hosted API + MCP | Free tier |
| Headroom | Fewer input tokens per request | Local | Free, open source |
| RTK | Smaller shell output | Local | Free, open source |
| Caveman skill | Shorter replies | Local | Free, open source |
| Langfuse | Spend visibility, evals | Self-host or cloud | Free self-hosted |
| Provider console | Spend visibility | Provider dashboard | Free |
How to pick fairly
Every tool here publishes its own headline numbers, and independent studies have found that fewer tokens do not always mean a smaller bill once prompt caching, retries and extra agent turns are counted (see PointFive's end-to-end study and JetBrains' rtk trial; neither tested SuperCompress). Run the same tasks with and without a tool, check that the tasks still succeed, and compare your provider bill per finished task. That test matters more than any list, including this one.
Disclosure: SuperCompress is our product, so we are not neutral. We describe every other tool from its own documentation and public reporting, link the sources, and say where another tool is the better fit. Plans and prices change; check each vendor before you decide.
Try SuperCompress
One command installs the CLI and wires the hosted compressor into every coding agent it finds (Claude Code, Cursor, Codex, Windsurf, Cline, Gemini CLI, OpenCode and more). supercompress doctor then shows a health check per agent.
npm install -g supercompress-proxy && supercompress setupBuilding your own app instead? Call the hosted API with a key from the dashboard.
FAQ
What is the best Caveman Cloud alternative?
It depends on the job. To send fewer tokens per request, SuperCompress (hosted) or Headroom (open source). To see agent spend, Langfuse or your provider's usage console.
Is there a free Caveman Cloud alternative?
Yes. Headroom, RTK, the Caveman skill and self-hosted Langfuse are all free and open source, and SuperCompress has a free tier of 5M tokens a month.
Does SuperCompress do everything Caveman Cloud does?
No. SuperCompress is a compression service: it shrinks the context in each request and reports original and kept tokens per call. It is not a team governance console.
How much does Caveman Cloud cost?
Per its pricing page, a free plan for one person and a Team plan at $300 a month including $300 of usage, with no per-seat charge. Check caveman.so/pricing for current terms.