Databricks for Tech and AI
Build better products faster with AI, apps and agents built on your data
Your data, your AI, your future
Discover how to ship AI-native products, unlock faster insights and automate complex workflows with AI, apps and agents built on your data.
Accelerate ML and AI production
Agent development
Design, test and deploy agentic workflows using foundation models and your own data and models, with built-in governance and observability.
RAG and retrieval pipelines
Combine structured tables and unstructured documents into retrieval systems that power accurate, auditable AI answers at scale.
LLM fine-tuning and evaluation
Fine-tune models on your proprietary data to improve accuracy, reduce inference costs and keep control over IP and outputs.
Low-latency real-time apps
Customer data enrichment
Turn raw data into actionable, 360-degree customer views to drive smarter decisions and improve engagement.
Real-time fraud detection
Detect fraud in real time by computing 250+ ML features with sub-100ms latency — no specialized streaming infrastructure required.
See how Coinbase does fraud detection
Real-time personalization
Use comprehensive data to power dynamic, personalized interactions that boost engagement, conversions and customer loyalty.
Model choice
Resource optimization
Analyze and allocate resources effectively, reducing costs and improving productivity across teams and systems in real time.
Model serving at scale
Deploy models at any scale with autoscaling endpoints, A/B testing and drift monitoring — no separate MLOps stack required.
Demand forecasting
Use data-driven forecasts to anticipate demand, reduce waste and ensure timely delivery of products and services.
Stand out with AI built on your data
Leading tech companies build AI, apps and agents on Databricks, getting unified data and governed infrastructure without the integration tax.Featured products
Tech and AI partner ecosystem
Databricks partners deliver tech industry-focused solutions and data tools that help you innovate faster, cut costs and drive greater value from your data.
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Guide
FAQ
A product analytics platform is a specialized tool used primarily for understanding user behavior within a software application, focusing on things like feature adoption, user flows and conversion events to optimize the product experience.
A SaaS data analytics strategy built on a unified data platform, however, is much broader. It centralizes product data alongside all other business data — such as sales, marketing, finance and operational data — in a single, governed environment.
This unified approach allows tech companies to build AI, apps, and agents on their data. Specifically, this enables comprehensive use cases, including building accurate customer 360 views, applying AI and machine learning to power real-time personalization and optimizing retention across the entire customer lifecycle, rather than just focusing on in-app behavior. Using a single foundation like Lakebase and Lakehouse for operational and analytical data eliminates the complexity and latency of managing separate systems.
Software companies use embedded analytics to deliver insights to their customers by integrating dashboards, reports and AI recommendations directly into their application interface. This process effectively puts intelligence inside your product, allowing customers to see crucial data insights in context, rather than having to navigate to a separate analytics portal.
Embedding dashboards and AI recommendations built on your data helps drive engagement and increase product value without requiring a separate analytics stack.
Tech companies use the power of AI, apps and agents built on their data to proactively address customer churn and enhance retention through several data-driven use cases:
- Retention optimization: Use data-driven insights to predict and prevent churn before it occurs, accelerating decision-making and driving long-term customer engagement.
- Customer 360 and behavioral segmentation: Analyze customer behavior to create comprehensive, 360-degree customer views that help deliver personalized campaigns. This strategy helps increase engagement and conversions across every channel.
- Lifetime value modeling: Model customer lifetime value to guide business strategy, optimize spending and boost engagement across high-value segments.
- Next-best-action models: Bring AI and ML models to their data to recommend the optimal action for each customer, boosting conversions and lifetime value.
- Real-time personalization: Use comprehensive data to power dynamic, personalized interactions at scale, which boosts engagement, conversions and customer loyalty.