Best Self-Hosted ML Experiment Tracking Tools in 2026: MLflow vs ClearML vs Aim

Machine learning teams generate dozens — sometimes hundreds — of training runs before settling on a production model. Without proper tracking, it becomes nearly impossible to reproduce results, compare hyperparameter configurations, or understand why one model outperformed another. Commercial experiment tracking services lock your data behind paywalls and usage limits. Self-hosted open-source alternatives give you full ownership of your experiment data, unlimited tracking, and deep integration with your existing infrastructure.

Why Self-Host Your ML Experiment Tracking

Every training run produces valuable metadata: hyperparameters, metrics curves, system resource usage, model artifacts, code snapshots, and environment configurations. When you send all of this to a third-party SaaS platform, you face several problems:

• Data ownership: Your experiment history is a strategic asset. It captures what works and what doesn’t for your specific use case. Losing access to this data means losing institutional knowledge.
• Cost at scale: SaaS pricing is typically per-user or per-experiment. Teams running hyperparameter sweeps with hundreds of parallel trials quickly hit expensive tiers.
• Network dependencies: Training clusters in isolated VPCs or air-gapped environments cannot reach external tracking servers. Local tracking eliminates this bottleneck.
• Custom infrastructure: Self-hosted tools integrate directly with your artifact stores (S3-compatible, NFS, minio), authentication systems (LDAP, OAuth), and CI/CD pipelines.
• Privacy compliance: When training on sensitive datasets — healthcare, finance, or proprietary data — keeping experiment metadata on-premises satisfies regulatory requirements.

The three leading open-source experiment tracking platforms in 2026 are MLflow, ClearML, and Aim. Each takes a different approach to the problem. This guide compares them in detail and shows you how to deploy each one with docker.

What Is ML Experiment Tracking?

Before diving into tools, it helps to understand what experiment tracking actually captures. A complete tracking system records:

1. Parameters: Learning rate, batch size, model architecture, dataset version, optimizer settings.
2. Metrics: Loss, accuracy, F1 score, AUC — logged per step or epoch, enabling curve visualization.
3. Artifacts: Serialized model weights, evaluation outputs, confusion matrices, sample predictions.
4. System metadata: CPU/GPU utilization, memory consumption, training duration, host information.
5. Code and environment: Git commit hash, Python package versions, Docker image digest, configuration files.
6. Tags and metadata: Labels for grouping runs, notes about experimental changes, links to related tickets.

The best tools provide a web interface for visual comparison, a programmatic API for logging, and storage backends that scale to thousands of experiments.

Feature Comparison: MLflow vs ClearML vs Aim

MLflow: The Complete ML Lifecycle Platform

MLflow, originally developed by Databricks, is the most widely adopted open-source ML platform. It goes beyond experiment tracking to cover the entire machine learning lifecycle: tracking, projects (reproducible runs), models (packaging), and registry (deployment management).

Architecture

MLflow Tracking consists of three components:

• Tracking API: Client-side library that logs parameters, metrics, and artifacts from your training code.
• Tracking Server: Centralized server that receives logs and stores them in a backend store (database) and artifact store (file system or object storage).
• UI: Web interface for browsing, searching, and comparing experiments.

Self-Hosted Setup with Docker

The simplest way to run MLflow locally is with the official Docker image. Here is a production-ready docker-compose.yml:

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version: "3.8"

services:
  mlflow-tracking:
    image: mlflow/mlflow:latest
    ports:
      - "5000:5000"
    command: >
      mlflow server
      --backend-store-uri postgresql://mlflow:mlflow_password@mlflow-db:5432/mlflow
      --default-artifact-root /mlflow/artifacts
      --host 0.0.0.0
    volumes:
      - mlflow-artifacts:/mlflow/artifacts
    environment:
      - MLFLOW_S3_ENDPOINT_URL=http://minio:9000
    depends_on:
      mlflow-db:
        condition: service_healthy

  mlflow-db:
    image: postgres:16-alpine
    environment:
      POSTGRES_USER: mlflow
      POSTGRES_PASSWORD: mlflow_password
      POSTGRES_DB: mlflow
    volumes:
      - mlflow-data:/var/lib/postgresql/data
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U mlflow"]
      interval: 5s
      timeout: 3s
      retries: 5

  minio:
    image: minio/minio:latest
    ports:
      - "9000:9000"
      - "9001:9001"
    environment:
      MINIO_ROOT_USER: minio_admin
      MINIO_ROOT_PASSWORD: minio_secret_key
    command: server /data --console-address ":9001"
    volumes:
      - minio-data:/data

volumes:
  mlflow-artifacts:
  mlflow-data:
  minio-data:

Start the stack:

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docker compose up -d

The MLflow UI will be available at http://localhost:5000, and the MinIO console at http://localhost:9001.

