Evidently AI vs OpenLIT
Evidently AI
evidentlyai.comOpenLIT
openlit.ioSide by side
| Figure | Evidently AI | OpenLIT |
|---|---|---|
| Price from | $80/mo | Free |
| 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 | 3 |
| Spec score · Model Monitoring Software | 9.3 (#1) | 8.4 (#13) |
| Spec score · LLM Observability Tools | 9.3 (#2) | 8.5 (#10) |
| Documentation depth | The documentation includes getting-started guides, metric catalogs, cookbook examples, evaluation guides, FAQs and a changelog. | The documentation includes provider-specific SDK guides, observability pages, controller deployment material, CLI configuration and an indexed documentation file. |
| Integrations | Documented integration examples cover Jupyter and Colab, Streamlit, MLflow, DVCLive, Airflow, Metaflow, FastAPI, PostgreSQL, Prefect, Grafana, Prometheus and AWS SES. | The home page advertises more than 56 integrations spanning LLMs, agent frameworks, vector databases and GPUs. |
| Rating on G2 | 5.0 out of 5 from 1 review | 5.0 out of 5 from 1 review. |
| Self-hosting | A local or remote Evidently service can be run with filesystem, SQLite, Postgres or other SQL-like storage, plus S3-compatible backends. | Docker Compose runs the UI on port 3000, OTLP receivers on ports 4317 and 4318, and ClickHouse for traces, metrics and logs; Kubernetes Helm deployment is also documented. |
| Support | Community support is provided through Discord, GitHub issue reporting, documentation, courses and direct email contact. | The OSS plan includes community support on GitHub. |
| Tracing | Open-source tracing records inputs, outputs, intermediate events and function calls, and allows traces to be exported as tabular datasets. | LLM tracing can capture calls, tools, and retrievals, with filters for user, session, cost, latency, and metadata. |
| What it does | Evidently evaluates, tests and monitors LLM applications, RAG systems, AI agents and predictive machine-learning models. | OpenLIT is an OpenTelemetry-native platform for tracing, evaluating, guarding and improving AI agents and coding agents. |
Plans
Evidently AI
- DeveloperFree
- Open-sourceFree
- Pro$80 a month
- EnterprisePriced on request
- StartupsPriced on request
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.
OpenLIT
OpenLIT is an open-source platform for tracing and evaluating LLM activity, managing prompts, and tracking costs. Built on OpenTelemetry, it can capture model calls, tool use, and retrievals, with filters for user, session, cost, latency, and metadata. Evaluations can use an LLM judge, heuristics, or human review on production traffic or experiments. Prompt Hub provides versioning, dynamic variables, API retrieval, unlimited prompt fetching, and AI-assisted improvement. OpenGround supports side-by-side model comparisons for latency, tokens, and cost, including comparisons using a user’s own key. Coding agent monitoring covers sessions, costs, tokens, repository activity, and code impact. The site lists more than 56 integrations across LLMs, agent frameworks, vector databases, and GPUs. OpenLIT accepts OTLP data and can be self-hosted with Docker Compose or Kubernetes Helm. Its Apache 2.0 license and site description note that self-hosting keeps prompts, traces, and secrets in the user’s environment. The OSS plan costs nothing and includes unlimited self-hosted usage, users, projects, and environments.