The Complete Guide to Databricks Pricing: Models, Tiers, and Cost Control

Databricks pricing confuses almost everyone. You can estimate cluster size, track job durations, and still be blindsided by the final bill. Ask any engineering lead or even CFO one simple question, “How much is Databricks actually going to cost us?” and you will likely hear some version of “It depends.”

And that’s the problem. Databricks pricing is neither arbitrary nor straightforward. It is a mix of compute units, cloud provider costs, instance types, cluster runtimes, and tiered feature sets. A small tweak in your workload, say, running an interactive notebook versus a scheduled job, can swing your monthly bill significantly. And most of the time, you won’t realize it until after the cost hits.

In this blog, we are going deep into how Databricks pricing works, where most cost assumptions go wrong, and what tools like the Databricks pricing calculator can tell you. If your team is currently building on Databricks or evaluating it for production-grade use, understanding the Databricks pricing model is essential for budgeting and governance.

You will also find answers to common questions like:

• What exactly is a DBU, and why is it at the center of Databricks cost calculations?

• How does pricing vary between Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP)?

• Why do some teams see unexpected spikes, even with predictable jobs?

• How do the workspace tiers, Standard, Premium, and Enterprise, affect your pricing strategy?

• Is the Databricks cost calculator enough for accurate forecasting?

Let’s decode what you are really paying for when you run Databricks and how to make sure it pays you back.

How Databricks Pricing Works at a High Level

For many companies evaluating Databricks, the first pricing conversation usually starts with, “What’s a DBU?” But to make sense of Databricks costs in 2025, that’s only part of the picture.

Let’s break down how Databricks pricing actually works, from what you are being charged for to the variables that influence how much you end up paying every month.

What Exactly Does Databricks Charge For?

At the most basic level, Databricks pricing is usage-based. You are not paying for licenses; you are paying for what you use. But what you are paying for can be divided into four main areas:

1. Compute usage – How long your clusters run and what kind of instances they use

2. Databricks Units (DBUs) – The internal metric that multiplies against compute time

3. Cloud infrastructure (AWS, Azure, GCP) – Where your platform runs

4. Workspace tier – The level of platform features you subscribe to: Standard, Premium, or Enterprise

Each of these layers contributes to your overall Databricks cost. If you are not watching all four, your bills can easily creep up even if usage seems stable.

DBUs: The Heart of the Databricks Pricing Model

Databricks Units, or DBUs, are the foundation of the Databricks pricing model. Think of them as a kind of internal credit score assigned to each type of workload. A DBU represents processing capability per unit of time (per second, billed by the second or hour, depending on your cloud), and every type of workload consumes a different number of DBUs.

For example:

• A job running on a standard job cluster might consume fewer DBUs per hour

• A photon-enabled runtime, which is faster but more powerful, will cost more DBUs

• Interactive clusters for notebooks usually have higher DBU consumption than production job clusters

So even if you are using the same cloud compute, switching the runtime or workload type will affect how many DBUs you are billed for.

This is why two teams using the same infrastructure can see very different Databricks costs because their DBU profiles differ.

Now, DBU rates differ by cloud provider as well as by workspace tier. And your cloud provider will bill you separately for the underlying VMs and storage, so you are essentially balancing two bills: Databricks DBU charges and Cloud infrastructure charges.

Platform vs Cloud-Level Costs: The Dual Billing Model

One of the easiest ways to miscalculate your Databricks cost is by overlooking the fact that you are paying both Databricks and your cloud provider.

Databricks handles the platform like orchestration, notebooks, job scheduling, runtime environments, ML tools, and workspace features. These are what DBUs are tied to.

Meanwhile, your cloud provider (AWS, Azure, or GCP) handles the actual hardware. That includes the virtual machines your clusters run on, the object storage used by your lakehouse (like S3 or ADLS), networking, and any autoscaling capacity you use.

If you are running on Azure Databricks, compute is billed hourly. Azure tends to bundle pricing a bit more neatly, but it still separates Databricks and Azure infrastructure in your invoice.

On AWS or GCP, you will see more granular metering with pricing based on seconds of usage for DBUs, while infrastructure is still charged per-second or per-hour, depending on the service.

This separation is why Databricks cost calculators and pricing pages show only one part of the picture. You still need to look at your cloud provider’s cost dashboard to see the full spend.

