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.
How does Elastic Compute protect production performance?
How does Elastic Compute protect production performance?
Each Elastic Compute environment is isolated by design. Variable workloads run in their own focused compute environments without competing for shared resources, so production SLAs are never at risk regardless of what else is running.
Do I need to migrate my data to use Elastic Compute?
Do I need to migrate my data to use Elastic Compute?
No. Elastic Compute works natively with object storage and open table formats including Apache Iceberg and Delta Lake. Your data stays exactly where it is. Modernization happens incrementally, at workload level.
What is the Intelligent Workload Fitness Assessment?
What is the Intelligent Workload Fitness Assessment?
Not every workload benefits from Elastic Compute, and that is by design. The Workload Fitness Assessment evaluates your specific workload patterns, scheduling, and data locality to identify where Elastic Compute would deliver measurable improvement before any commitment is made. Talk to your Teradata team to get started.
Who is Elastic Compute designed for?
Who is Elastic Compute designed for?
Elastic Compute is designed for data engineering and FinOps teams running complex, SLA-critical, or mixed analytical and AI workloads on cloud-native infrastructure where compute spend is growing and fixed capacity is no longer the right answer.
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.
