Both Agent Status and Langfuse help you run AI agents in production. They solve different problems and most teams that are serious about reliability end up running both. This page is an honest walk through where each one fits, where it doesn't, and how to choose.
One-line summary
- Agent Status: Agent Status is an outside-in monitoring platform. It validations your agent from outside your stack and tells you what your users experienced.
- Langfuse: Langfuse is an inside-out tracing and evaluation platform. It instruments your code and tells you what happened on your servers.
Feature matrix
| Capability | Agent Status | Langfuse |
|---|---|---|
| Outside-in validations from real regions | Native | No |
| Per-request assertions on response shape + content | Native | Via eval scripts |
| Inside-out tracing of chain execution | No | Native |
| Prompt management + versioning | No | Native |
| Offline LLM-as-judge evaluations | No | Native |
| Geographic variance alerting | Native | No |
| Self-hosting | No | Yes |
| Token-level cost tracking across experiments | No | Native |
When to use Agent Status
- You need to know your users' experience, not your servers'.
- Your agent depends on a third-party model provider and you need to catch their outages.
- Your users are distributed geographically and you need regional alerting.
- You want alerts in seconds, not minutes of aggregated latency data.
When to use Langfuse
- You need to trace individual requests end-to-end through your own code.
- You want to run offline evaluations on prompts and datasets.
- You need prompt management with versioning and A/B testing.
- You need token-level cost tracking across many experiments.
- You require self-hosting for data residency or compliance.
What Langfuse does that we don't
Prompt management and offline evals are Langfuse's home turf. If you're iterating on prompts daily or running LLM-as-judge evaluations on a dataset, Langfuse is the better tool. Agent Status does not try to compete there. We also don't self-host, which is a dealbreaker if your data can't leave your VPC.
What we do that Langfuse doesn't
Per-request outside-in validations and geographic alerting. Langfuse's tracing is read-only and runs inside your stack, so it cannot detect outages that happen on the model provider's side or in regions where you don't have servers. That's the gap Agent Status fills.
Pricing
- Agent Status: Free tier: 30 tests/month, 1 agent, 3 regions, 6-hour minimum interval. Paid tiers unlock unlimited agents and regions with usage-based pricing. No per-seat fees.
- Langfuse: Free tier at 50k observations, then per-observation pricing. Self-host free (with ops overhead).
Most teams use both
If you want a simple rule: if your agent fails, Langfuse tells you why. Agent Status tells you that it failed and who was affected. The two products answer different questions and the cost of running both is trivial compared to the cost of either blind spot.
Related
- /vs/helicone - Similar comparison with Helicone.
- /vs/datadog - If you already pay for Datadog APM.
- /blog/what-is-outside-in-monitoring - The conceptual difference.
- /platform - The product overview.
Frequently asked questions
Is Agent Status a replacement for Langfuse?
No. Langfuse is inside-out tracing - it tells you why an agent failed. Agent Status is outside-in - it tells you that it failed and who was affected. They solve different problems.
Can I use Agent Status and Langfuse together?
Yes, and most teams do. Langfuse for trace-level debugging, Agent Status for outside-in validation. The cost of running both is trivial next to either blind spot.
What does Agent Status do that Langfuse doesn't?
It probes your agent from real residential IPs in 70 countries and asserts on the user-facing response, catching failures that never appear in your traces.
How does pricing compare?
Agent Status has a free tier (30 tests/month) and usage-based paid plans; Langfuse prices around trace volume and seats. Because they do different jobs, teams typically budget for both.
Try Agent Status alongside Langfuse
Free tier includes 30 tests/month on 1 agent from 3 regions - enough to validate the pattern against your real traffic with a 10-minute setup and no code changes to the agent itself. See pricing or jump straight to the platform overview.