Logging from Training Code

Once the server is running, integrate tracking into your training script:

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import mlflow
import torch
import torch.nn as nn

# Configure the tracking server URI
mlflow.set_tracking_uri("http://localhost:5000")
mlflow.set_experiment("resnet-image-classifier")

with mlflow.start_run():
    # Log hyperparameters
    mlflow.log_params({
        "learning_rate": 0.001,
        "batch_size": 64,
        "epochs": 50,
        "optimizer": "adamw",
        "model": "resnet50",
    })

    # Training loop
    for epoch in range(50):
        loss = train_one_epoch(epoch)
        val_acc = evaluate(epoch)

        # Log metrics per epoch
        mlflow.log_metric("train_loss", loss, step=epoch)
        mlflow.log_metric("val_accuracy", val_acc, step=epoch)

    # Log the trained model as an artifact
    torch.save(model.state_dict(), "model.pth")
    mlflow.log_artifact("model.pth")
    mlflow.pytorch.log_model(model, "final_model")

Production Considerations

For production deployments, consider these additional configurations:

• Authentication: MLflow’s built-in server has no authentication. Place it behind a reverse proxy (Nginx, Traefik) with OAuth or basic auth.
• Database: Use PostgreSQL or MySQL for the backend store — SQLite does not support concurrent writers.
• Artifact store: Use S3-compatible storage (MinIO, Ceph, AWS S3) for scalable artifact storage.
• High availability: Run multiple Tracking Server instances behind a load balancer, all pointing to the same database and artifact store.

ClearML: The Full MLOps Platform

ClearML takes a broader approach than MLflow. It is not just an experiment tracker — it is a complete MLOps platform that includes experiment tracking, remote execution, data management, model management, and hyperparameter optimization, all in a single self-hosted deployment.

Architecture

ClearML’s architecture consists of several services:

• API Server: REST API that manages experiments, models, projects, and users.
• Web Server: Dashboard UI for monitoring and management.
• File Server: Handles artifact storage and retrieval.
• Agent System: Workers that pull tasks from the queue and execute them on any machine.
• SDK: Python client library for logging and orchestration.

The key differentiator is the agent system. You deploy ClearML agents on any machine (GPU servers, cloud instances, local workstations), and they automatically pull queued tasks, clone the code from Git, set up the environment, execute the training, and report results back to the server.

Self-Hosted Setup with Docker

ClearML provides an official Docker Compose setup that deploys the entire stack:

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# Clone the official setup repository
git clone https://github.com/allegroai/clearml-server.git
cd clearml-server/docker

# Launch the full stack
docker compose up -d

For a custom setup with specific port mappings and persistent storage:

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version: "3.8"

services:
  clearml-api:
    image: allegroai/clearml:latest
    ports:
      - "8008:8008"
    environment:
      - CLEARML__apiserver__mongo__host=clearml-mongo
      - CLEARML__apiserver__redis__host=clearml-redis
      - CLEARML__services__fileserver__port=8081
    depends_on:
      - clearml-mongo
      - clearml-redis

  clearml-web:
    image: allegroai/clearml:latest
    ports:
      - "8080:80"
    environment:
      - CLEARML__frontend__api__host=clearml-api
      - CLEARML__frontend__api__port=8008
    depends_on:
      - clearml-api

  clearml-fileserver:
    image: allegroai/clearml:latest
    ports:
      - "8081:8081"
    volumes:
      - clearml-files:/opt/clearml/files
    environment:
      - CLEARML__services__fileserver__port=8081

  clearml-mongo:
    image: mongo:7
    volumes:
      - clearml-mongo:/data/db

  clearml-redis:
    image: redis:7-alpine
    volumes:
      - clearml-redis:/data

volumes:
  clearml-files:
  clearml-mongo:
  clearml-redis:

After the services start, access the web UI at http://localhost:8080. The first user to register becomes the administrator.

