Cut Data Platform Costs 40-70% With Upstream Control
Stop paying to store, process, and transfer data you don't need. Expanso filters and processes data at the source - your downstream platforms only ingest what matters.
Why Your Data Bills Keep Growing
You're paying premium rates to store, process, and transfer data that nobody uses. The problem isn't your platform - it's what you're sending to it.
Bills growing faster than your data
Linear Cost Scaling
Vendors charge by volume, compute, or credits. As data grows, costs scale linearly - but most of that data is noise, duplicates, or low-value telemetry nobody uses.
60-80% of ingested data is waste
Paying for Noise
Debug logs, health checks, duplicate records, and raw telemetry flow into expensive platforms unchanged. You're paying premium rates to store data nobody queries.
Lock-in makes optimization hard
Vendor Lock-in
Proprietary formats, complex pricing tiers, and migration costs keep you paying. Native cost tools only optimize within their own ecosystem.
Control Data Before It Costs You
Filter, transform, and route data at the source. Only pay for what actually reaches your downstream platforms.
Optimize Costs Across Your Entire Stack
Platform-specific strategies powered by upstream data control
AWS
Reduce EC2, S3, and data transfer costs by filtering data before it leaves your VPC
Azure
Cut Azure Synapse, Blob Storage, and Log Analytics spend with upstream filtering
GCP
Lower BigQuery scan costs and Cloud Storage egress with source-side data control
Kubernetes
Reduce cluster compute, storage, and cross-cluster transfer costs
Snowflake
Cut credit consumption by filtering and deduplicating data before Snowpipe ingestion
Databricks
Lower DBU consumption and Delta Lake storage costs with pre-ingestion filtering
Splunk
Slash volume-based licensing costs by classifying and filtering logs at the source
How Expanso Cuts Your Costs
Six upstream strategies that reduce what reaches your expensive platforms
Drop the Noise
Source-Side Filtering
Classify and drop debug logs, health checks, and low-value telemetry before they leave the source server.
Summarize, Don't Store
Smart Aggregation
Convert high-frequency metrics into hourly summaries. Same insights, fraction of the storage and compute costs.
Comply Without Duplication
PII Masking at Origin
Mask sensitive data before it moves. No need for separate compliance pipelines or costly post-ingestion redaction.
Right Data, Right Tier
Intelligent Routing
Send critical data to hot storage, routine data to cold tiers, and noise to /dev/null. Route by value, not volume.
Eliminate Redundancy
Deduplication at Source
Remove duplicate records before ingestion. Fewer records means fewer credits, less storage, and faster queries.
Compress Before Transfer
Format Optimization
Convert, compress, and batch data before transmission. Reduce egress costs and accelerate ingestion.
Proven Cost Reductions
Real results from organizations using Expanso to cut data platform costs
Log volume reduction for a regional bank feeding Splunk
Annual savings for financial services observability pipeline
Cloud egress reduction for Fortune 500 retail data warehouse
Cost reduction for manufacturing ML inference pipeline
Real-World Cost Savings
See how organizations cut millions from their data platform bills
Splunk Licensing: $3.7M to $1.4M
A top-25 regional bank was spending $3.7M annually on Splunk licensing. 73% of ingested logs were debug messages and health checks. Expanso classified logs at the source - critical security events forwarded in real-time, noise dropped.
Data Warehouse: $358K to $211K/month
A Fortune 500 retail chain centralized 3.5 PB of store data in the cloud, paying massive egress and compute costs. Expanso enabled processing queries where data lives.
O-RAN Telemetry Costs Cut 47%
A European telecom operator running O-RAN infrastructure generated massive telemetry volumes. Expanso filtered and aggregated network telemetry at the RAN before forwarding to Splunk.
Stadium Analytics: $1.2M Annual Savings
A major North American sports league ran data pipelines across stadiums with frequent failures and high cloud costs. Expanso processed data locally before cloud upload.
Frequently Asked Questions
How much can I realistically save?
Most organizations see 40-70% cost reduction depending on the platform and data type. The biggest savings come from log processing (50-70% volume reduction) and data warehouse optimization (40-60% compute reduction). We offer a free assessment to estimate your specific savings.
Does Expanso replace my existing platform?
No. Expanso sits upstream of your existing platforms - Splunk, Snowflake, AWS, Databricks, etc. It filters and transforms data before it reaches those platforms. Your teams keep using the same tools, just with cleaner data and lower bills.
How quickly will I see cost savings?
Most deployments see measurable savings within 4-6 weeks. The Splunk case study saw 63% volume reduction within 9 weeks of deployment across 247 log sources. ROI typically exceeds deployment cost within the first quarter.
What types of data benefit most from cost optimization?
High-volume, high-frequency data streams see the largest savings: application logs, infrastructure telemetry, IoT sensor data, event streams, and network traffic. Any data where a significant portion is noise, duplicates, or low-value records is a good candidate.
Is there a free tier to test with?
Yes. Expanso offers free processing for up to 1TB per day. This is enough to run a proof of concept on a meaningful data stream and measure actual cost savings before committing.
Your data platform bills keep climbing
Every month you wait, you're paying to store data nobody uses. Let's fix that.