Databricks vs Labelbox

AI-native business-intelligence product comprising AI/BI Dashboards (AI-assisted dashboard/visualization creation) and Genie Spaces (conversational natural-language exploration of data), built into the Databricks Data + AI Platform with Unity Catalog governance and no per-seat licensing.

AI coworker (the evolution of the earlier Databricks Assistant/Genie) that lets business users ask questions, take action and drive outcomes over enterprise data via natural language, integrating with Slack, Teams, Jira, Google Drive and Salesforce, with mobile apps.

A control plane for building, evaluating, governing and monitoring AI agents across proprietary and open-source models, with native MCP support.

A managed service for fine-tuning open-source LLMs or training custom models on enterprise data using dedicated GPU infrastructure.

Databricks' tooling for building, fine-tuning, serving and evaluating models and agents on lakehouse data.

Lakehouse platform for storing, governing, querying and sharing enterprise data.

A managed hybrid semantic, keyword and vector search service (formerly Mosaic AI Vector Search) with automatic data sync and Unity Catalog governance.

Annotate is the data labeling product within Labelbox, providing 10+ built-in editors for multimodal chat, LLM evaluation, prompt/response generation, computer vision and NLP, plus customizable labeling and review workflows and team performance monitoring.

Catalog is Labelbox's data curation and search product providing out-of-the-box search across images, text, video, conversations and documents over metadata, vector embeddings and annotations without building your own vector database infrastructure.

Labelbox's reinforcement-learning platform for developing, evaluating and deploying enterprise specialist agents, connecting RL environments, evaluation systems and a training loop that fine-tunes models from graded rollout trajectories.

Foundry runs third-party foundation models over data already in Labelbox to pre-label and enrich image, text and document datasets without code, routing the predictions to human review; billed as inference cost per model run plus Labelbox Units.

Horizon supplies RL training gyms and evaluations for reasoning, tool use and computer use, using WorldSim to simulate enterprise environments such as GitLab, Jira, CRM, email and chat and to produce calibrated reward and preference signals for post-training.

Alignerr is Labelbox's expert-network product that routes AI training and evaluation tasks to credentialed contributors across 200+ knowledge domains and 40+ countries and returns structured outputs for RL training, RLHF and evaluation workflows.

Terra is Labelbox's robotics data product delivering video, trajectories and multimodal annotations across pre-training, post-training and evaluation stages, including expert teleoperation with action labels and multiple camera perspectives for embodied foundation models.