Explore Snowflake best practices and data insights

In 2025 Snowflake is more than a database. It has become the foundation for data, AI, and applications. With almost 10,000 active Snowflake customers** globally and more than 850 certified services partners, the challenge isn't finding a partner. It's finding the right partner who can deliver tangible results while building a sustainable, cost-effective data platform.

In this blog, we outline the key criteria to evaluate when selecting a Snowflake partner in 2025 and explain how the choice you make will directly shape the success of your data initiatives.

What is a Snowflake consulting partner?

A Snowflake consulting partner is a certified services provider that specializes in implementing, optimizing, and managing Snowflake's Data Cloud platform. These partners range from global system integrators managing petabyte-scale deployments to boutique firms focusing on specific industries or Snowflake features.

Snowstack is built for this role. As a Snowflake-first partner, our focus is entirely on helping organizations succeed with the platform. We design and deliver environments that are secure, cost-efficient, and ready for AI. Because we focus exclusively on Snowflake, we bring a level of technical depth, delivery discipline, and industry knowledge that generalist consultancies cannot match.

Best criteria for selecting your Snowflake partner in 2025:

In 2025, not every Snowflake partner delivers the same results. Your choice can determine whether your data projects drive real business value or slip into delays, cost overruns, and a loss of confidence across the organization. Here is what to look for when evaluating a partner’s approach:

1. Delivery methodology as the deciding factor

The single biggest predictor of Snowflake implementation success isn't the partner's brand recognition or size. It's how they deliver. In our analysis of successful Snowflake projects, delivery methodology consistently emerges as the most critical differentiator.

Ask prospective partners:

• What is their delivery rhythm? Look for agile methodologies with short, business-visible delivery cycles rather than waterfall approaches with big reveals at the end
• How do they balance technical debt vs. time to market? The best partners prioritize early wins while building sustainable architecture
• Do they work in short iterations with quick business feedback? Partners should deliver "first dashboard live in 4 weeks" rather than 6-month black box projects
• Can they balance governance and speed? Avoid partners who treat governance as an afterthought or create excessive bottlenecks

What to look for: Partners with repeatable, transparent, and well-documented processes that adapt to your internal structure while maintaining consistent quality standards.

2. Snowflake-native thinking vs. generic cloud advice

The difference between Snowflake specialists and generalist cloud consultants becomes evident in architecture decisions, cost optimization strategies, and feature utilization.

Depth of platform knowledge matters:

• Do they understand Snowflake's native capabilities? Look for expertise in Streams & Tasks, Snowpark, Secure Sharing, Cortex AI, and Dynamic Tables
• Do they optimize for platform strengths? The best partners design for Snowflake's unique architecture rather than forcing legacy patterns
• Can they demonstrate platform-specific know-how? Ask about credit optimization, role hierarchy design, cost guardrails, and performance tuning strategies
• Are they current with latest features? Snowflake releases new capabilities quarterly partners should stay updated

Evaluation technique: Ask candidates to walk through a specific Snowflake architecture decision and explain their reasoning. Generic answers reveal generalist thinking.

A leading financial services firm was spending more than 800,000 dollars per month on cloud costs with little visibility into where the money was going. Within 90 days, we delivered a governed Snowflake platform that reduced data ingestion latency by 80%, enabled AI readiness, and put full cost controls in place.

3. Time to value: shipping early and often

The era of 6-month data projects with big reveals is over. Modern Snowflake implementations should deliver value incrementally, building momentum and stakeholder confidence throughout the process.

Measurement criteria: Ask to see examples of their delivery cadence, backlog management practices, and documentation standards. Partners should have concrete examples of incremental value delivery. For instance, one of our clients, a regional pharma distributor, moved from legacy on-premises systems to a Snowflake-native platform. Instead of a single large rollout, we delivered in focused iterations. Dashboards came first, followed by finance and supply chain integrations, and advanced governance policies were in place before production go-live. This approach kept stakeholders engaged and satisfied.

5. Team structure and location strategy

The 2025 landscape offers multiple delivery models, each with distinct advantages and trade-offs. However critical questions beyond geography:

• Will you get named engineers or a rotating bench? Consistency matters for knowledge retention
• Is there a lead you can trust? Avoid partners who channel everything through project managers without technical depth
• How do they ensure knowledge retention over time? Look for documentation practices and handover procedures

6. Embedded Support vs. one-and-done projects

Snowflake is a living platform that evolves continuously. Your partner relationship shouldn't end at go-live. Successful implementations require ongoing optimization, new source integration, and platform evolution support.

