000 compare

AgentPing vs Langfuse.

Both tools track LLM work in production, but they're built for different shapes of team. Langfuse is open-source, self-hostable, and great for teams that need on-premise observability with mature trace and eval surfaces. AgentPing is a hosted platform built around the agent run as the unit of analysis, with schedule freshness and per-customer cost attribution as first-class features.

001 what langfuse does well

Open source, self-hostable, a deep tracing surface.

Langfuse is the leading open-source LLM observability project. Strong trace tree, prompt management, dataset-driven evals, integrations with most major frameworks. The self-hostable option is genuinely useful for teams with data-residency constraints, and the active OSS community means features land fast.

002 where agentping differs

Agent-aware out of the box, cost, monitoring, and quality on one event.

AgentPing's design starts from the agent run, not the trace. From one telemetry record per run, we derive cost attribution (per agent, customer, feature), schedule freshness (a missed cron pages within a tolerance window), and quality scoring with statistical drift detection on the production stream. The unit and the views are different.

Side-by-side

capabilities honest read
Capability Langfuse AgentPing
Open source / self-hostable Yes (MIT) Hosted SaaS at launch
Unit of analysis Trace / span Agent run
Trace tree depth First-class Per-run, parent/child supported
Cost attribution by customer / feature Via custom properties First-class, retroactive once tagged
Cache-aware token accounting Manual Default (Anthropic + OpenAI)
Schedule freshness (missed cron alerts) Not a focus Per-agent cron + tolerance window
LLM-as-judge on production sample Yes, batch-oriented Sample-based with calibration anchors
Live drift detection on production scores Limited z-score on 14-day baseline
Anomaly detection on per-agent spend Not a focus 14-day baseline, alert routes per agent
003 when to pick which

Self-hosted plus traces, or hosted plus agents.

Pick the tool that matches the shape of your team. Open-source self-hostable with strong trace ergonomics is Langfuse. Hosted, agent-centric, with cost and schedule freshness built in is AgentPing. Many teams test both before settling.

Pick Langfuse if

  • You need to self-host for data-residency or air-gapped reasons.
  • Your primary workflow is trace inspection and prompt management.
  • You want a mature OSS community and direct control over the deployment.
  • Offline batch evaluation is your dominant quality workflow.

Pick AgentPing if

  • The agent is your unit of analysis, not the trace.
  • You need cost attribution per customer or feature, not just per team.
  • You run scheduled agents and need missed-cron alerts within a grace window.
  • You want continuous quality scoring on the production stream with statistical drift detection.
  • You prefer a hosted SaaS with EU and US data planes already separated at the edge.
004 frequently asked
Langfuse is open source. Can AgentPing be self-hosted?
Not at launch. AgentPing is a hosted SaaS with EU and US data planes. Langfuse is genuinely the right fit for teams that need self-hosting for data-residency or air-gapped reasons. Enterprise on-premise deployment is on AgentPing's roadmap; talk to us if it's a hard requirement.
What does AgentPing do that Langfuse doesn't?
Three things. First, schedule freshness: a cron expression per agent with a tolerance window pages on a missed scheduled run. Langfuse does not watch for absence-of-signal. Second, per-customer cost attribution as a first-class column, not a custom property. Third, anomaly detection on a per-agent spend baseline, with the alert fired the day the spike starts.
Does Langfuse do LLM-as-judge?
Yes, and it's mature for offline batch evaluation. AgentPing's focus is the live production stream: sample-based judging on every team's real input distribution, calibration anchors to keep scores comparable across rubric versions, and statistical drift detection on the rolling baseline. The two approaches answer slightly different questions; many teams run both.
How does pricing compare?
Langfuse Cloud is usage-priced with a generous free tier. AgentPing is flat-tier (Starter £99, Team £249, Business £499 per month) with predictable allowances. For low-volume usage, Langfuse Cloud is cheaper. For predictable medium-volume usage with the agent-aware features, AgentPing is competitive. Annual billing saves 20% on every tier.
Can I use Langfuse and AgentPing together?
Yes. Some teams use Langfuse for the offline eval workflows and AgentPing for production cost attribution and schedule freshness. The SDKs do not conflict; events flow independently. That said, the dashboard overlap means most teams settle on one for the daily-driver surface.
005 read next

How AgentPing implements cost, monitoring, and quality.

Features → What is AI agent observability? → Docs →