Releases

Databricks released a command-line workflow for developing agents integrated with Unity Gateway, Agent Bricks Runtime and MLflow tracing.

Databricks acquired Row Zero to add a live, governed spreadsheet experience for humans and agents inside Genie.

Databricks introduced Genie One MCP to provide trusted governed business context to external AI agents and cowork tools.

Databricks reportedly closed a $5 billion strategic financing round at a $190 billion valuation.

Databricks signed a Coatue-led strategic-funding term sheet valuing the company at $188 billion, with proceeds targeted at Unity AI Gateway, Genie and Lakebase.

Databricks announced Unity AI Gateway as a unified governance and control plane across hosted and external AI assets.

Databricks announced native Grok model availability in Agent Bricks through a partnership with SpaceXAI.

Databricks expanded Agent Bricks with model choice, memory, document intelligence, sandboxes, registry support and multi-agent development capabilities.

Databricks announced an agreement to acquire AI SOC company Panther to expand its agentic security-lakehouse offering.

Databricks announced Lakewatch, an agentic SIEM for detection, investigation and response on a security lakehouse.

Databricks introduced Genie Code, an autonomous AI partner for data work, pipeline maintenance and model operations.

Databricks redesigned AI Search with decoupled storage and compute to support billions of vectors at lower cost.

Databricks disclosed a $1.4 billion annual revenue run-rate for its AI products.

Databricks completed about $5 billion of equity financing at a $134 billion valuation plus roughly $2 billion of additional debt capacity.

Agent Bricks Knowledge Assistant became generally available, powered by instructed retrieval and MLflow evaluation.

Databricks announced it was raising more than $4 billion in Series L equity financing at a $134 billion valuation.

Databricks became an early Microsoft Agent 365 partner, bringing governed Agent Bricks experiences to Teams, Outlook and Excel.

Databricks introduced ai_parse_document for multimodal PDF and document understanding integrated with Agent Bricks, AI Search and AI/BI.

Databricks announced the close of a $1 billion Series K at a valuation above $100 billion.

Databricks disclosed that its AI products had exceeded a $1 billion annual revenue run-rate.

Databricks introduced managed Model Context Protocol servers for Unity Catalog functions, Genie spaces and AI Search indexes.

Databricks introduced a simplified business-user front door to Genie spaces, dashboards and apps.

AI/BI Genie became generally available across AWS, Azure and Google Cloud, alongside a new knowledge store and planned Deep Research mode.

Databricks and Microsoft expanded integration across Azure Databricks, Microsoft 365 and governed enterprise agents.

Databricks released MLflow 3 with unified tracking, evaluation, observability and governance for models and agents.

Databricks introduced Agent Bricks, which generates task-specific data and benchmarks to optimize agents for enterprise use cases.

Databricks introduced Lakebase, a serverless Postgres category designed for operational data and AI agents.

Databricks agreed to acquire serverless Postgres company Neon to create Lakebase for AI applications and agents.

Databricks and Google Cloud expanded joint go-to-market and product integrations for Gemini models and enterprise AI.

Databricks and Anthropic established a partnership to offer Claude models natively through the Databricks Data Intelligence Platform.

Databricks enhanced agent evaluation with customizable LLM judges, human review workflows and OpenTelemetry-based MLflow tracing.

Databricks released APIs for embedding Genie conversational analytics into applications, Teams, Slack and SharePoint.

Mosaic AI Gateway added support for OpenAI-compatible external and custom model providers with centralized permissions, routing and monitoring.

Databricks completed $10 billion in equity financing and arranged $5.25 billion in debt facilities at a $62 billion equity valuation.

Databricks announced a Series J targeting $10 billion, with $8.6 billion completed at announcement, valuing the company at $62 billion.

Databricks introduced code-first Custom Agents for building, deploying and evaluating production agent systems using any LLM.

Mosaic AI Agent Framework and Agent Evaluation entered public preview with integrated deployment, evaluation, tracing and governance.

Databricks introduced Apps for building and deploying secure data and AI applications directly on the platform.

Databricks introduced AI/BI, pairing AI-assisted dashboards with Genie conversational analytics built on a compound AI system.