Post-implementation needs include:

• Onboarding new data sources as business requirements evolve
• Evolving data models based on changing business logic
• Performance optimization as data volumes and user counts grow
• Feature adoption as Snowflake releases new capabilities
• Cost optimization through usage pattern analysis

Partner support models to evaluate:

• Embedded engineers: Dedicated resources working as extended team members
• Managed services: Full platform management with SLA guarantees
• Retainer arrangements: On-demand expertise for specific needs
• Training and enablement: Knowledge transfer to build internal capabilities

Key consideration: Partners offering only project-based work may leave you stranded when you need ongoing support most. Unlike project-only vendors, our experts stay engaged long after go-live. Our model ensures that as your data platform grows, you have continuous access to the same experts who built it, ready to integrate new sources, optimize costs, and adopt new Snowflake features.

7. Governance, cost control, and trust

Platform ownership extends far beyond delivering functional pipelines. Successful Snowflake implementations require robust governance frameworks, proactive cost management, and enterprise-grade security practices.

Essential governance capabilities:

• Role-based access control and masking policies aligned with your security requirements
• Cost observability and alerting systems to prevent budget surprises
• Compliance framework alignment (SOC 2, GDPR, HIPAA, PCI-DSS)
• CI/CD and documentation practices for long-term maintainability
• Data quality and lineage tracking for trustworthy analytics

Without a solid governance foundation, a Snowflake platform may appear to work at first but will not scale sustainably. In our blog you can explore this topic in depth, but here is a snapshot of the cost control practices we recommend.

• Warehouse auto-suspend and auto-resume configuration
• Query result caching optimization
• Clustering key recommendations
• Storage optimization strategies
• Credit usage monitoring and alerting

8. AI Readiness and responsible adoption

Snowflake is rapidly evolving into a core platform for AI and machine learning, but realizing its potential requires more than connecting models to data. Successful implementations demand partners who can design secure, scalable, and responsible AI foundations inside Snowflake.

Essential AI readiness capabilities:

• Integration of Cortex AI for LLM-based applications with enterprise controls
• Snowpark ML workflows for efficient model training and deployment
• Feature store design for consistent and reusable machine learning pipelines
• AI governance frameworks to manage bias, privacy, and ethical use

Without a clear AI strategy built on trusted data, organizations face wasted investment, compliance risks, and a loss of stakeholder confidence. One regional pharma distributor overcame these challenges by migrating to Snowflake with us. With Snowpark ML workflows and governed feature stores, they got accurate demand forecasting and optimized their supply chain while ensuring responsible AI adoption.

Industry-Specific Considerations

Different industries have unique requirements that affect partner selection:

Financial Services: Emphasis on regulatory compliance, data residency, audit trails, and risk management frameworks.

Healthcare & Life Sciences: Focus on HIPAA compliance, data privacy, clinical data standards, and FDA validation support.

Manufacturing: Requirements for IoT data integration, real-time analytics, supply chain optimization, and operational intelligence.

Retail & E-commerce: Need for customer 360 views, real-time personalization, inventory optimization, and marketing analytics.

Technology Companies: Emphasis on developer productivity, API integrations, event streaming, and product analytics.

Snowflake partner red flags to avoid in 2025

Watch for these warning signs during partner evaluation:

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Who is the right Snowflake partner for you and your business in 2025?

Most data migrations don’t fail because of the technology. They fail because of poor execution and weak partner choices. When projects stall, the real cost is not just overspending. It is delayed initiatives, frustrated stakeholders, and lost confidence in the value of data.

In 2025, choosing a Snowflake partner is no longer about ticking boxes for certifications or chasing the lowest cost. It is a strategic decision that will shape whether your data initiatives deliver real business impact or fall short. At Snowstack, we combine deep Snowflake expertise with proven delivery methods, transparent team structures, and a focus on long-term governance and optimization. We help organizations move beyond one-off implementations to build scalable, AI-ready platforms that deliver measurable results and lasting trust in data.

👉 Book a strategy session with our experts now.

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