Understanding Databricks Workspace Tiers: Standard vs Premium vs Enterprise

When we talk about Databricks pricing, most of the focus goes to DBUs and cloud infrastructure. But there’s another layer that plays a crucial role in what you pay: workspace tiers.

Databricks offers three workspace tiers: Standard, Premium, and Enterprise, each designed for different levels of complexity, security, and compliance needs. These tiers don’t just influence access to features. They also change your DBU rate, affect how workloads are optimized, and impact your long-term cost strategy.

Understanding what each tier offers and what it leaves out can help you make smarter choices before provisioning users or committing to platform-wide rollouts.

1. Standard Tier: The Lightweight Entry Point

The Standard tier is Databricks’ most basic offering. It includes core platform services for compute and collaboration However, compared to the Premium and Enterprise tiers, it includes fewer advanced security and administrative capabilities. 

Core features available:

• Notebook development and collaboration

• Basic compute usage (jobs, interactive clusters, etc.)

• Databricks Runtime support (Spark + ML and Genomics Runtimes)

• Delta Lake for ACID transactions and scalable metadata

• Basic REST APIs and data access

For organizations running small teams, exploratory analytics, or early-stage ML experiments, the Standard tier may suffice. It is cost-effective and easy to get started with. But once teams begin scaling usage or working with sensitive data, the lack of visibility and access control often becomes a bottleneck.

2. Premium Tier: For Growing Teams with Governance Requirements

The Premium tier is a middle ground between flexibility and control. It includes everything in Standard, but adds capabilities designed for scaling teams, operational efficiency, and basic governance.

Key features added in Premium:

• Role-Based Access Control (RBAC): Control who can do what, where, and with which data

• Audit logs: Track access and actions across workspaces

• Job prioritization and quota management: Useful for managing compute-heavy environments

• Cluster access control

• Support for Unity Catalog integration (metadata governance)

• Data protection and compliance extensions

For companies moving toward production-grade workflows, multiple teams, or multiple environments (e.g., dev, staging, prod), Premium helps reduce the risk of accidental exposure or performance bottlenecks.

From a cost perspective, Premium does increase the Databricks DBU pricing. The cost difference between Standard and Premium tiers typically ranges from 20–25%, depending on the compute configuration and workspace features in use.

3. Enterprise Tier: Designed for Regulated, High-Security Workloads

The Enterprise tier is the most advanced and expensive workspace offering. It is built for large organizations with complex regulatory needs, such as financial institutions, healthcare providers, and global enterprises.

Everything from Premium is included, plus:

• Enterprise-level identity and access integrations

• Compliance support for HIPAA, PCI-DSS, FedRAMP, etc.

• Advanced network security (e.g., private link, customer-managed VPCs)

• Automated security monitoring

• Multi-region support and data residency control

This tier isn’t always necessary, but for teams dealing with PII, PHI, or cross-border data, it becomes non-negotiable. And with growing scrutiny over data handling and sovereignty, more enterprise IT teams are defaulting to this tier.

From a pricing standpoint, Enterprise commands the highest DBU rate. But that also comes with access to Databricks features that reduce operational risks, like fine-grained credential passthrough, strong encryption controls, and automated compliance posture checks.

This is the only tier where long-term cost becomes more about risk avoidance than usage-based math.

Pricing Differences Across Tiers

To give a clearer view of how pricing shifts across tiers, here’s an overview based on the Databricks pricing model:

Strategic Considerations for Tier Selection

If you are unsure which tier to start with, here are a few framing questions:

• Do you need user-level access controls?If yes, Premium or Enterprise is the way to go. Standard doesn't support RBAC.

• Will your platform be handling regulated or sensitive data?Enterprise is built for this, especially when certifications like HIPAA or PCI-DSS are required.

• Are you operating in multi-team or multi-region environments?Premium helps manage cross-team coordination, while Enterprise supports multi-region failover and data locality controls.

• Is cost predictability important right now, or control and security?Standard may help minimize upfront spend, but Premium or Enterprise delivers longer-term efficiency.

Understanding the trade-offs and their impact on DBU rates helps you avoid misaligned spending as your Databricks usage grows.

The Cost Equation Is More Than Just DBUs

So, what exactly are you paying for?

• Platform services, charged in Databricks DBUs

• Cloud infrastructure, charged by your provider (VMs, storage, networking)

• Workspace tier, which determines your feature set and DBU rate

• Workload type, which influences how many DBUs you burn per hour

This pricing model offers flexibility, meaning teams pay for what they use. But without visibility into all the moving parts, it is easy to overspend.

