Your Snowflake Credits Are Burning on Noise
Every duplicate record, every raw log line, every unfiltered telemetry event costs credits to ingest, store, and query. Expanso filters upstream - Snowflake only processes what matters.
Why Snowflake Costs Spiral
Credits scale with data volume. When everything flows in unfiltered, your compute and storage costs grow faster than your actual data needs.
Credits scale with volume
$2-4 Per Credit Adds Up
Snowflake credits are consumed for every query, every Snowpipe load, and every warehouse minute. More data means more credits - regardless of whether that data delivers value.
Warehouses auto-scale on junk
Compute Wasted on Noise
Auto-scaling warehouses spin up larger clusters to handle growing data volumes. But if 40-60% of ingested data is duplicates or low-value records, you're scaling compute for garbage.
Storage compounds monthly
Data In, Never Out
Snowflake storage costs are relatively low per TB, but they compound. Unfiltered ingestion means storage grows faster than business needs. Time Travel and Fail-safe multiply the cost of every wasted byte.
Clean Data Before It Hits Snowflake
Expanso sits upstream of Snowpipe and COPY INTO. Data is filtered, deduplicated, and optimized before a single credit is consumed.
How Expanso Cuts Snowflake Costs
Reduce credit consumption across ingestion, compute, and storage
Pre-ingestion filtering
Drop Before Snowpipe
Filter out noise, duplicates, and low-value records before they reach Snowpipe or external stages. Fewer records ingested means fewer credits consumed.
Deduplication at source
Eliminate Before Load
Remove duplicate records before they land in Snowflake. No need for expensive MERGE operations or post-load deduplication queries that burn warehouse credits.
Format optimization
Parquet Before Ingestion
Convert data to compressed Parquet format before loading. Snowflake processes columnar data faster, using fewer credits per query and less storage per row.
Warehouse usage reduction
Right-Size Through Data
When ingestion volume drops 40-60%, warehouse auto-scaling stays smaller. Queries run faster on clean data, reducing active warehouse time and credit consumption.
Schema normalization
Clean on Arrival
Transform and normalize data before ingestion so Snowflake doesn't need complex TRANSFORM steps during COPY INTO. Less compute per load, fewer failed loads.
Intelligent staging
Stage Less, Load Faster
Only stage data that passes quality and relevance checks. Reduce external stage storage costs and accelerate load times.
Proven Snowflake Cost Reductions
Real results from upstream data optimization for Snowflake workloads
Cost reduction for enterprise data warehouse with 1,300 data sources
Reduction in data volume moved to cloud for processing
Faster query performance with cleaner, deduplicated data
Typical credit consumption reduction across Snowflake workloads
Real-World Impact
See how organizations cut Snowflake costs with upstream data control
Fortune 500: 58% Warehouse Savings
A Fortune 500 retail chain centralized 3.5 PB of store data into their cloud warehouse. Massive egress and compute costs drove monthly spend to $358K. Expanso processed queries where data lives and filtered data before cloud ingestion.
Bank Observability: 63% Volume Reduction
A regional bank ingested 14.3 TB/day of logs into their analytics platform. Most was noise. Expanso classified and filtered at the source, demonstrating the same upstream approach that applies to any high-volume Snowflake workload.
Why Expanso for Snowflake
Upstream of Snowpipe
Integrates before data reaches Snowflake stages or Snowpipe. No changes to your Snowflake configuration needed.
No Snowflake lock-in
Works alongside Snowflake without depending on it. If you move workloads to Databricks or BigQuery, Expanso moves with you.
Prove savings before renewal
Run a proof of concept before your next Snowflake renewal. Show measurable credit reduction and negotiate from a position of strength.
Free tier to start
Process up to 1TB/day free. Test on your highest-volume Snowpipe streams and measure actual credit savings.
Optimize Costs Across Your Stack
See how Expanso reduces costs for other platforms
Frequently Asked Questions
How does Expanso integrate with Snowflake?
Expanso sits upstream of your Snowflake ingestion pipeline. It processes data before it reaches external stages, Snowpipe, or COPY INTO commands. Data arrives in Snowflake in the same format - just cleaner, deduplicated, and smaller.
Will this affect our Snowflake queries and dashboards?
No. Downstream queries, dashboards, and reports continue working as before. The data in Snowflake is the same - just without the noise and duplicates that were inflating volume and slowing queries.
How much can we save on Snowflake credits?
Typical savings range from 40-60% of credit consumption, depending on data type and current duplication rates. The biggest savings come from high-volume ingestion workloads where a significant portion of data is noise or duplicates.
Does this work with Snowflake's data sharing?
Yes. Expanso filters data before it enters Snowflake. Once in Snowflake, data sharing works normally. Shared data is cleaner and smaller, which also reduces credit consumption for consumers of shared data.
Can we optimize specific Snowflake databases or schemas?
Yes. You can target Expanso filtering at specific data pipelines feeding specific databases or schemas. Most customers start with their highest-volume ingestion streams and expand from there.
Your Snowflake credits going up in smoke?
Every duplicate record and noise log line costs credits to ingest, store, and query. Filter upstream and pay for what matters.