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Innovate faster
Reduce friction across teams with integrated workflows that streamline experimentation and production.
What it is
Teradata Cloud for always-on intelligence and on-demand innovation
Run continuous, production-grade AI and analytics without compromise. Teradata Cloud—the cloud deployment of the Teradata Autonomous Knowledge Platform—runs always-on active compute and on-demand elastic compute in a single managed system. Execute mission-critical lakehouse and analytics workloads while continuously operating agentic AI—all on one governed platform.
Teradata Cloud is purpose-built for enterprise workloads across AWS, Microsoft Azure, and Google Cloud, providing predictable performance, reliability, and cost control at scale.
Built for autonomous enterprise intelligence
Reason, act, and scale across unified, agentic AI and enterprise knowledge
Unify data management, analytics, machine learning, and agentic AI execution through Teradata AI Studio and Teradata Fabric. Ground your autonomous AI agents (Tera and Tera Agents) in enterprise context with built-in governance, data lineage, vector search, and open table format support.
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Operate autonomously
Run analytics and AI with self‑managing cloud services that optimize performance, security, and cost—without manual intervention.
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Scale on-demand
Elastic, modular compute scales up or down as demand changes—without over-provisioning.
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Eliminate complexity
Reduce fragmentation and tool sprawl by consolidating core AI and analytics capabilities into one coherent experience.
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Adapt effortlessly
Run where it makes the most sense—cloud, hybrid, or edge—without forcing a one-size-fits-all architecture.
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Run mixed workloads
Support AI and analytics, lakehouse, EDW, and data engineering side-by-side on a single platform.
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Unify governance
Apply consistent identity, access, and policy controls across cloud and hybrid environments.
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Trust at scale
Put AI into production with confidence through consistent security and governance across environments.
Active compute and Elastic compute
Most enterprises need two distinct kinds of compute capacity to manage modern AI and data workloads
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Active compute
Powers continuous, always-on operational reporting, customer-facing queries, and agentic AI workflows that operate 24/7 and cannot tolerate cold starts or performance degradation.
Both models run inside a single managed system under unified workload management. Unlike elastic-only architectures, continuous production workloads never compete with bursty analytics for the same resource pool.
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Elastic compute
Provides on-demand capacity for model training, experimentation, ad-hoc discovery, and seasonal bursts, scaling automatically within defined boundaries and releasing resources when idle.
Reach for active compute when the workload runs continuously and cannot tolerate latency spikes—such as operational reporting or always-on agent workflows. Reach for elastic compute when demand is intermittent and returns to zero upon job completion—such as model training and exploratory analytics.
Elastic compute operates directly on open table formats, including Apache Iceberg and Delta Lake, within low-cost object storage. The elastic compute documentation covers how scaling ranges are configured, and our guide on cloud elasticity and cloud scalability details the underlying architecture.
Elastic-only platforms (such as Snowflake, Databricks, or Google BigQuery) treat every query as a burst workload. While effective for ad-hoc experimentation, this approach introduces performance variability into continuous operational processes. Teradata Cloud runs active compute alongside elastic compute in one governed system. Continuous production workloads benefit from predictable infrastructure, while experimental tasks scale dynamically on demand. For an in-depth workload breakdown across cloud platforms, explore our full workload comparison.
Frequently asked questions
Teradata Cloud FAQs
What is Teradata Cloud?
What is Teradata Cloud?
Teradata Cloud is the cloud deployment of the Autonomous Knowledge Platform available in AWS, Microsoft Azure, and Google Cloud. In May 2026, elastic compute was introduced, supporting both always-on workloads and elastic experimentation within a single environment.
What problems does Teradata Cloud solve?
What problems does Teradata Cloud solve?
Teradata Cloud enables organizations to run continuous analytics and AI workloads without fragmenting data, duplicating systems, or compromising governance, while still supporting innovation and experimentation at scale.
What is elastic compute?
What is elastic compute?
Elastic compute is on-demand compute for training, experimentation, and burst workloads.
How does elastic compute work with open table formats?
How does elastic compute work with open table formats?
It operates directly on Iceberg and Delta tables in object storage.
How does Teradata balance active compute, always-on, and elastic compute?
How does Teradata balance active compute, always-on, and elastic compute?
The platform supports both active compute for always-on compute for mission-critical operations and elastic compute for experimentation and burst workloads—allowing organizations to run continuous intelligence while scaling innovation on demand.
Does data leave the on-premises environment in a hybrid deployment?
Does data leave the on-premises environment in a hybrid deployment?
No. Data remains in its original environment unless explicitly configured to move. Organizations control where data is accessed, where processing occurs, and how information is shared across cloud and on-premises systems.
Speak to the team
Discover what best-in-class data connectivity and analytics can do for your enterprise.