Science & optimisation
Parameter sweeps
Fan out thousands of simulation runs and gather results.
- VM
- Workflows
- NoSQL
- Utilities
The scenario
An agent runs a model across a large grid of parameters and summarises the outcome.
How it works
- 1A workflow launches runs in parallel sandboxes.
- 2Each result is written to a collection.
- 3A utility aggregates the final table.
An illustrative scenario, not a customer story.
Interactive simulation
Simulate this case study
Watch the partition come up, each step run, and usage build as it goes. Simulated: volumes, timings and prices are illustrative.
Partition
—
Status
not created
Elapsed
00:00
- Provisioning
- Processing
- Complete
01A workflow launches runs in parallel sandboxes.
queued- Ahura VMvm.runqueued
status: queued · 0 / 24 sandbox runs
- Ahura Workflowsworkflow.definequeued
status: queued · 0 / 36 steps completed
02Each result is written to a collection.
queued- Ahura NoSQLnosql.getqueued
status: queued · 0 / 600 records written
03A utility aggregates the final table.
queued- Ahura Utilitiesutilities.data_anonymizer_piiqueued
status: queued · 0 / 120 documents processed
Services in this partition
VM
0 calls · 0.00 ACU
Workflows
0 calls · 0.00 ACU
NoSQL
0 calls · 0.00 ACU
Utilities
0 calls · 0.00 ACU
Spend by service
Event log
- Waiting to start.
Why Ahura OS
Why this is better on Ahura OS
A parameter sweep is thousands of small jobs, and reserving a cluster for it means paying for the idle hours either side. On Ahura OS a workflow fans runs out across parallel sandboxes billed by the millisecond, each result lands in a collection and a function assembles the table. Nothing is reserved and nothing idles. Each run is isolated, and results are retained only as long as you choose.
What each capability replaces
- Ahura VMInstead of fleets of machines to patch, every run gets its own disposable sandbox, billed only while it executes.
- Ahura WorkflowsInstead of writing retry, timeout and approval plumbing, you declare the steps and the platform guarantees the behaviour and the history.
- Ahura NoSQLInstead of designing tables and maintaining indexes, you write documents and query them. Indexes are looked after for you.
- Ahura UtilitiesInstead of a function platform to host, common helpers are ready to call and billed per invocation.
And on every use case
One endpoint, not a stack
Memory, storage, compute, inference, identity and messaging share one connection, one identity and one bill. The integration work you would normally own disappears.
Isolation by construction
Every agent’s world is a partition. Separation is how the platform is built, not a convention teams must remember to follow.
Cost follows use
Pay per call and per millisecond. Idle agents freeze to archive pricing. There is no capacity to reserve and nothing running unseen.
Retention and deletion built in
Lifetimes, expiry, freezing and destruction are platform features, so your data policy is enforced rather than aspirational.
Safe autonomy
Spending limits, protected signing keys, approval steps and signed actions let agents work unattended without being unaccountable.
Ready for audit
Designed against SOC 2 and ISO 27001 controls, with every call metered, recorded and attributable.
Compared
The old way of doing things
Reserving a cluster for the week, or queueing for shared compute, then tearing it down by hand.
Data retention is built in
Everything this scenario stores lives in one isolated partition with a lifetime you set. Expired data deletes itself, a paused project freezes to low-cost archive, and a finished one can be destroyed completely. Security and compliance
More in science & optimisation
All use casesIndustries this applies to
More: all use cases, for researchers, for developers, pricing.

