Modern Pay-As-You-Go Data Platforms: Easy to Start, Challenging to Control

It’s Easier Than Ever to Start Getting Insights into Your Data

The rapid evolution of data platforms has revolutionized the way businesses interact with their data. Today, tools like Databricks and Snowflake have simplified the process, making it accessible for organizations of all sizes to extract meaningful insights. Businesses can onboard these platforms quickly, connect to their existing data sources, and start analyzing data without needing a highly technical team or extensive infrastructure investments. The result? Faster decision-making, streamlined operations, and more opportunities to uncover value. However, this simplicity can sometimes mask the complexities beneath.

The ease of access, while empowering, can lead to usage patterns that inadvertently inflate costs—especially when organizations lack a clear strategy for tracking and managing resource consumption.

Scalability and Flexibility: The Double-Edged Sword of Pay-As-You-Go Models

Pay-as-you-go pricing models are a game-changer for businesses. They provide unparalleled flexibility, allowing organizations to scale resources up or down based on real-time demands. This means no more paying for unused capacity or worrying about outgrowing a fixed-size infrastructure. For example, a retailer might scale up compute resources during the holiday season to manage a spike in sales data or scale down during quieter months to save on costs.

Yet, this flexibility comes with risks. Without proper observability, scaling decisions can lead to unforeseen expenses. A lack of monitoring might result in idle clusters running longer than necessary, overly broad data queries consuming excessive compute resources, or unexpected storage costs due to unoptimized data retention. In these scenarios, the very scalability that makes pay-as-you-go models attractive can undermine an organization’s return on investment.

Diverse User Roles and Decentralized Teams: Amplifying the Cost Challenge

One of the greatest strengths of modern data platforms is their ability to support a wide variety of users—data engineers, analysts, scientists, and even business stakeholders. Each role interacts with the platform differently, bringing unique requirements and objectives. For example, data scientists might focus on building complex machine learning models, requiring significant compute resources. Analysts, on the other hand, might run ad hoc queries or create dashboards, which can vary in their efficiency and resource consumption.

This diversity in usage, while powerful, introduces challenges. Without clear cost observability and governance, these varying needs can result in fragmented practices that drive up costs. A data scientist might spin up a large cluster for experimentation and forget to shut it down, while an analyst might write inefficient SQL queries that consume excessive compute power.

The situation becomes even more complicated with decentralized teams. In larger organizations, data teams often operate independently across business units or geographies, each with their own budgets, way of working, and priorities. This decentralization can lead to overlapping or redundant workloads, untracked usage, and inconsistent application of cost-saving best practices.

Inconsistent practices across roles and teams make it nearly impossible to pinpoint cost drivers or enforce optimizations. The lack of centralized oversight can result in not only ballooning expenses but also missed opportunities to align data platform usage with broader business goals. Observability becomes the glue that holds these disparate activities together, offering visibility into where resources are being used, by whom, and for what purpose.

By implementing robust observability practices and fostering cross-team alignment, organizations can empower diverse users while ensuring that cost efficiency remains a shared priority.

The Mindset of Being Cost Aware

Cost isn’t something to consider only after a platform is up and running—it needs to be embedded in the mindset from the very beginning. To prevent financial surprises and maximize the return on investment, organizations should treat cost management as a foundational principle when designing, implementing, and scaling their data platforms. This approach ensures that decisions are made with both performance and budget in mind.

Moreover, keeping costs under control shouldn’t be the sole responsibility of a single person or team. It must be a joint effort involving everyone who uses the platform, from data engineers and scientists to analysts and business stakeholders. By fostering a culture where all users are aware of the financial impact of their actions, such as running queries, spinning up clusters, or storing data, organizations can create collective accountability that drives both efficiency and sustainability.

The Mindset of Being Cost Aware: Three Essential Steps

Adopting a cost-conscious mindset involves three critical steps: achieving observability, taking action based on insights, and creating a culture of continuous optimization. These steps form the foundation for effective cost management, ensuring that organizations not only understand their spending but also take proactive measures to optimize it.

Step 1: Observe – The Power to See Costs Clearly

Effective cost management begins with comprehensive observability, enabling organizations to gain insights into their spending at multiple levels of granularity. Each level of observation not only provides a clearer picture of costs but also allows new possibilities for optimization.