Databricks introduced MLflow Tracing for end-to-end observability and debugging of generative-AI applications and agents.

Databricks launched a framework for authoring, evaluating, deploying and monitoring production-quality compound AI systems.

Databricks agreed to acquire Tabular, founded by the creators of Apache Iceberg, to advance interoperability between Delta Lake and Iceberg.

Databricks optimized DBRX inference and made the model available through Mosaic AI Model Serving.

Databricks released DBRX, a 132-billion-total-parameter mixture-of-experts model with 36 billion active parameters under an open license.

Databricks acquired Einblick, whose natural-language notebook technology and team strengthened AI-native data exploration.

Databricks introduced a data intelligence engine that learns enterprise semantics and powers context-aware assistance and optimization.

Databricks acquired enterprise data replication company Arcion to make operational data available for analytics and AI.

Databricks introduced pay-per-token APIs and provisioned throughput for serving curated open foundation models.

Databricks raised more than $500 million in Series I financing at a $43 billion valuation.

Databricks completed its acquisition of MosaicML, adding foundation-model training, inference, research and open models to its AI platform.

MLflow AI Gateway provided a unified interface for routing requests to multiple commercial and open model providers.

Databricks introduced automated and human evaluation tooling for assessing generative-AI quality on enterprise data.

Databricks announced managed vector search integrated with the lakehouse for retrieval-augmented generation and semantic search.

Databricks introduced an AI assistant for generating, explaining and repairing SQL and Python with organizational context.

Databricks launched Lakehouse AI, later branded Mosaic AI, as an integrated platform for building generative-AI applications on enterprise data.

MosaicML released the MPT-30B base, instruct and chat models with commercial-use terms.

MosaicML released MPT-7B, Base, Instruct, Chat and StoryWriter variants under commercially usable licenses.

Databricks agreed to acquire data-governance company Okera to strengthen governance and AI security capabilities.

Databricks released Dolly 2.0 with commercial-use weights and the human-generated databricks-dolly-15k instruction dataset.

Databricks released Dolly, a six-billion-parameter instruction-following model that could be trained inexpensively on one machine.

MosaicML released MosaicBERT, an optimized BERT architecture designed to pretrain high-quality models at much lower cost.

MLflow 2.0 expanded evaluation, pipelines and deployment workflows for production machine learning.

Databricks closed a $1.6 billion Series H with participation from AWS, CapitalG and Microsoft at a $38 billion post-money valuation.

Databricks introduced a managed Feature Store integrated with Delta Lake, MLflow and model serving.

Databricks introduced Unity Catalog as a unified governance layer for data and AI assets across clouds.

Databricks introduced AutoML to automatically train models while generating editable notebooks.

Databricks and Google Cloud launched Databricks on Google Cloud, completing availability across the three major clouds.

Databricks raised $1 billion led by Franklin Templeton at a $28 billion post-money valuation.

Databricks acquired open-source visualization company Redash to expand SQL analytics and collaboration.

Databricks articulated the lakehouse architecture, combining data-lake openness with data-warehouse management and performance.

Databricks raised $400 million in Series F financing at a $6.2 billion valuation.

Databricks introduced the MLflow Model Registry for centralized model versioning and lifecycle management.

MLflow 1.0 marked the first stable major release of the open-source ML lifecycle platform.

Databricks raised $250 million led by Andreessen Horowitz, valuing the company at $2.75 billion.

Databricks introduced MLflow as an open-source platform for managing the machine-learning lifecycle.

Microsoft and Databricks announced Azure Databricks, a first-party Apache Spark analytics service on Azure.

Databricks raised $140 million in a Series D led by Andreessen Horowitz.

Databricks raised a $60 million Series C led by New Enterprise Associates.

Co-founder Ali Ghodsi became chief executive officer of Databricks.

Databricks raised $33 million to expand its cloud platform for large-scale data processing.

Andreessen Horowitz led Databricks' first institutional financing to commercialize Apache Spark.

Databricks was founded by seven University of California, Berkeley researchers who helped create Apache Spark.