---
title: "Caveman vs PromptLayer: prompt registry plus compression"
description: "Keep PromptLayer. Caveman works with it, and the two barely overlap. PromptLayer owns your prompts: who edited them, which version is live, how each version scores. Caveman Middleware never edits a pr"
canonical: https://caveman.ac/compare/promptlayer
last-updated: 2026-10-12
---

# Caveman vs PromptLayer: prompt registry plus compression

Keep PromptLayer. Caveman works with it, and the two barely overlap. PromptLayer owns your prompts: who edited them, which version is live, how each version scores. Caveman Middleware never edits a prompt. It shortens the tool output that piles up around the prompt in an agent loop, like a 20,000-token API response on turn three that rides along to turn twelve. Your registry keeps serving the exact version it serves today.

## What is PromptLayer?

PromptLayer is a platform for prompt management, LLM observability and evals. Its center is the Prompt Registry: versioned prompt templates with release labels, so a domain expert can promote a new version to `prod` without a code deploy. Dynamic release labels split traffic between versions by percentage or user segment, which gives you A/B tests on prompts.

Around the registry sit the other pieces. Request logs capture inputs, outputs, cost, latency and feedback in one timeline. Tables are the workspace for datasets, evaluations, backtests against past production traffic, and batch runs, with scorecards for grading. Workflows connect multi-step systems, and there are traces, webhooks and an MCP server for agents.

Getting data in is flexible. `pl.run()` fetches a template and runs it for you. `pl.openai` and `pl.anthropic` proxy the provider SDKs and log each call. `log_request()` logs calls you made yourself. `PromptLayer(enable_tracing=True)` turns on OpenTelemetry auto-instrumentation for OpenAI, Anthropic, Google GenAI and Bedrock, and any OTel pipeline can export to `https://api.promptlayer.com/v1/traces`.

The platform is hosted, and the Python SDK is Apache 2.0 on GitHub. Pricing starts with a free tier of 2,500 requests a month, then Pro at $49 a month and Team at $500 a month. Enterprise adds self-hosting on GCP, AWS or Azure.

## Does Caveman work with PromptLayer?

Yes, when your app makes the model call itself. Caveman Middleware wraps the provider client or framework you call, then, before each request, swaps eligible tool results (file reads, API responses, command output, extracted documents) for a shorter copy from a local runtime. The model gets a `caveman_retrieve` tool to read the full original back. Your app's history keeps the originals.

Caveman does not touch system prompts or user messages. So the prompt version PromptLayer served, filled with its variables, reaches the model byte for byte, and your release labels and A/B splits mean exactly what they meant before.

There are two paths where Caveman can't sit, and it's worth knowing them up front:

- **`pl.run()`** builds its own provider client inside the PromptLayer SDK. There is no client of yours to wrap, so those calls stay as they are. Single-turn template runs rarely carry big tool output anyway.
- **The `pl.openai` proxy** returns a PromptLayer wrapper object. Caveman's Python OpenAI adapter only accepts a plain `OpenAI` client, so use `enable_tracing=True` or `log_request()` for calls you want compressed.

## Which copy does a PromptLayer log record?

With `enable_tracing=True`, the text is the original and the token count is the smaller request. PromptLayer's OpenAI instrumentor wraps `chat.completions.create` and records the messages your code passed in. Caveman rewrites the body below that point. The input token count on the span comes from the provider's response, which counted what was actually sent. The Anthropic instrumentor wraps `messages.create` the same way.

So a log can show a 20,000-token tool result in its input while the input token count is far lower. Tell reviewers before they file it as a bug.

With `log_request()`, you decide. Most teams log their app's own history as `input` and pass `input_tokens` from `response.usage`, which gives the same picture. If you want to see the model's view, a shortened result starts with `[caveman: shortened; exact original via caveman_retrieve handle=cmw_…]`.

