Enterprise AIEnterpriseEditor's pick
Databricks
Data and AI lakehouse with agentic workflows
- Overview
- Databricks unifies data engineering, analytics, and AI on a single lakehouse platform built on Delta Lake and Apache Spark. Its 2026 product suite spans Lakeflow for pipelines, Lakebase for operational Postgres, Genie for agentic AI coworkers, and Unity Catalog for governance — making it a full-stack platform for enterprises that want data infrastructure and AI in one governed environment. The platform runs across AWS, Azure, and GCP and has deep integrations with Microsoft 365, Teams, and Copilot.
- Why it matters
- Databricks is the leading independent alternative to hyperscaler-native data platforms, now trusted by roughly 70% of the Fortune 500 for data and AI workloads. Its 2026 push into agentic AI — through Genie One, Agent Bricks, and Lakebase — positions it to capture budget previously held by standalone MLOps tools, BI platforms, and workflow orchestrators. The July 2026 Microsoft partnership (Genie embedded in M365, Teams, and Copilot) significantly raises switching costs and deepens lock-in. Practitioners evaluating Databricks should model total cost carefully: the DBU bill and the cloud infrastructure bill compound quickly at scale.
- In practice
- Most teams start with Lakeflow Jobs for ETL and Databricks SQL for analytics, then layer in Genie for natural-language data access across business teams. Data science and ML teams use serverless compute and Mosaic AI model serving to fine-tune and deploy models without managing clusters. A genuine limitation is cost unpredictability: consumption-based billing with dual bills (DBUs + cloud infra) routinely surprises teams that don't set cluster auto-termination and spend caps via Unity AI Gateway from day one.
- Best for
- Data-intensive enterprises that need a single governed platform for data engineering, analytics, and production AI agents — especially those already on AWS, Azure, or GCP with large Spark or SQL workloads.
- Pricing
- Consumption-based, no public seat tiers. Billed in DBUs (Databricks Units) at rates that vary by workload: Data Engineering (Lakeflow Jobs) from ~$0.15/DBU, SQL Warehousing from ~$0.22/DBU, Interactive/Data Science from ~$0.40/DBU, AI model serving and agents from ~$0.07/DBU, Lakebase (operational Postgres) from ~$0.069/CU. Classic compute carries a second cloud infrastructure bill on top; serverless compute bundles it. Committed-use contracts reduce effective rates. Median contract ~$250K/year; enterprise deployments commonly exceed $500K. Free trial available.
Key features
- Lakeflow: unified pipeline suite (batch, streaming, no-code prep) built on Spark Declarative Pipelines
- Genie One: agentic AI coworker for business and technical teams, connected to 50+ enterprise apps
- Unity AI Gateway: runtime governance for AI spend, model routing, MCP server registry, and guardrails
- Lakebase: serverless operational Postgres for AI agents, built on Neon and Electric
- Unity Catalog: data and AI governance across tables, models, agents, and ML assets
Quick answers
- What is Databricks?
- Databricks unifies data engineering, analytics, and AI on a single lakehouse platform built on Delta Lake and Apache Spark. Its 2026 product suite spans Lakeflow for pipelines, Lakebase for operational Postgres, Genie for agentic AI coworkers, and Unity Catalog for governance — making it a full-stack platform for enterprises that want data infrastructure and AI in one governed environment. The platform runs across AWS, Azure, and GCP and has deep integrations with Microsoft 365, Teams, and Copilot.
- Why does Databricks matter?
- Databricks is the leading independent alternative to hyperscaler-native data platforms, now trusted by roughly 70% of the Fortune 500 for data and AI workloads. Its 2026 push into agentic AI — through Genie One, Agent Bricks, and Lakebase — positions it to capture budget previously held by standalone MLOps tools, BI platforms, and workflow orchestrators. The July 2026 Microsoft partnership (Genie embedded in M365, Teams, and Copilot) significantly raises switching costs and deepens lock-in. Practitioners evaluating Databricks should model total cost carefully: the DBU bill and the cloud infrastructure bill compound quickly at scale.
- What is Databricks best for?
- Data-intensive enterprises that need a single governed platform for data engineering, analytics, and production AI agents — especially those already on AWS, Azure, or GCP with large Spark or SQL workloads.
- How much does Databricks cost?
- Consumption-based, no public seat tiers. Billed in DBUs (Databricks Units) at rates that vary by workload: Data Engineering (Lakeflow Jobs) from ~$0.15/DBU, SQL Warehousing from ~$0.22/DBU, Interactive/Data Science from ~$0.40/DBU, AI model serving and agents from ~$0.07/DBU, Lakebase (operational Postgres) from ~$0.069/CU. Classic compute carries a second cloud infrastructure bill on top; serverless compute bundles it. Committed-use contracts reduce effective rates. Median contract ~$250K/year; enterprise deployments commonly exceed $500K. Free trial available.
Seen in recent stories
Where Databricks showed up in the last 90 days of KeyNews editions.
Know the terms
Glossary entries that explain what Databricks is built on.
RAG (Retrieval-Augmented Generation)
A technique that retrieves relevant documents from an external knowledge base and feeds them to a model alongside the user's query. RAG reduces hallucination and keeps responses grounded in current, factual data.
Agentic orchestration
The architecture pattern of coordinating multiple AI agents to accomplish complex tasks, with a supervisor agent routing work, managing state, and combining results from specialized sub-agents.
AI governance
The organizational frameworks, policies, and processes that govern how AI systems are developed, deployed, monitored, and retired within an enterprise. AI governance covers model risk management, bias auditing, access controls, and regulatory compliance.
Model Routing
A system that dynamically selects which AI model should handle each request based on the query's complexity, cost constraints, latency requirements, or domain. Model routing lets applications use expensive frontier models only when needed and cheap efficient models for everything else.
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