Agent Setup for Distributed Training

The real power of ClearML is its agent system. Install the ClearML agent on any machine that has GPUs:

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pip install clearml-agent

# Configure the agent to connect to your server
clearml-agent init --docker --gpu

# Start the agent — it pulls tasks from the default queue
clearml-agent daemon --queue default --gpus all

Your training code requires minimal changes. The ClearML SDK auto-captures most framework logging:

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from clearml import Task
import torch

# Create a ClearML task — this automatically captures
# git repo, uncommitted changes, and installed packages
task = Task.init(
    project_name="image-classification",
    task_name="resnet50-baseline",
    task_type=Task.TaskTypes.training,
)

# Connect hyperparameters for tracking and remote override
args = {
    "learning_rate": 0.001,
    "batch_size": 64,
    "epochs": 50,
}
task.connect(args)

# Training loop — ClearML auto-logs framework metrics
for epoch in range(args["epochs"]):
    loss = train_one_epoch(epoch)
    val_acc = evaluate(epoch)

    # Manual metric logging (framework auto-logging also works)
    task.get_logger().report_scalar(
        title="Training Loss",
        series="loss",
        value=loss,
        iteration=epoch,
    )
    task.get_logger().report_scalar(
        title="Validation Accuracy",
        series="accuracy",
        value=val_acc,
        iteration=epoch,
    )

# Upload model artifact
task.upload_artifact("model", artifact_object="model.pth")

To remotely enqueue this experiment for execution on a GPU server:

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# Enqueue the task to the "gpu-cluster" queue
task.enqueue(queue="gpu-cluster")

Built-in Hyperparameter Optimization

ClearML includes a built-in optimizer that supports grid search, random search, and Bayesian optimization:

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from clearml.automation import HyperParameterOptimizer
from clearml.automation.optuna import OptimizerOptuna

optimizer = OptimizerOptuna(
    base_task_id="your-task-id",
    objective_metric_title="Validation",
    objective_metric_series="accuracy",
    objective_metric_sign="max",
    max_number_of_concurrent_tasks=4,
    total_max_jobs=20,
    optimizer_kwargs={"n_trials": 20},
)

optimizer.set_parameters({"General/learning_rate": (0.0001, 0.1, "loguniform")})
optimizer.set_parameters({"General/batch_size": [16, 32, 64, 128]})

optimizer.start_locally()
optimizer.wait()
optimizer.stop()

Aim: Lightweight, High-Performance Experiment Visualization

Aim takes a focused approach: it does one thing and does it exceptionally well. Aim is designed to be the fastest experiment tracker for teams that run thousands of experiments and need to compare them visually. It uses a columnar storage format optimized for reading and comparing large numbers of runs.

Architecture

Aim’s architecture is deliberately simple:

• Storage: A custom columnar file format (based on RocksDB) that stores metrics, parameters, and metadata. This format is optimized for fast reads and comparisons across thousands of runs.
• Server: A lightweight Go-based HTTP server that serves the UI and handles tracking API requests.
• UI: A React-based dashboard focused on visual comparison — scatter plots, parallel coordinates, metric overlays, and run grouping.
• SDK: Python and TensorFlow clients for logging from training code.

Aim does not include a model registry, remote execution, or hyperparameter optimization. It assumes you already have those tools and need a fast, reliable way to visualize and compare experiment results.

Self-Hosted Setup with Docker

Aim’s Docker image is straightforward — it runs as a single container with a mounted volume for data:

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version: "3.8"

services:
  aim-server:
    image: aimstack/aim:latest
    ports:
      - "43800:43800"
    volumes:
      - aim-data:/repo
    command: server --host 0.0.0.0 --port 43800 --repo /repo

volumes:
  aim-data:

Start with:

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docker compose up -d

The UI is available at http://localhost:43800.

Command-Line Interface

Aim includes a powerful CLI for managing experiments directly from the terminal:

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# Initialize an Aim repository in your project
aim init

# Start the UI server
aim up --port 43800

# List all experiments
aim experiments ls

# Compare specific runs
aim runs show <run_hash_1> <run_hash_2>

# Search runs using Aim's query language
aim runs search "metrics.loss.min < 0.1 and params.learning_rate < 0.01"

Logging from Training Code

Aim’s SDK integrates with popular ML frameworks through callback mechanisms:

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from aim import Run
import torch

# Create a new run — Aim auto-captures Git info and system metrics
run = Run(
    experiment="image-classification",
    description="ResNet50 with AdamW optimizer",
)

# Log hyperparameters
run["hparams"] = {
    "learning_rate": 0.001,
    "batch_size": 64,
    "epochs": 50,
    "model": "resnet50",
}

