What is Teradata Cloud Elastic Compute?
Elastic Compute is an on-demand compute capability within Teradata Cloud that adds isolated, granular capacity around specific workloads without permanently expanding the production core. It's designed for variable workloads including ELT, AI preparation, agentic workloads, and ad hoc analytics.
The cost of getting compute wrong
Enterprise workloads are increasingly variable. Traditional cloud architectures respond by adding capacity in large increments, creating a predictable cycle: overprovision for the peak and pay for idle compute, or under provision and watch variable workloads compete with production.
- Scheduled pipelines sit idle between runs, paying for compute around the clock
- AI and agentic workloads create unpredictable demand that fixed infrastructure was not designed for
- Variable workloads competing with production create contention, missed SLAs, and delayed pipelines
The right compute for every workload
Elastic Compute adds isolated, on-demand capacity around each workload independently, in smaller increments, and only when needed.
- Granular, incremental scaling: node-based capacity added in precise increments for the specific workload that needs it, without permanently expanding the production core
- Variable workloads, isolated by design. Production performance, protected by default
- Open data, no re-platforming: works natively with Iceberg, Delta Lake, and OTF-based data in object storage, including vectorization for AI and search use cases, without data movement or migration
- Managed workload operations: policy-based scheduling, auto-suspend, governance, and consumption visibility built in by default
Any platform can scale. Not every platform knows what to scale for.
Most cloud platforms can add compute on demand. Making elasticity work across governed enterprise data, mixed workload types, and mission-critical SLAs is a different problem. That is where workload-aware elasticity matters.
- Compute scales in isolation so variable workloads never compete with production
- Governed enterprise data stays in place, with no additional data copies or security perimeters to manage
- Decades of enterprise workload management, applied to the demands of the agentic era
Built for workloads that do not stand still
Elastic Compute delivers value wherever variable, event-driven compute demand meets governed enterprise data. The pattern extends across any industry where workloads are scheduled, intensive, and variable in scale.
Scale with the market, not against it
Financial services teams run hundreds of pipeline jobs daily, spiking around market events and regulatory cycles, with compute that scales up when the job runs and costs that fall to zero when it finishes.
The result is fresher predictions, faster response to demand signals, and engineering teams focused on building models rather than managing infrastructure conflicts.
Retrain models without touching production
Global retailers retrain AI forecasting models on a weekly or event-triggered schedule, with feature engineering and model preparation running in isolated environments while production analytics stay unaffected.
The result is pipelines that finish on time regardless of what else is running, regulatory deadlines met without emergency provisioning, and a clear view of what each workload actually costs.
Teradata Cloud Elastic Compute FAQ
What is Teradata Cloud Elastic Compute?
What is Teradata Cloud Elastic Compute?
Elastic Compute is an on-demand compute capability within Teradata Cloud that adds isolated, granular capacity around specific workloads without permanently expanding the production core. It is designed for variable workloads including ELT, AI preparation, agentic workloads, and ad hoc analytics.
What are the top Elastic Compute use cases?
What are the top Elastic Compute use cases?
Teradata Cloud Elastic Compute is designed for variable, intensive workloads such as AI/ML, ELT and data engineering, ad hoc analytics, experimentation, agentic workloads, and seasonal or event-driven processing.
What problems does Elastic Compute solve?
What problems does Elastic Compute solve?
Teradata Cloud Elastic Compute helps address over-provisioned capacity, idle compute costs, and resource contention when variable workloads compete with production workloads. It provides isolated, on-demand compute that scales independently, allowing organizations to handle workload spikes without permanently increasing their production capacity.
How does Elastic Compute cost?
How does Elastic Compute cost?
Teradata Cloud Elastic Compute is part of Teradata’s Fixed + Flex pricing model. Flex provides on-demand capacity for variable workloads, and you pay only for what you consume, using Teradata Units as the common pricing currency.
Who is Elastic Compute best for?
Who is Elastic Compute best for?
Teradata Cloud Elastic Compute is designed for data engineering and FinOps teams running complex, SLA-critical, or mixed analytical and AI workloads where compute demand is variable and fixed capacity is no longer the right fit. It’s particularly suited to workloads that are intensive, scheduled, bursty, or event-driven.
What are the benefits of Elastic Compute?
What are the benefits of Elastic Compute?
Key benefits include isolated compute for variable workloads, granular capacity that can be added when needed, pay-as-you-go consumption, reduced idle compute, protection of production performance, policy-based scheduling and auto-suspend, and the ability to work directly with data in object storage and open table formats such as Apache Iceberg and Delta Lake.
Is Elastic Compute a good choice for AI and machine learning?
Is Elastic Compute a good choice for AI and machine learning?
Teradata Cloud Elastic Compute supports AI and machine learning workloads, including model training, experimentation, in-database ML and analytics functions, Bring Your Own Model (BYOM) scoring, and Python and R scripts. Its isolated, on-demand compute is designed to handle variable AI/ML workloads without competing with production workloads.
How does elasticity differ from scalability?
How does elasticity differ from scalability?
Scalability is the ability to increase capacity to support sustained growth. Elasticity dynamically adds and releases resources as workload demand changes. In practice, scalability addresses longer-term growth, while elasticity is designed for shorter-term fluctuations such as spikes, bursts, and variable workloads. Teradata supports both Active Compute for continuous workloads and Elastic Compute for on-demand workloads.
Find out if your workloads are a fit
Not every workload needs Elastic Compute. But some of yours probably should. Talk to our team to find out which ones.
