Proxy-first instead of SDK-first. Fully MIT instead of OSS plus EE. Statistical A/B testing and savings recommendations built in.
Langfuse is the most mature OSS option and the safest pick if community size is the deciding factor. Spanlens wins on integration speed (1-line baseURL swap), license clarity (no EE folder), and built-in features like Prompt A/B with t-tests, judge to human correlation, and model-swap savings recommendations.
| Feature | Spanlens | Langfuse |
|---|---|---|
| 1-line baseURL proxy swap | Yes | No |
| OpenTelemetry (OTLP) ingest | Yes | Yes |
| SDKs & framework integrations | Yes | Yes |
| Per-request log with full body | Yes | Yes |
| Cost tracking per request and rollups | Yes | Yes |
| Agent tracing (waterfall span tree) | Yes | Yes |
| Critical Path on agent traces | Yes | No |
| 3σ anomaly detection on latency/cost | Yes | Partial |
| Versioned prompt library | Yes | Yes |
| Prompt A/B side-by-side runner | Yes | Yes |
| Built-in Welch t-test on A/B results | Yes | No |
| Prompt playground | Yes | Yes |
| Gradual prompt rollout via header | Yes | Partial |
| LLM-as-judge scoring | Yes | Yes |
| Human annotation queue | Yes | Yes |
| Judge to human correlation tracking | Yes | Partial |
| Datasets / golden test sets | Yes | Yes |
| Pre-built evaluators marketplace | Partial | Yes |
| Model swap recommendations with $ savings | Yes | No |
| Per-model cost breakdown & budget alerts | Yes | Yes |
| Security scanning (API keys, PII, prompt injection) | Yes | Partial |
| Per-call log-body opt-out header | Yes | Partial |
| Fully MIT (entire repo) | Yes | No |
| Docker Compose self-host | Yes | Yes |
| Managed cloud option | Yes | Yes |
Updated 2026-06-03. Scroll for the grouped view with notes below.
Spanlens is proxy-first. Replace api.openai.com with your Spanlens endpoint and every call is captured. Langfuse requires wrapping clients with their SDK or wiring OTel. That works fine for greenfield apps but gets painful when existing codebases have many call sites.
Every line of Spanlens ships under MIT. Langfuse moved all product features to MIT too, but still keeps an ee/ folder that gates enterprise security and compliance add-ons (SCIM, audit logs, data retention, project RBAC, data masking) behind a commercial license. Spanlens has no ee/ folder: what you self-host is exactly what we run.
Spanlens lets you run prompt variants side by side and gives you a Welch t-test on latency and cost, plus a z-test on error rate, not just average bars. Langfuse has prompt management and experiments, but statistical significance testing is something you build yourself.
Both products let you score traces with humans and with LLMs. Spanlens surfaces the correlation between the two as a first-class metric, so you can tell when your LLM judge starts to drift from human raters. In Langfuse the same correlation is computable but is left as bring-your-own analysis.
Spanlens analyzes your traffic and suggests "swap these gpt-4o classification calls to gpt-4o-mini, $412/mo saved" with the evidence. Langfuse shows cost dashboards, but the swap recommendation is a manual exercise.
For multi-step agents, Spanlens highlights the longest dependency chain, the actual bottleneck, not just the longest span. Langfuse renders waterfall traces but doesn't compute critical path automatically.
Last updated 2026-06-03 · Spot something inaccurate? Let us know.
We don't think every team should pick us. Here's where Langfuse legitimately wins.
Langfuse has been public since 2023 with thousands of GitHub stars and a busy community. If proven OSS adoption is your top criterion, Langfuse is ahead. Spanlens shipped in 2026 with Critical Path tracing and Welch t-test A/B already in v1, capabilities Langfuse has not added.
Langfuse is OTel-native and slots in naturally if your stack already has OTel collectors. Spanlens supports OTLP ingest too, but Langfuse's OTel pedigree is deeper.
Langfuse offers a richer set of pre-built evaluators like toxicity and helpfulness that you can chain. Spanlens leans on LLM-as-judge with your own rubric plus human annotation, which stays flexible when your team's quality criteria don't match a stock evaluator.
Langfuse's datasets feature is mature for building golden test sets and re-running them on every prompt change. Spanlens datasets cover the same flow with a simpler UI; if your golden-set workflow already lives in CI scripts, the surface difference matters less than it looks.
Spanlens is proxy-first. Replace api.openai.com with your Spanlens endpoint and every call is captured. Langfuse requires wrapping clients with their SDK or wiring OTel. That works fine for greenfield apps but gets painful when existing codebases have many call sites.
Every line of Spanlens ships under MIT. Langfuse moved all product features to MIT too, but still keeps an ee/ folder that gates enterprise security and compliance add-ons (SCIM, audit logs, data retention, project RBAC, data masking) behind a commercial license. Spanlens has no ee/ folder: what you self-host is exactly what we run.
Spanlens lets you run prompt variants side by side and gives you a Welch t-test on latency and cost, plus a z-test on error rate, not just average bars. Langfuse has prompt management and experiments, but statistical significance testing is something you build yourself.
Both products let you score traces with humans and with LLMs. Spanlens surfaces the correlation between the two as a first-class metric, so you can tell when your LLM judge starts to drift from human raters. In Langfuse the same correlation is computable but is left as bring-your-own analysis.
Spanlens analyzes your traffic and suggests "swap these gpt-4o classification calls to gpt-4o-mini, $412/mo saved" with the evidence. Langfuse shows cost dashboards, but the swap recommendation is a manual exercise.
For multi-step agents, Spanlens highlights the longest dependency chain, the actual bottleneck, not just the longest span. Langfuse renders waterfall traces but doesn't compute critical path automatically.
Langfuse has been public since 2023 with thousands of GitHub stars and a busy community. If proven OSS adoption is your top criterion, Langfuse is ahead. Spanlens shipped in 2026 with Critical Path tracing and Welch t-test A/B already in v1, capabilities Langfuse has not added.
Langfuse is OTel-native and slots in naturally if your stack already has OTel collectors. Spanlens supports OTLP ingest too, but Langfuse's OTel pedigree is deeper.
Langfuse offers a richer set of pre-built evaluators like toxicity and helpfulness that you can chain. Spanlens leans on LLM-as-judge with your own rubric plus human annotation, which stays flexible when your team's quality criteria don't match a stock evaluator.
Langfuse's datasets feature is mature for building golden test sets and re-running them on every prompt change. Spanlens datasets cover the same flow with a simpler UI; if your golden-set workflow already lives in CI scripts, the surface difference matters less than it looks.
Both tools are good. Pick Spanlens if you want to be running in 60 seconds and want statistical rigor built in. Pick Langfuse if community size and OTel-native is non-negotiable.
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