Evidently vs whylogs vs NannyML: Self-Hosted Model Monitoring Guide 2026

When you deploy a machine learning model to production, the work is just beginning. Models degrade over time as real-world data shifts away from training distributions. Without active monitoring, you won’t know your model’s predictions have become unreliable until users start complaining.

Self-hosted model monitoring tools give you full visibility into data drift, concept drift, and model performance — without sending sensitive production data to third-party SaaS platforms. This guide compares three leading open-source options: Evidently, whylogs, and NannyML.

Why Self-Host Model Monitoring

Running your own model monitoring stack offers several advantages over cloud-hosted alternatives:

• Data privacy — production data never leaves your infrastructure, critical for healthcare, finance, and regulated industries
• No vendor lock-in — open-source tools integrate with your existing observability stack (Prometheus, Grafana, OpenTelemetry)
• Cost control — no per-request or per-event pricing that scales unpredictably with traffic
• Full customization — define your own drift detection thresholds, custom metrics, and alerting rules
• Offline capability — monitoring works in air-gapped or restricted network environments

Tool Overview

Evidently is the most comprehensive and actively maintained option. It provides 100+ built-in metrics covering data drift, data quality, and model performance, with interactive HTML reports and a service mode for continuous monitoring. It has the largest community and the most frequent releases.

whylogs takes a different approach — it creates compact statistical profiles (“sketches”) of your data that can be stored, compared, and shared. Its standout feature is privacy-preserving data collection using differential privacy techniques, making it ideal for environments where raw data cannot be logged or inspected.

NannyML specializes in post-deployment performance estimation. Its unique selling point is estimating model performance metrics (like accuracy, ROC-AUC, precision) without requiring ground truth labels — solving the “cold start” problem where you need to know if your model is degrading before labeled feedback data becomes available.

Installation and Setup

All three tools are Python packages installable via pip. They can run as libraries within your application, or be deployed as standalone services.

Evidently

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pip install evidently

For the monitoring service with web UI:

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pip install evidently[service]
evidently --config config.yaml

whylogs

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pip install whylogs

For streaming support:

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pip install whylogs[spark] whylogs[viz]

NannyML

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pip install nannyml

Docker Compose Deployments

Running model monitoring as a Docker container is the recommended approach for production. Here are Docker Compose configurations for each tool.

Evidently Monitoring Service

Evidently provides a service mode that exposes a REST API for collecting snapshots and a web UI for viewing reports.

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version: "3.8"

services:
  evidently:
    image: evidentlyai/evidently:latest
    container_name: evidently-monitoring
    ports:
      - "8000:8000"
    volumes:
      - ./config.yaml:/app/config.yaml:ro
      - ./snapshots:/app/snapshots
    command: >
      evidently --port 8000
        --workspace ./snapshots
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
      interval: 30s
      timeout: 10s
      retries: 3

whylogs with Local Storage

whylogs profiles can be stored locally and visualized through Jupyter notebooks, or sent to a WhyLabs workspace.

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version: "3.8"

services:
  whylogs-writer:
    image: python:3.11-slim
    container_name: whylogs-writer
    volumes:
      - ./profiles:/app/profiles
      - ./monitoring_script.py:/app/monitoring_script.py:ro
    working_dir: /app
    command: >
      bash -c "pip install whylogs &&
      python monitoring_script.py"
    environment:
      - WHYLOGS_DIR=/app/profiles
    restart: unless-stopped

NannyML with JupyterLab

NannyML is primarily used through Python notebooks for analysis and visualization.

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version: "3.8"

services:
  nannyml:
    image: jupyter/scipy-notebook:latest
    container_name: nannyml-analysis
    ports:
      - "8888:8888"
    volumes:
      - ./notebooks:/home/jovyan/work
      - ./data:/home/jovyan/data
    command: >
      start-notebook.sh
        --NotebookApp.token=''
        --NotebookApp.password=''
    environment:
      - GRANT_SUDO=yes
    restart: unless-stopped

Data Drift Detection Comparison

Data drift detection is the core function of any model monitoring system. Here is how each tool approaches it.

