Evidently AI vs LangSmith
Evidently AI
evidentlyai.com
LangSmith
langchain.comSide by side
| Figure | Evidently AI | LangSmith |
|---|---|---|
| Price from | $80/mo | $39/mo |
| Free plan | Yes | Yes |
| Free trial | Yes | — |
| Open source | 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) | 9.0 (#4) |
| Spec score · AI LLM Evaluation Tools | 9.3 (#2) | 9.1 (#5) |
| Spec score · LLM Observability Tools | 9.3 (#2) | 9.0 (#5) |
| Company profile | LinkedIn lists Evidently AI as a privately held software-development company headquartered in San Francisco, with 2–10 employees and a 2020 founding year. | LinkedIn lists LangChain as privately held, headquartered in San Francisco, with 201–500 employees and 469 employees shown on the page. |
| Integrations | Documented integration examples cover Jupyter and Colab, Streamlit, MLflow, DVCLive, Airflow, Metaflow, FastAPI, PostgreSQL, Prefect, Grafana, Prometheus and AWS SES. | Documented integrations cover OpenAI, Anthropic, Bedrock, Gemini, Mistral, LiteLLM, OpenTelemetry, CrewAI, Vercel AI SDK, PydanticAI, AutoGen and many other frameworks and providers. |
| Purpose | Evidently evaluates, tests, and monitors LLMs, RAG applications, AI agents, and machine-learning models. | LangSmith is a commercial agent engineering platform for building, testing, deploying, monitoring, and improving AI agents. |
| Rating on G2 | 5.0 out of 5 from 1 review | 4.4 out of 5 from 78 reviews. |
| Rating on Product Hunt | 5.0 out of 5 from 2 reviews | 4.8 out of 5 from 19 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. | Enterprise customers can run LangSmith self-hosted or hybrid on Kubernetes in AWS, Google Cloud or Azure. |
| Support | Community support is provided through Discord, GitHub issue reporting, documentation, courses and direct email contact. | The Developer plan includes community support, and the Enterprise plan lists a support SLA. |
| Tracing | Open-source tracing records inputs, outputs, intermediate events and function calls, and allows traces to be exported as tabular datasets. | Tracing records agent conversations and runs step by step, including prompts, model calls, tools, errors, latency and token usage. |
| What it does | Evidently evaluates, tests and monitors LLM applications, RAG systems, AI agents and predictive machine-learning models. | LangSmith is LangChain's framework-agnostic platform for observing, evaluating and deploying AI 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.
LangSmith
LangSmith is a commercial agent-engineering platform for building, evaluating, deploying, monitoring, and improving AI agents. It supports offline evaluations on curated datasets and real-time scoring of production interactions. Evaluation methods include human review, heuristic checks, LLM-as-judge evaluators, pairwise comparisons, and custom Python or TypeScript evaluators. Annotation queues route runs to subject-matter experts for feedback that can calibrate evaluations, improve prompts, or augment datasets. The Playground compares prompt versions or model providers, and LangSmith chat can auto-improve prompts. Observability includes agent and retrieval tracing, token and general cost tracking, trajectory monitoring, online evaluations, and webhook or PagerDuty alerts. Traces can arrive through SDKs or an API from LangGraph, custom Python, or other stacks; SDKs are listed for Python, TypeScript, Go, and Java. Hosting ranges from cloud to hybrid and self-hosted Enterprise options. The Developer plan costs nothing, Plus costs $39 a month, and Enterprise pricing is custom.