# Log metrics during training
for epoch in range(50):
    loss = train_one_epoch(epoch)
    val_acc = evaluate(epoch)

    run.track(loss, name="loss", step=epoch, context={"subset": "train"})
    run.track(val_acc, name="accuracy", step=epoch, context={"subset": "val"})

# Log system metrics (CPU, GPU, memory)
run.track_system_metrics()

For PyTorch Lightning users, Aim provides a built-in callback:

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from aim.pytorch_lightning import AimLogger
import pytorch_lightning as pl

logger = AimLogger(
    experiment="image-classification",
    log_system_params=True,
)

trainer = pl.Trainer(
    max_epochs=50,
    logger=logger,
    callbacks=[AimLogger()],
)

The Aim Query Language

Aim’s standout feature is its SQL-like query language for filtering and comparing experiments:

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# Find runs where final accuracy exceeds 95%
metrics.accuracy.max > 0.95

# Find fast training runs with low loss
metrics.loss.min < 0.1 and metrics.train_time < 3600

# Group by optimizer and compare
params.optimizer == "adamw" and metrics.accuracy.last > 0.9

# Com[plex](https://www.plex.tv/) multi-metric queries
metrics.loss.last < metrics.loss.first * 0.1 and
params.learning_rate > 0.0005 and
params.learning_rate < 0.01

These queries power the UI’s scatter plots, parallel coordinates views, and run grouping features, making it easy to spot patterns across hundreds of experiments.

Choosing the Right Tool

The decision depends on your team’s needs and infrastructure:

Choose MLflow if:

• You want a complete ML platform covering tracking, projects, models, and registry.
• Your team already uses Databricks or plans to migrate.
• You need model registry with stage transitions for production deployment.
• You want broad framework support including Spark MLlib and scikit-learn.

Choose ClearML if:

• You want experiment tracking combined with remote execution and orchestration.
• Your team runs training across multiple GPU clusters and needs centralized job management.
• You want built-in hyperparameter optimization without adding separate tools.
• You need multi-user RBAC, workspaces, and team collaboration out of the box.

Choose Aim if:

• You already have orchestration and execution tools and only need tracking + visualization.
• You run thousands of experiments and need fast UI performance.
• You value a powerful query language for filtering and comparing runs.
• You prefer a lightweight, single-container deployment with minimal dependencies.

Deployment Best Practices

Regardless of which tool you choose, follow these practices for production self-hosted deployments:

1. Use a reverse proxy: Place Nginx, Caddy, or Traefik in front of the tracking server for TLS termination, authentication, and rate limiting.
2. Separate storage: Use dedicated volumes or object storage for artifacts. Do not store artifacts on the same disk as the application.
3. Database backups: Schedule regular backups of the tracking database. Experiment history is irreplaceable.
4. Network isolation: If training runs on a separate network segment, ensure the tracking server is reachable from all training nodes.
5. Resource monitoring: Monitor the tracking server’s disk usage. Metrics and artifacts accumulate quickly with large teams.
6. Retention policies: Implement automated cleanup of failed or stale experiments to prevent unbounded storage growth.
7. Version pinning: Pin your Docker image versions to avoid breaking changes during upgrades. Test upgrades on a staging instance first.

Self-hosted experiment tracking puts your ML team in control. The data stays on your infrastructure, the cost scales with your hardware rather than your experiment count, and the tools integrate seamlessly into your existing pipelines. Pick the platform that matches your workflow, deploy it with Docker, and start tracking every run from day one.

Frequently Asked Questions (FAQ)

Which one should I choose in 2026?

The best choice depends on your specific requirements:

• For beginners: Start with the simplest option that covers your core use case
• For production: Choose the solution with the most active community and documentation
• For teams: Look for collaboration features and user management
• For privacy: Prefer fully open-source, self-hosted options with no telemetry

Refer to the comparison table above for detailed feature breakdowns.

Can I migrate between these tools?

Most tools support data import/export. Always:

1. Backup your current data
2. Test the migration on a staging environment
3. Check official migration guides in the documentation

Are there free versions available?

All tools in this guide offer free, open-source editions. Some also provide paid plans with additional features, priority support, or managed hosting.

How do I get started?

1. Review the comparison table to identify your requirements
2. Visit the official documentation (links provided above)
3. Start with a Docker Compose setup for easy testing
4. Join the community forums for troubleshooting