HoneycombObservability built for the AI era
vs
LangfuseOpen Source Agent Evals & Observability
Comparison · Observability · weekly run Nov 3
Honeycomb vs Langfuse
Which one do AI engines recommend, and how do they differ on price, fit and features?
The short answer
Honeycomb is the stronger AI pick in Observability: it has a visibility score of 14.1 versus 1.4 for Langfuse.
Choose Honeycomb if…
- You want the brand AI engines rank higher (14.1 vs 1.4)
- High-cardinality debugging
- Deep distributed tracing for complex distributed systems
- Predictable event-based pricing
Choose Langfuse if…
- You want a lower entry price ($29 vs $150)
- AI answers describe it more positively (50% vs 47% positive)
- You need a self-hosted or on-premises option
- You need ISO 27001 or HIPAA-ready region or GDPR coverage
AI visibility side by side
Latest weekly run · Nov 3
| Metric | Honeycomb | Langfuse |
|---|---|---|
| Visibility score | 14.1% | 1.4% |
| Change vs last week | New | New |
| Rank in Observability | #9 | #27 |
| Share of voice | 5.2% | 0.4% |
| Mentions this run | 31 | 2 |
| Positive in AI answers | 47% | 50% |
The stronger value in each row is marked with a ✓.
Visibility score trend
Weekly runs over the last 60 days
Honeycomb 0pp · Langfuse 0pp over 0 weekly runs.
Engine by engine
Honeycomb leads on 3 of 3 engines
ChatGPT
AI Mode
Perplexity
HoneycombLangfuse
Head to head in AI answers
0 engine answers name both brands
0Honeycomb ranked higher
0Langfuse ranked higher
Features, pricing & fit
| Dimension | Honeycomb | Langfuse |
|---|---|---|
| Pricing model | Usage-based | Usage-based |
| Entry price | Free; paid from $150 / mo | Free; paid from $29 / mo |
| Free plan | Yes | Yes |
| Deployment | Cloud-hosted, with a Honeycomb Private Cloud option | Langfuse Cloud in US, EU, Japan and HIPAA regions, or self-hosted under the MIT license |
| Integrations | 100+ | 100+ |
| Compliance | SOC 2 Type II | SOC 2 Type II, ISO 27001, HIPAA-ready region, GDPR |
| Support | Honeycomb Support on Pro plans, Enterprise-grade support with onboarding on Enterprise plans, Installation and enablement services as an add-on | Community support via GitHub, In-app support, Private Slack or MS Teams channel on Pro and Enterprise plans, Named lead support engineer on Enterprise plans |
| Founded | 2016 | — |
| Headquarters | San Francisco, CA, United States | — |
| Employees | 201–500 | — |
| Funding | Series C | Seed · $4M raised |
| Best for | Engineering teams running distributed services and AI agents in production, including dev, ops, SRE, and platform teams | Engineering teams building LLM applications and AI agents that want open-source, self-hostable tracing and evaluation |
Plans side by side
Pros and cons
Themes from the AI answers that mention each brand
Honeycomb
- High-cardinality debugging
- Deep distributed tracing for complex distributed systems
- Predictable event-based pricing
- Highly rated by Gartner Peer Insights
- Recognized as a visionary for high-cardinality data exploration
- Self-hosted backend must be managed and scaled by the team
Langfuse
- Discounted cloud pricing for qualifying early-stage startups
- Well suited to evaluating and debugging LLM or agent behavior
Data collected ·ChatGPT, Perplexity, Google AI Mode·coverage 100%·next run
Cite as: SlateIndex, “Honeycomb vs Langfuse”, https://slateindex.ai/compare/honeycomb-vs-langfuse, data of Nov 3, 2026. Licence: CC BY 4.0.