Understanding Notebook Costs on Snowpark Container Services: A Business Perspective

Snowpark Container Services (SPCS) notebooks have transformed how data science teams work within Snowflake’s ecosystem, offering powerful containerised development environments directly integrated with your data warehouse. However, with this capability comes an important question for business leaders and FinOps practitioners: how do we ensure we’re consuming Snowflake credits efficiently, running the right workloads at the right time?

The Business Case for Understanding SPCS Notebook Costs#

SPCS notebooks provide data scientists with familiar Jupyter-style interfaces whilst maintaining data governance and security within Snowflake. This integration eliminates data movement and simplifies MLOps workflows. Yet unlike traditional Snowflake warehouses that suspend automatically after brief idle periods, SPCS notebooks introduce different consumption patterns that require strategic cost management.

The critical business challenge isn’t avoiding SPCS notebooks altogether—it’s understanding when their value justifies the consumption, and optimising usage to maximise return on investment. This requires a fundamental understanding of how SPCS notebooks consume credits and what drives those costs.

SPCS Notebook Cost Structure Explained#

SPCS notebooks consume Snowflake credits through compute pools—dedicated resources that run your containerised workloads. Unlike virtual warehouses that charge per-second with automatic suspension, compute pools bill continuously whilst running, regardless of active computation.

Key cost components include:

• Compute pool credits: Charged based on instance type (CPU/GPU specifications) and runtime duration
• Idle time consumption: Pools continue consuming credits even when notebooks aren’t actively executing code
• Container image storage: Minimal cost for storing custom notebook images
• Data processing: Additional warehouse costs if notebooks trigger separate query execution

Instance sizes typically range from 1-2 CPU cores (small) to 16+ cores (large), with corresponding credit consumption rates. A medium instance might consume 2-4 credits per hour, whilst large GPU-enabled instances can exceed 20 credits hourly. Understanding these rates relative to your workload requirements forms the foundation of cost optimisation.

Primary Cost Drivers to Monitor#

Runtime Duration The most significant cost driver is how long compute pools remain active. A notebook left running overnight consumes credits unnecessarily. Unlike warehouse queries that complete and suspend, interactive development sessions can span hours.

Instance Size Selection Overprovisioned instances waste credits. A data exploration task requiring 2GB RAM running on a 64GB instance generates 10x unnecessary cost. Right-sizing requires understanding workload memory and CPU requirements.

Auto-Suspend Configuration SPCS compute pools support auto-suspend after specified idle periods. Setting aggressive auto-suspend thresholds (5-10 minutes) for development environments prevents wastage whilst balancing user experience against cold-start delays.

Concurrent Usage Multiple team members running simultaneous notebooks multiply consumption. A 10-person team each running medium instances for 4 hours daily consumes 80-160 credits daily—potentially 2,400-4,800 credits monthly.

The Right Workload, Right Time Framework#

When SPCS Notebooks Excel Financially:

• Iterative ML model development requiring frequent data access and feature engineering within Snowflake
• Production ML inference serving predictions at scale where integration benefits outweigh compute costs
• Collaborative data science where governance and security requirements justify premium consumption
• Complex transformations better suited to Python/containers than SQL alone

When Traditional Warehouses Win:

• Ad-hoc analysis where SQL queries on virtual warehouses provide faster, cheaper results
• Scheduled batch processing where warehouse auto-suspend eliminates idle costs
• Simple transformations efficiently handled by Snowflake’s native compute

When External Platforms Make Sense:

• Deep learning training requiring specialised GPU configurations unavailable or cost-prohibitive in SPCS
• Existing MLOps pipelines with sunk costs in alternative platforms
• Infrequent workloads where serverless pricing models (SageMaker, etc.) prove cheaper

Practical Cost Optimisation Strategies#

Right-Size Compute Pools Begin with smaller instances and scale up based on observed performance. Monitor memory and CPU utilisation to identify optimisation opportunities.

Implement Smart Auto-Suspend Configure development pools with 5-10 minute auto-suspend thresholds. Production inference services might warrant longer thresholds or continuous operation, depending on latency requirements and request patterns.

Establish Development vs Production Tiers Development environments should use minimal viable instances. Reserve larger, more expensive configurations for production workloads where performance directly impacts business outcomes.

Schedule Batch Workloads Strategically Run training jobs during off-peak hours if your Snowflake pricing includes time-based discounting, or batch multiple experiments to maximise compute pool utilisation during active periods.

Monitoring SPCS Notebook Costs with SQL#

Track SPCS consumption using Snowflake’s metering views:

-- Daily SPCS compute pool credit consumption
SELECT
    DATE_TRUNC('day', start_time) AS usage_date,
    service_name,
    SUM(credits_used) AS total_credits,
    ROUND(SUM(credits_used) * 3.50, 2) AS estimated_cost_gbp
FROM snowflake.account_usage.metering_history
WHERE service_type = 'CONTAINER_SERVICES'
    AND start_time >= DATEADD('day', -30, CURRENT_TIMESTAMP())
GROUP BY usage_date, service_name
ORDER BY usage_date DESC, total_credits DESC;

Identify idle compute pools:

-- Compute pools with high idle time (cost optimisation candidates)
SELECT
    service_name,
    COUNT(*) AS total_hours,
    SUM(CASE WHEN credits_used < 0.1 THEN 1 ELSE 0 END) AS idle_hours,
    ROUND(100.0 * idle_hours / total_hours, 1) AS idle_percentage
FROM snowflake.account_usage.metering_history
WHERE service_type = 'CONTAINER_SERVICES'
    AND start_time >= DATEADD('day', -7, CURRENT_TIMESTAMP())
GROUP BY service_name
HAVING idle_percentage > 20
ORDER BY idle_percentage DESC;

Real-World Cost Scenario Comparison#

Assumptions: £3.50 per credit, 20 working days monthly

These scenarios illustrate that SPCS notebooks command a premium. The business question becomes: does the integration value, development velocity, or governance benefit justify the additional consumption?

Cost Governance Best Practices#

Implement Tagging and Attribution Tag compute pools with cost centres, projects, or teams to enable accurate chargeback and accountability.

Set Resource Policies Establish limits on maximum instance sizes per environment type. Development environments shouldn’t access expensive GPU instances without approval.

Regular Cost Reviews Schedule monthly consumption reviews identifying optimisation opportunities: underutilised pools, oversized instances, or workloads better suited to alternative compute.

Budget Alerts Configure Snowflake resource monitors on compute pools to alert when consumption exceeds expected thresholds, preventing budget overruns.

Conclusion#

Understanding SPCS notebook costs from a business perspective transforms consumption from an uncontrolled expense into a strategic investment. The framework is straightforward: know your cost structure, monitor consumption patterns, match workloads to appropriate compute resources, and implement governance that balances enablement with optimisation.

SPCS notebooks deliver tremendous value for the right workloads—integrated data science, containerised ML, and governed collaboration. The key is ensuring you’re running the right workload at the right time, consuming credits intentionally rather than accidentally.

Key Takeaways#

• SPCS notebooks consume credits continuously whilst running, including idle time—understand this fundamental difference from virtual warehouses
• Monitor the four primary cost drivers: runtime duration, instance sizing, auto-suspend configuration, and concurrent usage
• Apply the “right workload, right time” framework: use SPCS where integration value justifies premium consumption
• Implement practical monitoring using metering SQL queries to identify optimisation opportunities
• Establish cost governance through tagging, resource policies, and regular reviews

Additional Resources#

• Snowflake SPCS Pricing Documentation ↗
• Snowflake Resource Monitors ↗
• Account Usage Views for Cost Analysis ↗