Streamhouse: the data architecture that serves AI at the speed of business
A Streamhouse continuously captures, processes, governs and serves the current state of business to agents, apps, and analytics so they all run on the same fresh, governed source of truth.
Always Current: Keep your business context continuously up-to-date
Continuously capture, validate, and enrich data the moment it changes to ensure agents operate on the current state of your business.
Mission Critical: Put live data on the critical path
Ensure zero downtime for live applications and autonomous agents on a resilient, fault-tolerant foundation engineered to prevent costly disruptions.
Decentralized: Deliver consistent context anywhere
Serve consistent, trusted context across every cloud, app, and analytical tool in any shape or format without redundant data copies or brittle pipelines.
Previously constrained by legacy batch architectures and high operational overhead, moving to a Streamhouse architecture powered by Confluent transformed our event streams into governed, reusable, real-time data products that flow seamlessly across operational and analytical systems. Confluent eliminated the friction of custom integrations, dropping our time-to-insight from months to days and empowering our teams to focus on delivering high-value AI for our 800,000 employees.
As an AI-native commerce platform, Extend requires exceptional adaptability across rapid merchant onboarding, real-time pricing, and instant fraud detection. Implementing a 100% serverless Streamhouse architecture anchored by Confluent Cloud turns Kafka into our operational source of truth, enabling downstream microservices to asynchronously consume and query real-time data without breaking contracts. This decoupled streaming foundation provides the flexibility and responsiveness needed to scale operations and power real-time agentic AI workflows.
Agentic AI is transforming real-time data from a convenience to a necessity. In IDC’s 2026 Event-Driven Automation and Analytics survey, two-thirds of organizations are investing heavily in or already using agentic AI, with 83% citing real-time data as improving AI trust, accuracy, and decision-making. Streamhouse’s combination of current data and governance exemplifies the future of enterprise data architecture, with IBM and Confluent positioned to meet this demand.
The primary bottleneck in enterprise AI today isn’t foundation-model intelligence. Rather, it is feeding autonomous agents fresh operational data at machine speed. Over 90% of data leaders report hitting severe architectural bottlenecks when deploying actionable agents, with stale batch data directly driving hallucinations and execution failures. IBM’s introduction of Streamhouse resolves this roadblock by delivering sub-second, event-driven working memory directly to reasoning engines. By replacing rigid, batch-centric pipelines with real-time streaming context, Streamhouse provides the factual anchor agents require to execute trusted, production-grade decisions.
Resources
Explore webinars, videos, blogs and more resources to learn how to advance towards real-time action at scale with Streamhouse.
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Streamhouse | FAQs
What is a Streamhouse?
A Streamhouse is a data architecture that continuously captures, processes, governs, and serves the current state of a business. It gives production applications, analytics, and AI agents fresh, trusted context they can use as events happen.
Why do organizations need a Streamhouse?
Applications and AI agents increasingly need to make decisions while business events are unfolding. When data arrives through scheduled batch updates or disconnected pipelines, those systems can act on stale or conflicting information. A Streamhouse keeps business context current and reusable across the systems that need it.
How is a Streamhouse different from a lakehouse?
A lakehouse is built to store and analyze historical data, while a Streamhouse keeps current business state continuously available to operational applications and AI agents. The architectures serve different workloads and can work together, including through open data formats.
Does a Streamhouse replace our existing warehouse or lakehouse?
A Streamhouse complements your existing warehouse or lakehouse; it does not require a rip-and-replace. Keep using those systems for reporting, historical analysis, and other workloads where batch freshness is sufficient, and use a Streamhouse for workloads that depend on current business context.
Is Streamhouse tied to a particular vendor? Can we build one with open-source technology?
Streamhouse is an open, vendor-neutral architecture category, not a product owned by one company. Organizations can build one with open-source technologies, commercial products such as managed Kafka, Flink or Iceberg, or a combination of both.
What does Confluent have to do with Streamhouse?
Streamhouse describes the architecture; Confluent’s Data Streaming Platform provides the most unified, performant and open foundation for building one. It brings together capabilities to stream, connect, process, govern, and query real-time data, reducing the need to assemble and operate separate tools for each capability.
What capabilities make up a Streamhouse?
A Streamhouse combines capabilities to capture and connect data, stream and store events, process changes, govern data, and serve or query current state. Together, these capabilities make business context available to applications, analytics, and AI agents without requiring every consumer to build a separate data pipeline.
How does governance fit into a Streamhouse?
Governance is part of the architecture, not an afterthought. Schema, data quality, access, and lineage practices help keep data trusted as it changes and is reused across consumers. The specific controls depend on the technologies and policies in your implementation.
How quickly is data available in a Streamhouse?
A Streamhouse is designed to keep data continuously current as business events occur, rather than waiting for a scheduled batch refresh. Actual end-to-end freshness depends on the source systems, processing, and serving path; the Streamhouse category itself does not promise one latency for every implementation.
What can we use a Streamhouse for?
Use a Streamhouse when applications, analytics, or AI agents need current business context to respond or act. Examples include fraud detection, inventory updates, operational monitoring, customer support, and grounding AI agents with fresh information.