---
title: "Caveman vs Helicone for agent observability and costs"
description: "Helicone gives you a gateway and a view of the requests passing through it. Caveman gives you local context compression and a path into agent-level cost investigation. If you can already see the expen"
canonical: https://caveman.so/compare/helicone
last-updated: 2026-09-07
---

# Caveman vs Helicone for agent observability and costs

Helicone gives you a gateway and a view of the requests passing through it. Caveman gives you local context compression and a path into agent-level cost investigation. If you can already see the expensive requests, Caveman's useful next step is a change you can test on the underlying task.

Choose based on what is missing. Logging a request and reducing the repeated content in that request are separate jobs. Both may belong in the same stack.

## What Helicone already covers

Helicone's [current quickstart](https://docs.helicone.ai/getting-started/quick-start) uses an OpenAI-compatible gateway with logging and fallbacks. Its documentation also includes sessions, custom properties, cost tracking, datasets, and evaluation scores. It would be inaccurate to describe it as only a request counter.

| Need | Helicone | Caveman |
| --- | --- | --- |
| Gateway access | Unified model access through its gateway | Local proxy with documented provider routes |
| Request visibility | Automatic logging and gateway observability | Local reports; Platform workflows require private access |
| Agent context reduction | Inspect the configured gateway features | Content-specific compression with original-data recovery |
| Work attribution | Session and custom-property features | Analysis should retain explicit workflow identity and evidence |
| Adoption without an account | Depends on your Helicone deployment | Local skill and proxy can be tried without Platform access |

## Why a trace should lead to a test

A trace might show a repeated tool result consuming much of an agent's input. The next question is specific: can the model finish the task using a smaller view and recover detail when it needs it?

Caveman can test that locally. Its proxy stores original bytes before emitting a lossy view, and keeps the source when safe compression is unavailable. Use a known task and the same model so the result tells you about context, not a different inference provider.

A local token report is useful for finding a candidate. The provider's usage and the accepted task result decide whether the candidate improved the complete run.

## Do not lose session context in a migration

An application trace often carries more than the model request: a user action, tool spans, and links to the final output. Preserve those relationships if you change the logging path. A dashboard with the same total tokens but no way to find the failed task is a weaker operational tool.

Likewise, decide where message content is retained. A full prompt capture and a usage-only record have different privacy and debugging properties. Make the capture policy explicit before running two exporters or moving a sample to another platform.

Caveman Platform's native LiteLLM integration is documented. We do not infer a supported Helicone historical-log import from that fact. A Platform migration needs an agreed ingestion path and a pilot with real records.

## Compare operating cost as well as tokens

Gateway spend, trace storage, provider charges, and integration work all matter. Use the terms on your own accounts rather than a copied pricing table that can become stale.

Run a candidate against representative tasks and include failures, recovery calls, and retries. Keep cold and warm-cache behavior visible. A cached baseline and uncached candidate can make a useful optimizer look expensive, or reverse the result when run order changes.

The [measurement guide](/guides/measure-agent-cost) explains how to keep those cases comparable. We have not established a general Caveman-versus-Helicone cost advantage on matched production traffic.

## A practical first move

Keep Helicone logging while testing `caveman shrink` on an expensive tool output, or use a separate direct-provider caller through the local proxy. The [switching guide](/switch/helicone) covers both, with session-ID checks and rollback.

Read [Helicone's docs](https://docs.helicone.ai/getting-started/quick-start), [Caveman local reporting](https://docs.caveman.so/docs/proxy/tokens), and [Caveman Platform](/products/platform) before deciding which part of the stack to change.