DeepEval vs Evidently AI
DeepEval
deepeval.com
Evidently AI
evidentlyai.comSide by side
| Figure | DeepEval | Evidently AI |
|---|---|---|
| Price from | $200/mo | $80/mo |
| Free plan | Yes | Yes |
| Free trial | — | Yes |
| Open source | Yes | Yes |
| Web | Yes | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 5 | 5 |
| Spec score · AI LLM Evaluation Tools | 9.4 (#1) | 9.3 (#2) |
| Integrations | Listed integrations include LangChain, Pydantic AI, OpenAI Agents, LangGraph, AWS AgentCore, Strands, Google ADK, LlamaIndex, and CrewAI. | Documented integration examples cover Jupyter and Colab, Streamlit, MLflow, DVCLive, Airflow, Metaflow, FastAPI, PostgreSQL, Prefect, Grafana, Prometheus and AWS SES. |
| Metrics | The project advertises more than 50 metrics spanning quality, agents, RAG, conversations, safety, voice and multimodal use cases. | The Python library provides more than 100 evaluation metrics. |
| Purpose | DeepEval is an open-source LLM evaluation framework for building evaluation pipelines to test AI systems. | Evidently evaluates, tests, and monitors LLMs, RAG applications, AI agents, and machine-learning models. |
| Rating on G2 | 4.6 out of 5 from 5 reviews | 5.0 out of 5 from 1 review |
| Self-hosting | Confident AI's enterprise service can be deployed on the customer's own infrastructure or used in the vendor cloud. | A local or remote Evidently service can be run with filesystem, SQLite, Postgres or other SQL-like storage, plus S3-compatible backends. |
| Synthetic data | DeepEval includes synthetic dataset generation for difficult-to-collect edge cases. | Synthetic datasets can be generated for realistic edge cases and adversarial prompts. |
| Tracing | DeepEval traces agent steps so they can be graded and inspected in the terminal and test runner. | Open-source tracing records inputs, outputs, intermediate events and function calls, and allows traces to be exported as tabular datasets. |
| What it does | DeepEval is an open-source framework for testing and evaluating LLM applications locally or in CI/CD. | Evidently evaluates, tests and monitors LLM applications, RAG systems, AI agents and predictive machine-learning models. |
Plans
DeepEval
- DeepEvalFree
- FreeFree
- Starter$200 a month
- Team$2,000 a month
- EnterprisePriced on request
Evidently AI
- DeveloperFree
- Open-sourceFree
- Pro$80 a month
- EnterprisePriced on request
- StartupsPriced on request
In their own words
DeepEval
DeepEval is a free, open-source framework for building evaluation pipelines to test AI systems. Its Pytest-native evaluations run in CI/CD or as Python scripts, and the framework lists more than 50 research-backed metrics, including checks for hallucination, faithfulness, answer relevancy, summarization, toxicity and bias. It evaluates text, images and audio, including conversational and voice interactions, using methods such as G-Eval, DAG, QAG and JevEval. DeepEval can generate synthetic goldens from a knowledge base and model conversations across user personas. It also traces agent steps so they can be graded and inspected in a terminal or test runner. Integrations cover agent frameworks including LangChain, Pydantic AI, OpenAI Agents, LangGraph, LlamaIndex and CrewAI, alongside a range of model providers. By default, it sends basic telemetry to PostHog, excluding personally identifiable information and stored results; users can opt out with an environment variable. The maker describes DeepEval OS as for pre-production testing, with results in local files and an engineer-owned test runner. Enterprise capabilities are offered through Confident AI Evals, with self-hosted or maker-cloud deployment options.
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.