Databricks Mosaic AI vs Determined

Databricks Mosaic AI8.5Visit DDetermined8.2Visit

Higher score Databricks Mosaic AI #4 in Experiment Tracking 8.5/10 14-day trial Free trial✓ 0 of 3 features Visit Databricks

vs

D Determined #7 in Experiment Tracking 8.2/10 Free plan Free plan✓ 0 of 3 features Visit Determined

• Databricks Mosaic AI fits best

  Databricks users tracking experiments in a managed stack

• Determined fits best

  Teams training distributed models on their own

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Side by side

Feature	Databricks Mosaic AI 8.5/10 Visit ↗	DDetermined 8.2/10 Visit ↗
At a glance
Editor score	8.5	8.2
Ranking	#4 in Experiment Tracking	#7 in Experiment Tracking
Best for	Databricks users tracking experiments in a managed stack	Teams training distributed models on their own
Pricing model	Paid	Free
Starting price	—	Free plan
Free plan	Not checked yet	✓ (best)
Free trial	14 days	—
Deployment	Cloud	Cloud, Self-hosted
Platforms	Web	Web, Linux
Support	Docs	Community
Built for	Mid-market, Enterprise	Small business, Mid-market, Enterprise
Features Databricks Mosaic AI 0/3 · Determined 0/3
Self-hosted option	Not checked yet	Not checked yet
Artifact storage	Not checked yet	Not checked yet
Run comparison	Not checked yet	Not checked yet
Specs
Free plan seats	Not checked yet	Not checked yet
Version control	Not checked yet	Not checked yet
Deployment options	Not checked yet	Not checked yet
Our review
Pros	Tracks run parameters, metrics, artifacts, and code versions with MLflow. Connects experiment tracking to a Unity Catalog model registry and serving. Includes orchestration, managed compute, inference, and model monitoring.	Tracks code versions, metrics, checkpoints, and hyperparameters Combines distributed training with GPU scheduling and resource management Supports deployments to AWS, GCP, Kubernetes, and Slurm/PBS
Cons	Cloud deployment only. Support channels listed are documentation. Usage-based charges vary by cloud provider, region, and selected services.	Experiment tracking is narrower than leading suites Workflows are centered on Python Support is community-based
Our verdict	Databricks Mosaic AI brings experiment tracking together with tools for developing, deploying, monitoring, and governing machine-learning and generative-AI applications. Its MLflow tracking covers parameters, metrics, artifacts, runs, and… Read the review →	Determined brings distributed training, hyperparameter tuning, experiment tracking, and resource management into an open-source deep learning platform. It is aimed at researchers and machine learning teams that train models on their own… Read the review →

Strengths and trade-offs

• Databricks Mosaic AI — where it wins

  • Tracks run parameters, metrics, artifacts, and code versions with MLflow.
  • Connects experiment tracking to a Unity Catalog model registry and serving.
  • Includes orchestration, managed compute, inference, and model monitoring.

  Where it doesn't

  • Cloud deployment only.
  • Support channels listed are documentation.
  • Usage-based charges vary by cloud provider, region, and selected services.

• Determined — where it wins

  • Tracks code versions, metrics, checkpoints, and hyperparameters
  • Combines distributed training with GPU scheduling and resource management
  • Supports deployments to AWS, GCP, Kubernetes, and Slurm/PBS

  Where it doesn't

  • Experiment tracking is narrower than leading suites
  • Workflows are centered on Python
  • Support is community-based

• Databricks Mosaic AI8.5/10 · 14-day trial

  MLflow experiment tracking sits alongside registry, orchestration, serving, and managed compute.

  Visit DatabricksFull verdict →
• DDetermined8.2/10 · Free plan

  A focused option for teams training distributed models on their own infrastructure.

  Visit DeterminedFull verdict →