LangfuseOpen Source Agent Evals & Observability
vs
ZenMLAI orchestration, on the infra you choose
Comparison · MLOps Platforms · weekly run Nov 2
Langfuse vs ZenML
Which one do AI engines recommend, and how do they differ on price, fit and features?
The short answer
ZenML is the stronger AI pick in MLOps Platforms: it has a visibility score of 6 versus 0.3 for Langfuse.
Choose Langfuse if…
- You want a lower entry price ($29 vs $39)
- You need more native integrations (100+ vs 66+)
- You need a self-hosted or on-premises option
- You need HIPAA-ready region or GDPR coverage
Choose ZenML if…
- You want the brand AI engines rank higher (6 vs 0.3)
- AI answers describe it more positively (55% vs 50% positive)
- Flat tiers pricing suits your budget better than usage-based
- Free and fully transparent open-source option
AI visibility side by side
Latest weekly run · Nov 2
| Metric | Langfuse | ZenML |
|---|---|---|
| Visibility score | 0.3% | 6% |
| Change vs last week | New | New |
| Rank in MLOps Platforms | #51 | #14 |
| Share of voice | 0.2% | 1.5% |
| Mentions this run | 1 | 11 |
| Positive in AI answers | 50% | 55% |
The stronger value in each row is marked with a ✓.
Visibility score trend
Weekly runs over the last 60 days
Langfuse 0pp · ZenML 0pp over 0 weekly runs.
Engine by engine
ZenML leads on 3 of 3 engines
ChatGPT
AI Mode
Perplexity
LangfuseZenML
Head to head in AI answers
0 engine answers name both brands
0Langfuse ranked higher
0ZenML ranked higher
Features, pricing & fit
| Dimension | Langfuse | ZenML |
|---|---|---|
| Pricing model | Usage-based | Flat tiers |
| Entry price | Free; paid from $29 / mo | Free; paid from $39 / mo |
| Free plan | Yes | Yes |
| Deployment | Langfuse Cloud in US, EU, Japan and HIPAA regions, or self-hosted under the MIT license | Self-hosted open source, or ZenML's cloud. Enterprise runs on-prem or air-gapped in the customer's cloud, with data and compute staying in the customer's VPC. |
| Integrations | 100+ | 66+ |
| Compliance | SOC 2 Type II, ISO 27001, HIPAA-ready region, GDPR | SOC 2 Type II, ISO 27001 |
| Support | 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 | Community Slack, Email, Dedicated support with SLA on Enterprise, Workshops and architecture reviews on Enterprise |
| Founded | — | 2021 |
| Headquarters | — | Munich, Germany |
| Employees | — | 11–50 |
| Funding | Seed · $4M raised | Seed extension · $6M raised |
| Best for | Engineering teams building LLM applications and AI agents that want open-source, self-hostable tracing and evaluation | Data science and ML engineering teams that want pipelines and AI agents run on their own infrastructure, and agent teams that want to find the evals they are missing. |
Plans side by side
Pros and cons
Themes from the AI answers that mention each brand
Langfuse
- Discounted cloud pricing for qualifying early-stage startups
- Well suited to evaluating and debugging LLM or agent behavior
ZenML
- Free and fully transparent open-source option
- Open-source and Kubernetes-native MLOps tool
- Recommended starting point for MLOps lifecycle coverage
- Among the strongest MLOps platform alternatives
Data collected ·ChatGPT, Perplexity, Google AI Mode·coverage 100%·next run
Cite as: SlateIndex, “Langfuse vs ZenML”, https://slateindex.ai/compare/langfuse-vs-zenml, data of Nov 2, 2026. Licence: CC BY 4.0.