PromptCost vs Langfuse
Different shapes of tool entirely. Langfuse is an SDK-first LLM engineering platform you wrap around your code. PromptCost is a proxy you sit in front of your API calls. Here's when each one fits.
Side by side
| Feature | PromptCost | Langfuse |
|---|---|---|
| Integration model | HTTP proxy (URL swap) | SDK (Python, JS, decorators) |
| Setup time | ~60 seconds | A few hours |
| Code changes required | None — just headers | Yes — install SDK, wrap calls |
| Make.com / n8n usable | ✓ Native HTTP node | — Hard without code |
| Per-agent cost breakdown | ✓ Header tag | ✓ Via metadata / sessions |
| Hard budget cap that blocks at 429 | ✓ Core feature | — Observability only |
| Tracing & multi-step debugging | — Out of scope | ✓ Best-in-class |
| Prompt management / versioning | — | ✓ |
| Evals / datasets | — | ✓ |
| Self-hosting | — Hosted only | ✓ Open source |
| Free tier | Unlimited agents, 7-day history | 50K events/mo, 2 users |
| Paid entry plan | $9/mo (early access, 50 spots) | $29/mo Core |
| Provider key storage | ✓ Never stored | N/A — SDK never sees the key |
The fundamental difference
Langfuse is an observer. You instrument your code and Langfuse sees what happened after the fact. It's read-only on the request path — by the time Langfuse knows about a request, the request has already gone to OpenAI and you've already been billed for it.
PromptCost is an enforcer. It sits on the request path itself. It can decide not to forward a request to OpenAI based on a budget rule, returning a 429 to your code instead. You don't pay for blocked calls.
Both philosophies are valid. Which one you want depends on whether your problem is "I need to understand what my AI is doing" (Langfuse) or "I need to stop my AI from spending more than $50 this month" (PromptCost).
Where Langfuse wins
- Deep tracing. Langfuse's tree view of nested LLM calls, retrieval steps, and tool calls is genuinely best-in-class. If you're debugging an agent chain, you want this.
- Prompt management. Versioning, A/B testing, prompt registry — full-featured.
- Evals and datasets. Production-grade eval pipelines with dataset versioning.
- Open source & self-hosting. You can run it on your own infra with no usage limits.
- OpenTelemetry support. Plays nicely with existing observability stacks.
Where PromptCost wins
- You don't write code. Or you do, but your AI lives in Make/n8n/Zapier where SDKs aren't an option. Langfuse can technically be hit from an HTTP node, but the ergonomics are bad. PromptCost is designed for this exact case.
- You need a hard cap, not just visibility. Langfuse will tell you you spent too much. PromptCost will stop the spending.
- You want zero setup overhead. URL + 2 headers vs install SDK + wrap calls + emit events.
- You don't need 90% of Langfuse. If tracing, prompt versioning, and evals aren't your problem, you're paying for surface area you won't use.
- Indie pricing. $9/mo locked vs $29/mo entry.
How to pick
Pick Langfuse if
- You're shipping a Python or Node AI app and you control the codebase.
- You need to debug multi-step agents, version prompts, or run evals.
- You want self-hosting, or full OpenTelemetry integration.
- "Observability" is your real need — not "stop spending."
Pick PromptCost if
- Your AI runs in Make.com, n8n, Zapier, Bubble, or anywhere that's not "your codebase."
- You've been burned (or worry about being burned) by a runaway workflow.
- You need hard budget caps, not alerts.
- You want a tool that does one thing exceptionally well, not ten things adequately.
"Can I use both?"
Yes — and it's a reasonable setup. Some teams use Langfuse to instrument their main Python product (deep tracing, prompt management, evals) and PromptCost in front of their no-code automations (hard budget caps for the Make scenarios that finance pays for). The two layers don't conflict.
Try PromptCost in 60 seconds.
Free forever. No SDK, no credit card. If you need full observability, Langfuse is excellent — pick what fits.
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