Still running a separate tool
just for LLM traces?
$10 per million spans. OpenTelemetry native. LLM traces unified with your infrastructure.
Oodle can help you if
- ✓You run agents in production at scale (millions of traces/day)
- ✓You need LLM traces correlated with backend infrastructure
- ✓You want out-of-the-box insights and anomaly detection for silent failures
- ✓You want one platform for traces, logs, metrics, and APM
- ✓You want predictable costs that don't scale per-event
Oodle is probably not for you if
- —You only need LLM observability - no infra monitoring
- —You're at early stage with low trace volume where per-unit is cheap
Trusted by teams running millions of agent traces per day
Why teams choose Oodle over Langfuse
Langfuse focuses on LLM tracing. Oodle unifies it with your full infrastructure stack.
Separate Data Silo
Langfuse only sees LLM calls. When an agent is slow, is it the model, the API call, or the database? You can't tell - you need another tool. Oodle correlates everything in one trace.
Cost at Production Scale
Langfuse's per-unit pricing grows with trace volume (graduated tiers from $8 to $6 per 100K units). At 3M+ traces/day, S3-based storage is 5–10x cheaper than per-event metering.
Operational Overhead
Self-hosted Langfuse requires maintaining ClickHouse, Postgres, Redis, and S3. Cloud Langfuse solves this but adds per-unit cost. Oodle is fully managed - zero infrastructure to run.
No Built-in Anomaly Detection
Langfuse shows you traces after the fact - you have to know what to look for. Oodle detects silent failures and anomalies automatically: latency spikes, error rate changes, and cost outliers surface without manual queries.
Oodle vs Langfuse - Feature Comparison
| Feature | Oodle AI | Langfuse |
|---|---|---|
| Pricing model | $0.30/GB (~$10 per million spans) | Per observation (event unit) |
| Infrastructure trace correlation | Built-in - LLM spans live in the same traces as API, DB, cache calls | Not available - separate data silo |
| Instrumentation | OpenTelemetry native - GenAI semantic conventions, single collector for LLM + infra | Langfuse SDK or OTLP endpoint (separate pipeline from infra traces) |
| Sampling | None - 100% trace retention | None on paid plans |
| Prompt versioning & evals | Yes - built-in LLMOps workflow | Yes - core feature |
| Self-hosting option | BYOC (Bring Your Own Cloud) available | Yes - open source, self-hostable |
| Scale (production proven) | 3M+ agent traces/day, S3-native serverless | Varies by deployment - self-hosted requires ops |
| Logs, metrics, APM | Full unified platform | Not available - LLM observability only |
| AI-powered debugging | Natural language queries, root cause analysis across all signals | LLM-as-a-Judge evals; no NL query debugging |
| Out-of-the-box insights | Pre-built dashboards for token usage, cost, latency, error rates across models | Manual dashboard setup required |
| Anomaly detection | Automatic detection of silent failures, latency spikes, cost outliers | Not available - manual monitoring only |
| Compliance | SOC 2 Type II, ISO 27001, GDPR | SOC 2 Type II, ISO 27001, GDPR (cloud) |
$10 per Million Spans
Langfuse charges per observation. Oodle charges $0.30/GB - that works out to ~$10 per million spans at any scale.
Switch in Under an Hour
Point your OTel exporter at Oodle. No code changes required.
How to Switch
Add OTel Instrumentation
15 minUse OpenLLMetry, LangChain, Vercel AI SDK, or any library emitting GenAI semantic conventions.
Point OTLP Exporter at Oodle
5 minOne config line. Same endpoint for LLM and backend traces - no new collector needed.
See Unified Traces
InstantLLM calls, tool invocations, and infra spans appear in one unified trace waterfall.
Who is Switching
“Your MCP server is amazing. I've stopped logging into the UI.”
Ashish Gupta
Principal Engineer, Fello
LangfuseProven at enterprise scale
Enterprise-ready from day one
Security and compliance built without compromise
Ready to replace Langfuse?
Go live in 15 minutes. No data limits. No infrastructure changes.
Fello moved 3M+ agent traces / day from Langfuse into Oodle - cut LLM trace costs by 80%
Read the Fello case study