Evidently: 100+ Built-in Metrics

Evidently ships with the most extensive collection of drift detection methods out of the box:

• Numerical features: Kolmogorov-Smirnov test, Wasserstein distance, PSI (Population Stability Index), Jensen-Shannon divergence
• Categorical features: Chi-squared test, Jensen-Shannon divergence, PSI
• Text features: Language detection, word overlap, embedding-based distance
• Data quality: Missing values, constant columns, out-of-range values, new categorical values

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from evidently.metric_preset import DataDriftPreset
from evidently.report import Report
from evidently.test_preset import DataDriftTestPreset
from evidently.test_suite import TestSuite

# Generate a drift detection report
report = Report(metrics=[DataDriftPreset()])
report.run(reference_data=reference_df, current_data=current_df)
report.save_html("drift_report.html")

# Run a test suite for CI/CD pipelines
tests = TestSuite(tests=[DataDriftTestPreset()])
tests.run(reference_data=reference_df, current_data=current_df)
result = tests.as_dict()
# Fails if any drift exceeds configured thresholds

whylogs: Statistical Profiling with Constraints

whylogs takes a fundamentally different approach. Instead of running statistical tests on each comparison, it creates compact statistical profiles (called “sketches”) that summarize the distribution of each column:

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import whylogs as why

# Profile your dataset
profile = why.log(pandas=production_data)

# View the profile summary
view = profile.view()
print(view.get_column("customer_age").get_metric_names())

# Save profile for later comparison
profile.writer("local").write()

# Compare two profiles
from whylogs.core.constraints import ConstraintsBuilder
builder = ConstraintsBuilder(dataset_profile=profile)
builder.add(
    builder.greater_than("prediction_score", 0.5)
)
constraints = builder.build()
result = constraints.validate()

The key advantage: profiles are tiny (kilobytes) compared to raw data (gigabytes), making them practical to store, transmit, and compare at scale.

NannyML: Performance Estimation Without Ground Truth

NannyML’s approach is unique in the open-source space. It estimates model performance metrics without needing the actual target labels:

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import nannyml as nml

# Estimate performance on unlabeled production data
estimator = nml.CBPE(  # Confidence-Based Performance Estimation
    problem_type="classification_binary",
    y_pred="prediction",
    y_pred_proba="prediction_proba",
    metrics=["roc_auc", "precision", "recall", "f1"]
)
estimator.fit(reference_data=reference_df)
results = estimator.estimate(production_df)
results.plot().show()

This is invaluable when labeled feedback arrives weeks or months after predictions are made (common in fraud detection, medical diagnosis, or credit scoring scenarios).

Model Performance Monitoring

Beyond drift detection, tracking actual model performance over time is essential.

Evidently: Performance Reports

Evidently computes standard classification and regression metrics, visualized as interactive reports:

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from evidently.metric_preset import ClassificationPreset

report = Report(metrics=[
    ClassificationPreset()
])
report.run(
    reference_data=reference_df,
    current_data=production_df,
    column_mapping={"target": "actual_label", "prediction": "model_prediction"}
)

whylogs: Constraint-Based Validation

whylogs validates that model outputs stay within expected bounds:

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from whylogs.core.constraints.factories import mean_between_range

builder = ConstraintsBuilder(profile)
builder.add(
    mean_between_range("prediction_score", 0.0, 1.0)
)
constraints = builder.build()

NannyML: Realized Performance + Estimated Performance

NannyML tracks both estimated performance (when labels are unavailable) and realized performance (when ground truth arrives):

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# When ground truth becomes available
calculator = nml.PerformanceCalculator(
    problem_type="classification_binary",
    metrics=["roc_auc", "accuracy"]
)
calculator.fit(reference_data)
realized = calculator.calculate(production_df_with_labels)
realized.plot()

Alerting and Integration

Self-hosted monitoring is only useful if it can trigger alerts when something goes wrong.

