Use case · Cost optimizationMeasure the cost of moving and processing every byte
Expanso runs data-processing pipelines on connected nodes close to where data is generated. Run a bounded evaluation to see whether filtering, transforming, or routing data at the source changes what your workload costs.
Use your baseline, not a universal promise
- 01
Establish the baseline
Record current source volume, destination behavior, workload cost, and required downstream outcomes.
- 02
Select a representative flow
Choose a bounded workload, explicit acceptance criteria, and a reversible routing plan.
- 03
Run near-source processing
Define the evaluation job in YAML, then target connected nodes by label.
- 04
Compare observed results
Measure retained volume, cost, performance, failure behavior, and downstream compatibility.
Start with the platform you already operate
AWS
Transfer, storage, and managed-service volume.
Open guide →Azure
Analytics, observability, storage, and regional flows.
Open guide →Google Cloud
Analytics scans, logging, storage, and data movement.
Open guide →Snowflake
Upstream volume against warehouse work and query utility.
Open guide →Databricks
Upstream volume against lakehouse jobs and data utility.
Open guide →Splunk
Log volume against alert, search, and retention requirements.
Open guide →Kubernetes
Pod-level filtering against cluster compute and cross-cluster egress.
Open guide →Bring a workload to evaluate
See whether filtering, transforming, or routing data at the source changes what your workload costs.
