Valohai + Snowflake
Secure, Scalable MLOps Inside Your Snowflake Environment
Valohai is proud to be part of the Snowflake Partner Network, working to bring full-lifecycle MLOps orchestration into the heart of modern data ecosystems.
Through a native integration with Snowpark Container Services (SPCS), Valohai enables customers to run training, fine-tuning, and batch inference jobs directly within their Snowflake environment — with no data egress, no pipeline duplication, and no compliance friction.
Build, train, and deploy ML — where your data already lives.
🔧 What the Integration Enables
✅ Keep Your Data Inside Snowflake
With Valohai orchestrating compute directly in your Snowflake environment, ML workloads can run without ever transferring sensitive data — reducing risk and accelerating approvals.
✅ Containerized Workloads via Snowpark
Valohai triggers and manages containerized training or inference jobs through SPCS, adding automation and reproducibility to your Snowflake-native workflows.
✅ Reproducible ML Pipelines
From training to batch inference, every run is versioned — parameters, environments, and data inputs fully tracked. Ideal for enterprise-grade auditability and governance.
💡 Key Benefits
| Capability | Technical Value | Business Impact |
|---|---|---|
| Native Snowpark Orchestration | Run training & inference jobs inside Snowflake via SPCS | Eliminate custom glue code & reduce engineering overhead |
| Data Remains in Snowflake | No data leaves the VPC during ML workloads | Minimize compliance risks & speed up procurement/security alignment |
| Full Pipeline Reproducibility | Versioning of code, data, parameters, and outputs | Streamline audits, model explainability, and governance |
| Automated ML on Snowpark Data | Direct pipeline execution on in-platform data | Shorten time-to-insight and time-to-market |
| End-to-End Lifecycle Control | CI/CD logic for ML workflows in production | Enable continuous ML delivery with lower operational burden |
🧠 Who It’s Built For
This partnership is built for organizations that are data-first, Snowflake-native, and operate in compliance-heavy or high-scale ML environments.
Perfect for teams who need:
- Tight data governance and zero data duplication
- Enterprise-scale ML workflow automation
- Full auditability and reproducibility across the ML lifecycle
⚙️ How It Works
- ML team defines the pipeline in Valohai
- Valohai schedules and runs jobs via Snowpark Container Services
- Jobs execute securely inside the customer’s Snowflake VPC
- Model outputs and logs are tracked in Valohai – without moving any source data
🔍 Why It Matters
Snowflake has redefined data infrastructure. Valohai brings that same control and efficiency to MLOps.
With this integration, organizations can:
- Eliminate data egress and reduce security risk
- Automate ML workflows without needing orchestration code
- Accelerate time-to-value by operating where data lives
- Strengthen governance with full experiment traceability
- Scale safely and globally, even across regulated environments
🚀 Want Early Access?
We’re onboarding select customers to the Valohai + Snowflake integration.
W If you’re running ML on Snowflake — or plan to — we’d love to show you how Valohai makes it reproducible, automatable, and enterprise-ready.
Reach out for early accessExplore all partnerships