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

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 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.

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 and release labels
• PromptLayer Tables and custom logging
• PromptLayer OpenTelemetry ingest and pricing
• PromptLayer Python SDK on GitHub
• Caveman Middleware docs and Python middleware
• Related: measure agent cost, prompt compression