## PromptLayer vs Caveman: feature comparison

| What you need | PromptLayer | Caveman Middleware |
| --- | --- | --- |
| Versioned prompts with release labels | Yes, core feature | No, never edits prompts |
| A/B test prompt versions | Dynamic release labels | No |
| Request logs with cost and latency | Yes | No, one decision report per call |
| Evals, backtests, scorecards | Tables | No |
| Shrink a 20k-token tool result | No | Yes, original recoverable |
| Works with `pl.run()` | Yes | No, nothing to wrap |
| Keep the app's history unchanged | Not its concern | Yes, only the outbound copy changes |
| Self host | Enterprise plan | Runtime runs next to your app |
| Status | Released | Alpha |

## How do you use PromptLayer to check Caveman?

Run the same inputs twice with the same pinned prompt version, once with the middleware client in `record` mode and once in `compress`, and tag the logs `caveman-record` and `caveman-compress`. Pin a version number rather than a label, so a release landing mid-test can't change the prompt under you.

Then import both sets of request history into a Table and score the outputs with the same scorecard. Score the outputs your app produced. A prompt column in a Table runs through PromptLayer, outside your app, so Caveman isn't in that path.

Count `caveman_retrieve` calls in the logs, since each recovery carries the original back and costs tokens, and compare provider-reported input tokens across the whole task.

Caveman's own figures don't settle quality. The docs show a 400-line log going from 8,399 tokens to between 177 and 187 over sixteen runs, counted with `o200k_base` and labelled inferred. The middleware page's 13 synthetic samples are also inferred, and it says task quality was not measured. Your Table is where that gets measured.

## When should you use PromptLayer alone?

Use PromptLayer alone when the prompt is the payload: long templated prompts, no tools, one turn. There the prompt is the bill, and versioning and testing it is the optimization.

Use it alone when your bottleneck is people. If prompt fixes wait days for an engineer, the registry fixes that, and no token savings will.

Use it alone when most calls go through `pl.run()`, since Caveman has nothing to wrap there.

## When does adding Caveman make sense?

Add Caveman when a PromptLayer log shows a small, carefully versioned system prompt next to a pile of tool output. Say a 600-token prompt on turn twelve of an agent that has read four files and two search results along the way. Editing the prompt again moves the 600 tokens. Caveman works on the rest.

For terminal coding agents, use [Caveman Proxy](/products/caveman-proxy) instead.

## How to add Caveman next to PromptLayer

Start the runtime, fetch the template as you do now, call through a wrapped client, and log with `enable_tracing=True` or `log_request()`. The walkthrough with code and a rollback is in [add Caveman to PromptLayer](/switch/promptlayer).

## FAQ

**Is Caveman a PromptLayer alternative?**
No. PromptLayer manages and evaluates prompts. Caveman shortens tool output in the request. They stack.

**Will Caveman change my prompt versions?**
No. System prompts and user messages are left alone, so the served version reaches the model unchanged.

**Can I compress calls made with `pl.run()`?**
No. PromptLayer's SDK builds its own client there. Make the call yourself through a wrapped client if that path carries big tool output.

**Does Caveman have its own prompt or log UI?**
No prompt UI. Caveman Cloud, a hosted workspace, is open for sign-up with a Free plan. If you need a prompt UI, use PromptLayer.

## Sources

- [PromptLayer docs](https://docs.promptlayer.com/introduction) and [release labels](https://docs.promptlayer.com/features/prompt-registry/release-labels)
- [PromptLayer Tables](https://docs.promptlayer.com/features/tables/overview) and [custom logging](https://docs.promptlayer.com/features/observability/request-logs/custom-logging)
- [PromptLayer OpenTelemetry ingest](https://docs.promptlayer.com/features/observability/traces/opentelemetry) and [pricing](https://www.promptlayer.com/pricing)
- [PromptLayer Python SDK on GitHub](https://github.com/MagnivOrg/prompt-layer-library)
- [Caveman Middleware docs](https://docs.caveman.so/docs/sdk/middleware) and [Python middleware](https://docs.caveman.so/docs/sdk/middleware/python)
- Related: [measure agent cost](/guides/measure-agent-cost), [prompt compression](/guides/prompt-compression)