Platform Level: At this level, organizations should focus on understanding the total expenditure across their entire data platform. Tracking high-level metrics such as total monthly costs and identifying major cost contributors, including compute, storage, and services, allows organizations to quickly spot trends and anomalies.
Team Level: Breaking down costs by teams or departments is essential for pinpointing which groups are driving usage. This level of detail allows for targeted accountability and optimization, as teams can be held responsible for their resource consumption. This insight can lead to tailored training programs or the implementation of team-specific cost-saving measures.
Use case level: Evaluate the cost associated with each specific use case or project to understand its financial impact. This ensures that resources are allocated to initiatives that align with strategic business goals and provide the greatest return on investment.
Workload Level: Diving deeper into specific jobs, queries, or costly tables provides insights into the most expensive workloads. This level of analysis is key to understanding inefficiencies and identifying areas for improvement. By pinpointing resource-intensive processes, organizations can take targeted actions to optimize performance and reduce costs.

Achieving the workload level of observability is challenging, as most platforms don’t provide out-of-the-box solutions for granular cost tracking. Tagging resources is necessary to associate costs with specific teams, projects, or workloads. However, implementing effective tagging requires careful planning and consistent adherence across the organization.

Step 2: Act – Turning Insights into Optimization

Observability is just the first step toward effective cost management. The real challenge lies in optimizing inefficiencies. To achieve cost efficiency, it’s crucial to start by asking: What are you optimizing for? Different priorities can significantly influence cost management strategies.

Data freshness might be prioritized over cost to ensure that insights are delivered in (near) real-time, which is critical for decision-making in fast-paced environments.
Enhancing developer experience could be deemed more important than cost savings. For example, a data scientist might receive a large cluster to run their experiments more quickly.
Weekend workloads might operate under different SLA requirements.

All these examples are case-specific and should be tailored to the specific needs of the organization. However, having observability and a clear understanding of the cost implications of different workloads is key to making informed decisions.

Once the decision is made, inefficiencies can be categorized into two primary areas: compute and storage.

Compute: Best practices for compute involve ensuring that resources are right-sized and appropriately allocated. For example, avoid running idle clusters by setting up auto-termination policies and ensure that workloads are matched to cluster sizes to prevent overprovisioning.
Storage: Storage optimization focuses on managing and maintaining datasets efficiently. In Databricks, for instance, running maintenance tasks such as the VACUUM command in Delta Lake removes obsolete data files, helping to reduce storage costs while maintaining performance. Additionally, regularly archiving or deleting non-critical data can further prevent unnecessary expenses.

By focusing on these areas, organizations can translate observability into tangible cost savings and operational efficiencies. This proactive approach not only minimizes waste but also enhances the overall performance and sustainability of the data platform.

Step 3: Maintain – Embedding Cost Efficiency into Your Culture

Long-term cost efficiency requires continuous effort, awareness, and collaboration across the organization. Maintainability ensures that the initial cleanup isn’t a one-time effort but an ongoing practice.

Creating Awareness: Foster a culture where all users, from data engineers to analysts, understand the financial impact of their actions. Highlight the cost implications of spinning up clusters, running inefficient queries, and retaining unnecessary datasets, encouraging mindful usage of resources.
Upskilling Teams: Go beyond awareness by providing targeted training and resources. Teach users how to apply best practices, such as efficient query writing, optimal resource allocation, and storage management techniques. This ensures they not only understand the “why” but also the “how” of cost-effective practices.
Establishing Governance Policies: Define and enforce clear policies for resource usage, such as mandatory tagging conventions or cluster termination rules. Governance ensures consistency across teams and provides a structure for accountability, reducing the risk of unchecked costs.

By embedding cost observability and action into the platform’s culture, organizations can empower their teams to maintain control and ensure that their data platforms remain both effective and sustainable.

Summary

Creating a data platform has never been easier, with tools like Databricks and Snowflake offering unparalleled scalability and flexibility. However, this simplicity can lead to unexpected costs without proper management.

Effective cost control relies on three essential steps:

Observe: Gain visibility into spending at platform, team, and workload levels to understand usage patterns and inefficiencies.
Act: Optimize resource usage by implementing best practices for compute and storage to reduce waste and improve efficiency.
Maintain: Build a cost-aware culture by training teams, enforcing governance policies, and promoting shared accountability.

By combining observability, actionable strategies, and continuous improvement, organizations can keep costs in check while fully leveraging the potential of modern data platforms.

See clearly, spend wisely: The power of data platform observability