Evidently AI

Evidently AI is an evaluation and monitoring toolkit for LLMs, AI agents, RAG systems, and predictive ML models. It suits teams that want to build quality checks into development and keep watching systems after deployment. The open-source framework is a strong starting point; Pro is the clearer fit once hosted usage needs exceed its free quotas.

Overview

Evidently combines a Python library with a platform for evaluating, testing, and monitoring AI applications and machine-learning models. Its Apache 2.0 framework includes more than 100 evaluation metrics, while the platform adds tracing, storage for application data and evaluation runs, test-dataset management, and dashboards.

Automated evaluations measure output quality, safety, and reliability, and can run in pipelines with visual reports. For deployed systems, a live dashboard tracks evaluation results and quality checks to help catch drift, regressions, and emerging risks. Predictive ML workflows also get metrics for model performance, data drift, and data quality, making Evidently relevant beyond LLM observability.

Teams can generate realistic, edge-case, or adversarial inputs tailored to a use case, then use red-team testing to probe for jailbreaks, prompt injections, data leaks, harmful content, and prompt leakage. The platform includes LLM and agent tracing, LLM evaluations, and prompt management. Evidently Cloud can send monitoring alerts to Slack, Discord, and email.

Key features

• Evaluation and testing: More than 100 metrics and pipeline-ready automated evaluations give teams a way to check AI output quality, safety, and reliability. Visual reports make results reviewable without reducing the work to a single score.
• Monitoring: A live dashboard tracks quality checks and evaluation results, helping teams look for drift, regressions, and emerging risks. Slack, Discord, and email alerts extend monitoring beyond the dashboard.
• Adversarial coverage: Tailored synthetic inputs and red-team tests target concrete failure modes, including prompt injection and data leakage. This is useful for teams assessing risks before or during deployment, though the listed capabilities do not establish that testing replaces a broader security review.
• Predictive ML support: Built-in performance, data-drift, and data-quality metrics let ML teams use the same product family for conventional predictive workflows as well as generative AI.

Pricing

Evidently has two free offerings with different emphases. Open-source is free, billed Free, and includes core AI evaluation and testing, 100+ metrics, local self-hosted dashboards, API/CLI access, and Discord/docs support. It is the natural choice for teams comfortable managing the framework themselves; it does not include the hosted quotas and seats spelled out in Developer.

Developer is also free, billed Free, and provides 10,000 rows/month, 1GB snapshots, 3 projects, 2 seats, and community support. Its caps make it a sensible no-cost entry for a small project, but limited rows, storage, projects, and seats may constrain broader team use.

Pro costs $80 a month, billed Monthly Billing. It raises the allowances to 100,000 rows/month, 100GB snapshots, 10 projects, and 5 seats, and adds email support. Usage beyond the quota costs $10 per 10,000 additional rows/month and $1 per additional GB stored, so teams with growing data volume should account for overages rather than treating the base price as an all-in ceiling.

Enterprise has custom pricing and custom limits, SSO, roles, audit logs, private cloud, and premium support. It is aimed at organizations that need those deployment and administration controls rather than a fixed self-serve quota. Startups is a special offer with all core features, free credits toward Pro, and premium support; it has no published price. A free trial is available, and the pricing FAQ says teams can request a trial of the self-hosted Enterprise version by contacting Evidently.

Platforms

Evidently supports API, Linux, macOS, Windows, web, and self-hosted use, with both deployment options available. That breadth accommodates local framework workflows as well as platform-based monitoring. The open-source offering has community support through Discord and documentation; Pro adds email support, while Enterprise includes premium support.

Who it's for

Evidently is a good fit for developers and ML teams that need repeatable evaluation, tracing, dataset management, and monitoring across LLMs, agents, RAG applications, or predictive models. Its open-source framework suits teams that want local control and can rely on community support. Developer fits small hosted projects, while Pro is more appropriate when the team needs higher quotas, more projects and seats, or email support. Organizations requiring private cloud, audit logs, or custom access controls should consider Enterprise.

Pros and cons

• Pros: Broad coverage across AI and predictive ML. Evaluation, red teaming, tracing, monitoring, and predictive-model metrics address both pre-deployment checks and ongoing oversight.
• Pros: A substantial open-source entry point. Apache 2.0 licensing, 100+ metrics, local dashboards, and API/CLI access allow teams to start without a paid plan.
• Pros: Clear path to team-scale use. Pro expands Developer's monthly rows, storage, projects, and seats, and adds email support.
• Cons: Free hosted use is bounded. Developer's 10,000 rows/month, 1GB snapshots, 3 projects, and 2 seats can be restrictive as adoption grows.
• Cons: Pro has metered overages. Extra rows and storage incur additional charges, so higher-volume teams need to watch usage.
• Cons: Support depends on plan. The open-source and Developer options rely on community support; email support begins with Pro.

Alternatives

Deepchecks is worth considering for a smaller setup centered on one AI application: its free Basic plan covers up to 3 seats, 1 AI application, 5K DPUs/month, 3 months of data retention, and unlimited prompt-based metrics.

NannyML may suit teams focused on model monitoring that want a self-managed open-source option or a paid Starter plan with 2 models and 10 M predictions for $399 a month, billed $399/month.

Opik is another freemium option; its core observability and evaluation features are open source and can be downloaded and run locally.

Arthur offers a free plan with defined job, span, inference, evaluation, project, and retention limits, making it an alternative for teams comparing capped free usage.

Galileo may be a better fit for teams seeking its Pro plan's 50,000 traces per month, standard RBAC, advanced analytics and insights, and dedicated Slack support; it costs $100 a month, billed yearly.

Radicalbit AI Monitoring is a free option with an open-source plan for readers comparing monitoring tools.

Arize AX offers a free cloud plan with unlimited users and evaluations, alongside limits of 10 issues/month, 25k trace spans/month, 1 GB ingestion/month, and 15-day retention.

SUPERWISE is a paid alternative with a 30-day free period before its Solo plan costs $10 a month; that plan covers one Sentinel deployment for development use on a shared services platform.

For category comparisons, see Machine Learning Model Monitoring Software, Model Monitoring Software, LLM Observability Tools, and AI LLM Evaluation Tools.

Verdict

Choose Evidently AI if your team wants one toolkit for evaluating and monitoring LLMs, agents, RAG applications, and predictive ML, with an open-source route to local use and a paid tier for larger hosted workloads. Its breadth and 100+ metrics are the main reasons to choose it; the main reasons to look elsewhere are the small free hosted quotas and Pro's usage-based overages.