Evidently AI vs Langfuse
Evidently AI
evidentlyai.com
Langfuse
langfuse.comSide by side
| Figure | Evidently AI | Langfuse |
|---|---|---|
| Price from | $80/mo | $29/mo |
| Free plan | Yes | Yes |
| Free trial | Yes | — |
| Open source | Yes | Yes |
| Web | Yes | Yes |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 5 | 10 |
| Spec score · Model Monitoring Software | 9.3 (#1) | 9.0 (#3) |
| Spec score · AI LLM Evaluation Tools | 9.3 (#2) | 9.1 (#4) |
| Spec score · LLM Observability Tools | 9.3 (#2) | 9.0 (#4) |
| Integrations | Documented integration examples cover Jupyter and Colab, Streamlit, MLflow, DVCLive, Airflow, Metaflow, FastAPI, PostgreSQL, Prefect, Grafana, Prometheus and AWS SES. | Langfuse lists more than 100 integrations spanning OpenTelemetry, Python and TypeScript, model providers, agent frameworks, gateways, and developer tools. |
| Metrics | The Python library provides more than 100 evaluation metrics. | Metrics cover quality, cost, latency, and volume, with breakdowns by user, session, geography, feature, model, and prompt version. |
| Rating on G2 | 5.0 out of 5 from 1 review | 4.5 out of 5 from 1 review |
| Rating on GetApp | 0 out of 5 from 0 reviews | 0 out of 5 from 0 reviews |
| Self-hosting | A local or remote Evidently service can be run with filesystem, SQLite, Postgres or other SQL-like storage, plus S3-compatible backends. | The OSS edition can be deployed with Docker Compose, Kubernetes Helm, or Terraform guides for AWS, GCP, and Azure, including offline or air-gapped installations. |
| Support | Community support is provided through Discord, GitHub issue reporting, documentation, courses and direct email contact. | Hobby includes community support via GitHub, Core includes in-app support, Pro includes prioritized in-app support, and Enterprise includes support SLA and a named lead support engineer. |
| What it does | Evidently evaluates, tests and monitors LLM applications, RAG systems, AI agents and predictive machine-learning models. | Langfuse is an open-source AI engineering platform for tracing, prompt management, evaluations, metrics, and iterative improvement of LLM applications and agents. |
Plans
Evidently AI
- DeveloperFree
- Open-sourceFree
- Pro$80 a month
- EnterprisePriced on request
- StartupsPriced on request
Langfuse
- HobbyFree
- HobbyFree
- Core$29 a month
- Core$29 a month
- Pro$199 a month
- Pro$199 a month
- Teams Add-on$300 a month
- Teams Add-on$300 a month
- Enterprise$2,499 a month
- Enterprise$2,499 a month
In their own words
Evidently AI
Evidently AI evaluates, tests, and monitors LLMs, RAG applications, AI agents, and machine-learning models. Its open-source Python framework is licensed under Apache 2.0 and provides more than 100 evaluation metrics. Automated evaluations measure output quality, safety, and reliability, can run in pipelines, and produce visual reports. The platform includes tracing, storage for application data and evaluation runs, test-dataset management, and dashboards. Teams can generate realistic, edge-case, or adversarial inputs tailored to a use case. Live monitoring tracks quality checks and can surface drift, regressions, and emerging risks. Red-team testing targets jailbreaks, prompt injections, data leaks, harmful content, and prompt leakage. Predictive ML workflows include performance, drift, and data-quality metrics. Evidently Cloud can send alerts to Slack, Discord, and email. Open-source support is through Discord and documentation; Pro includes email support. The Developer plan is free and limited to 10,000 rows per month, 1GB of snapshots, 3 projects, and 2 seats. Pro costs $80 a month, with additional rows and storage charged separately.
Langfuse
Langfuse is an open-source AI engineering platform for teams debugging, analyzing, and iterating on AI agent applications. Its tracing records prompts, model responses, token use, latency, tool calls, retrieval steps, and metadata. Prompt Management stores, versions, deploys, and tests prompts, then links them to traces. Evaluation features include LLM-as-a-judge, code evaluators, user feedback, manual labeling, datasets, and experiments. Integrations include Python and JS/TS SDKs, OpenTelemetry, an API, CLI, MCP Server, and popular AI frameworks. Teams can self-host the MIT-licensed core on customer infrastructure using Docker, with options including Docker Compose, Kubernetes, AWS, and Azure. Langfuse reports annual SOC 2 Type 2 and ISO 27001 audits, external penetration tests, GDPR compliance, a DPA, and a HIPAA-ready region. Its Cloud traffic uses TLS 1.2+ and stored data uses AES-256 encryption. The free Hobby plan is free and includes 50k units per month, 30 days of data access, two users, and community support via GitHub. Core costs $29 a month, billed monthly.