A practical guide to platform selection based on your needs, capabilities, and goals
In today’s data-driven landscape, companies face a crucial architectural decision: whether to adopt a SaaS-based analytics platform like Databricks or Snowflake or to build a tailored Lakehouse solution in-house with support from a partner like Montrose Software.
This article offers a strategic framework for making that choice, covering key technology stack considerations, organizational maturity thresholds, and a hybrid approach.
Overview
Databricks
A unified data and AI platform offering lakehouse architecture, collaborative notebooks, Delta Lake, MLflow, and Mosaic AI for LLMs. It's a powerful SaaS option ideal for fast-moving teams who need scalability, advanced analytics, and immediate AI/ML capabilities.
Snowflake
A cloud data warehouse designed for ease of use and fast SQL analytics. It excels in structured BI workloads, secure data sharing, and zero-maintenance operations. While it has added ML/LLM capabilities via Cortex, it's not designed for deep model training or heavy data engineering.
Montrose Software Lakehouse
A customizable, open-source lakehouse solution built and managed by Montrose Software. It uses best-of-breed components like Apache Hudi, Iceberg, Spark, Kafka, Flink, MLflow, and Superset. Ideal for companies that want cost control,a focused kick start with architecture flexibility, and reduced vendor lock-in.
Thresholds for building in-house
Building your own lakehouse platform requires certain organizational capabilities. Here are the key thresholds to evaluate:
- CloudOps / DevOps Maturity: You'll need automation, infrastructure-as-code, and monitoring in place as well as teams ready to handle the IT challenges promptly in a structured fashion.
- Big Data Engineering Talent: Spark, Kafka, Flink, Delta/Hudi/Iceberg require experience.
- Security and Compliance Needs: If you need fine-grained IAM or sovereignty controls.
- Desire for Cost Control: SaaS licensing can grow quickly; OSS can reduce costs long-term. Investing in FinOps is critical to make sure you spend where it matters most.
- Long-Term Strategic Autonomy: Avoiding vendor lock-in or integrating niche components (like specific LLMs).
If the client lacks the engineering capacity but wants control, Montrose offers a middle path: we build and maintain the lakehouse for you, tailored to your requirements.
Hybrid approach
We often consider a hybrid approach, leaving space for adaptability in the future.
Phase 1: POC/MVP with Databricks or Snowflake
- Get to value quickly using Databricks or Snowflake
- Test business logic, dashboards, and ML/LLM use cases.
- Validate performance and usage patterns.
Phase 2: Expand and Transition to Montrose Lakehouse
- Re-implement successful pipelines on the OSS stack.
- Use Hudi/Iceberg for compatibility.
- Replace dashboards and notebooks with open-source tools or integrate with the Frontend tools you are already using today.
Phase 3: Optimize & Decommission
- Fully migrate storage, compute, and orchestration.
- Keep Databricks selectively (e.g., for GPU workloads).
- Achieve cost savings and full architectural control.
This approach gives your organization early results without long-term SaaS premiums.
When to choose what?
| Use Case | Databricks | Snowflake | Montrose Custom Lakehouse |
|---|---|---|---|
| Fast MVP / POC | Yes | Yes | Slower to implement unless very specific requirements are well-defined and close-ended |
| Capex vs Opex financing | Opex | Opex | Hybrid |
| Deep ML/LLM Workflows | Best Choice | Inference only | With engineering help |
| Streaming Data Ingestion | Strong | External-only | Flexible + scalable |
| Cost Efficiency (at scale) | Can get expensive | Premium pricing | OSS = low cost, high control |
| Custom Architecture | Limited | Closed system | Full control |
| Multi-cloud / Sovereignty Needs | Good | Good | Total freedom |
| Need for consulting services for setup and operations | Yes, partially elevated with the help of Databricks resources | Yes, partially elevated with the help of Databricks resources | Flexible: Embedded in the pricing or time-and-materials depending on the needs |
Business use case
At the core of any transformation is the “why.”
Whether you're a Data Practitioner looking to improve data quality and governance, a Product Owner striving to deliver actionable insights faster, or a CTO aiming to future-proof your architecture with AI and automation, our mission is to support your vision with the right technology and delivery strategy.
Are you trying to:
- Empower internal data teams with scalable, reliable pipelines?
- Unlock a new portfolio of precise, decision-ready BI reports?
- Improve business efficiency with intelligent assistants that automate repetitive tasks?
At Montrose Software, we apply Silicon Valley-inspired product thinking and methodologies like Amazon’s “working backwards” approach to ensure every solution serves a clear business objective.
From data infrastructure modernization to custom AI applications, we tailor our platforms and tools to support your long-term growth, solving immediate challenges while laying the foundation for innovation.
Tech stack considerations
| Component | Databricks | Snowflake | Montrose Lakehouse |
|---|---|---|---|
| Storage Format | Delta Lake | Proprietary | Delta / Hudi / Iceberg / s3 |
| Stream Ingestion | Autoloader, Kafka | External only | Kafka, Pulsar, Kinesis |
| Stream Processing | Spark Streaming | Not native | Flink, Spark Streaming, GoLang on K&8 |
| ML & AI Pipelines | MLflow, Mosaic AI | Cortex (limited inference) | MLflow, KubFlow, LangChain, custom LLM endpoints, MCP |
| BI Dashboards | Native Dashboards | Tableau, Power BI | Apache Superset,Power BI, Tableau |
| Catalog & Lineage | Unity Catalog | Snowflake Data Governance | Amundsen, DataHub, Nessie (for Iceberg) |
| Notebook Dev | Collaborative Notebooks | None | JupyterHub, VSCode, Deepnote |
Choosing the right lakehouse platform isn’t just about features, but even more importantly, it’s about alignment with your timing, teams, timeline, budget, and long-term data strategy.
- Use Databricks for speed and AI-rich MVPs and to secure future-proof enterprise readiness and ready-to-scale,
- Use Snowflake for easy analytics and dashboards, enterprise, and scale-ready.
- Use Montrose Lakehouse for full control, open standards, and scalable savings.
Or, best of all: use them in tandem, and let Montrose guide your roadmap toward a tailored, cost-effective, AI-ready data platform.
Ready to explore your options? Contact Montrose Software for an architecture workshop or discovery call.