Evidently

• Integrates with Grafana through its REST API
• Test suites return pass/fail results suitable for CI/CD pipelines
• Webhook support for alerting via Slack, PagerDuty, or custom endpoints
• Prometheus metrics export for integration with existing observability stacks

whylogs

• WhyLabs platform provides alerting (cloud-hosted, optional)
• Self-hosted: compare profiles programmatically and trigger alerts via your own logic
• Integration with Great Expectations for data quality pipelines
• Compatible with Apache Airflow for scheduled monitoring jobs

NannyML

• Primarily notebook-based analysis with Matplotlib visualizations
• Results can be exported to CSV/JSON for custom alerting logic
• Integrates with MLflow for experiment tracking
• Works within Apache Spark for large-scale batch processing

Choosing the Right Tool

For most teams starting with model monitoring, Evidently provides the broadest feature set and the most active development community. Its combination of drift detection, data quality checks, model testing, and interactive reports makes it a strong default choice.

whylogs excels when data privacy is a primary concern or when you need to process massive datasets where storing raw data for comparison is impractical. Its profile-based approach is elegant for distributed systems.

NannyML fills a unique gap when you need to know if your model is degrading before ground truth labels arrive. If your use case has delayed feedback (fraud detection, medical outcomes, loan defaults), NannyML’s performance estimation is invaluable.

Many production setups combine tools — for example, using whylogs for lightweight profiling at the data ingestion layer, Evidently for comprehensive drift reports, and NannyML for performance estimation on delayed-feedback scenarios.

Related Resources

For related reading, see our ML experiment tracking guide for managing model versions alongside monitoring, the ML feature store comparison for production feature engineering, and the data annotation tools guide for building labeled datasets.

FAQ

What is model monitoring and why is it necessary?

Model monitoring is the practice of continuously tracking a deployed model’s inputs, outputs, and performance to detect degradation over time. Models trained on historical data naturally degrade as real-world conditions change — a phenomenon called “data drift.” Without monitoring, you may deploy a model that works perfectly in testing but produces increasingly inaccurate predictions in production, often going undetected until business impact is significant.

What is data drift and how is it detected?

Data drift occurs when the statistical distribution of input data in production differs from the distribution the model was trained on. Common detection methods include:

• Kolmogorov-Smirnov test — compares cumulative distribution functions for numerical features
• Chi-squared test — detects shifts in categorical feature distributions
• Population Stability Index (PSI) — quantifies the magnitude of distribution shift
• Wasserstein distance — measures the “effort” to transform one distribution into another

Can model monitoring detect all types of model degradation?

No single tool catches everything. Data drift detection identifies when input distributions change, but concept drift — when the relationship between inputs and outputs changes — is harder to detect without ground truth labels. NannyML addresses this by estimating performance without labels. For complete coverage, combine drift detection with business metric tracking (conversion rates, error rates, revenue impact).

How often should I run model monitoring checks?

The frequency depends on your model’s sensitivity and data volume:

• High-traffic models (thousands of requests/hour): Run checks every 1-6 hours
• Moderate-traffic models: Daily monitoring is usually sufficient
• Batch prediction models: Monitor after each batch run
• Critical systems (healthcare, finance): Real-time monitoring with automated alerting

Start with daily checks and increase frequency if you detect rapid drift patterns.

Is self-hosted model monitoring better than SaaS alternatives?

Self-hosted monitoring is better when:

• You handle sensitive or regulated data that cannot leave your infrastructure
• You need full control over alerting thresholds, custom metrics, and retention policies
• Your data volume makes SaaS pricing prohibitively expensive
• You operate in air-gapped or restricted network environments

SaaS alternatives may be preferable for small teams with limited DevOps capacity or when you need a fully managed solution with minimal setup.

How do I set up alerting for model monitoring?

For self-hosted setups, the typical flow is:

1. Run monitoring checks on a schedule (cron job, Airflow DAG, or continuous service)
2. Compare metrics against configured thresholds
3. If thresholds are exceeded, trigger an alert via webhook, email, or messaging platform
4. Route alerts to the appropriate team (data science, MLOps, on-call engineer)

Evidently integrates directly with Grafana and Prometheus for alerting. For whylogs and NannyML, you write custom comparison logic that triggers alerts through your existing notification infrastructure.