Langfuse is a powerful tool for debugging LLM quality. But if your goal is reducing what you're spending on OpenAI and showing the CFO the savings — you need a different tool. Preto integrates in one URL change and tells you exactly what to cut.
No SDK. No instrumentation. One URL change.
Langfuse is genuinely excellent for ML engineers building complex LLM systems who need traces, evals, and quality scoring. The teams looking for alternatives are typically engineering or finance teams who need to reduce the OpenAI bill — and found Langfuse wasn't designed for that job.
Langfuse requires adding its SDK to your codebase and wrapping your LLM calls. That's real engineering time — and maintenance ongoing. Preto is a transparent proxy: point your base_url at us. Your existing code stays exactly as-is.
Langfuse helps you understand LLM quality and behavior. It doesn't rank your cost optimization opportunities or estimate savings per finding. You still need an engineer to dig through data and build the business case for each change.
Langfuse is built for ML engineers. Preto's savings dashboard is built for anyone who needs to answer "how much are we spending on AI and what did we do about it?" — including people who don't read traces.
Built for LLM developers who need to understand what their models are doing — traces, evals, prompt versioning, quality debugging. Strong open-source community and self-hosting option.
Built for teams who need to reduce their OpenAI bill — cost tracking broken down by feature, ranked recommendations with dollar estimates, and budget enforcement at the proxy level.
| Feature | Langfuse | Preto.ai |
|---|---|---|
| Integration method | SDK-based | URL change only |
| Setup time | Hours – days | Under 10 minutes |
| Request cost tracking | ✓ | ✓ |
| LLM tracing / spans | ✓ | ✗ not our focus |
| Evals + quality scoring | ✓ | ✗ not our focus |
| Prompt versioning | ✓ | ✗ not our focus |
| AI cost recommendations | ✗ | ✓ |
| Dollar savings estimates | ✗ | ✓ |
| Savings dashboard | ✗ | ✓ |
| Budget enforcement | ✗ | ✓ |
| Self-hostable | ✓ | Roadmap |
| Open source | ✓ | Soon |
Traces, spans, evals — Langfuse answers quality questions with depth. For teams building complex LLM chains who need to understand exactly what their model did on a specific request, Langfuse is the right tool. The observability is comprehensive and the open-source ecosystem is strong.
Preto answers cost questions with action: which features use the most expensive models, what you can switch, how much it'll save per month. Then it tracks the actual dollars recovered after each change. That's the loop Langfuse — and most other tools — leave open.
Langfuse could show you this usage data if instrumented. Preto surfaces the finding automatically — no instrumentation required — estimates savings, and tracks implementation.
Unlike moving between SDK-based tools, switching to Preto doesn't require removing Langfuse instrumentation first (though you can). Just point your OpenAI base_url at Preto and we start capturing data immediately. Your Langfuse setup can continue running in parallel if needed.
No instrumentation wrappers. No spans. No SDK dependency to manage.
Book a demo and we'll show you what Preto found in the first 24 hours of monitoring a similar codebase.
Book a Demo →Or email gaurav@preto.ai
What they said after switching.
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We're in private beta. Quotes coming soon — reach out if you want to be first.