In the next section, we will get into how pricing changes across AWS, Azure, and GCP, and what that means for your budget planning.

Comparing Databricks Pricing Across Cloud Providers: AWS, Azure, and GCP

When it comes to deploying Databricks, your choice of cloud provider can significantly impact your costs. While Databricks offers a unified platform across these providers, differences in billing granularity, DBU rates, and additional infrastructure charges can lead to varying expenses.

Billing Granularity: Per-Second vs. Per-Hour

AWS and GCP bill Databricks usage on a per-second basis, providing fine-grained billing that can lead to cost savings, especially for short-lived or bursty workloads. This means you are charged precisely for the compute time you use.

Azure, on the other hand, bills Databricks usage on a per-hour basis. While this might simplify billing, it can be less cost-effective for workloads that don't utilize full-hour increments, potentially leading to higher costs for intermittent tasks.

DBU Rates Across Cloud Providers

Databricks DBU costs vary not only by cloud provider but also by the type of workload and the chosen plan (Standard, Premium, or Enterprise). Here's a comparative overview:

Note: DBU rates are subject to change and may vary by region. Always refer to the official pricing pages for the most up-to-date information.

Additional Infrastructure Costs

Beyond DBU charges, each cloud provider imposes additional infrastructure costs:

• AWS charges for EC2 instances, EBS volumes, and S3 storage used by Databricks clusters. These costs are billed separately from Databricks charges.

• Azure bills for virtual machines, managed disks, and Azure Blob Storage utilized by Databricks. Similar to AWS, these are separate from Databricks charges.

• GCP charges for Compute Engine instances, persistent disks, and Cloud Storage used in Databricks deployments.

These infrastructure costs can significantly impact your total expenditure, especially for data-intensive workloads. It is essential to consider both DBU and infrastructure charges when estimating your Databricks costs.

Spot Instances and Reserved Pricing

All three cloud providers offer options to reduce costs through:

• Spot Instances (AWS and GCP) / Low-Priority VMs (Azure): These provide access to unused compute capacity at discounted rates, ideal for fault-tolerant and flexible workloads.

• Reserved Instances or Committed Use Discounts: By committing to a certain level of usage over a period (typically 1 or 3 years), you can receive significant discounts on compute resources.

Leveraging these options can lead to substantial savings, particularly for predictable and long-term workloads.

Databricks Cost Calculator: How to Estimate Your Spend

For many teams exploring Databricks, one of the first questions is: How do we estimate what this will cost? Unlike traditional SaaS pricing, Databricks operates on a consumption model where variables like runtime hours, cluster types, and cloud infrastructure directly shape your bill. 

Databricks offers a pricing calculator that lets you model costs in advance.

That said, this tool works best when you understand what inputs matter and where real-world usage can differ from forecasted numbers. 

A Quick Overview of the Databricks Pricing Calculator

Databricks provides a dedicated pricing tool to help estimate costs based on workload size, compute types, and cloud deployment specifics.

Databricks Pricing Calculator

The calculator estimates your Databricks cost by considering:

• Databricks Unit (DBU) consumption by workload type

• Cloud provider and compute instance pricing

• Tier-specific features (Standard, Premium, Enterprise)

• Estimated duration and frequency of jobs

• Cluster size and concurrency

It is structured for transparency but assumes that you already know the architecture of your workload, something that can be challenging in early-stage planning.

What Inputs You’ll Need

To get accurate results from the Databricks pricing calculator, you will need to fill in the following variables:

Compute Type

Select the kind of workload:

• Jobs Compute: For scheduled ETL pipelines

• All-Purpose Compute: For interactive notebook-based sessions

• SQL Compute: For Databricks SQL Warehouses

Different compute types have different Databricks DBU costs. Jobs compute is generally the cheapest per unit, while interactive workloads consume more.

Cloud Provider and Region

Choose between AWS, Azure, or Google Cloud Platform, and select the region your workloads will run in. This impacts:

• Databricks DBU cost (varies slightly across providers)

• Underlying VM and storage pricing

Refer to your organization’s current cloud strategy to align with existing commitments or discounts (e.g., reserved instances, spot pricing).

Instance Type

Choose the virtual machine configuration that will back your clusters:

• General purpose (e.g., m5.xlarge)

• Memory-optimized (r5.2xlarge)

• Compute-optimized or GPU instances for ML workloads

Each instance type affects both cloud costs (via VM pricing) and how quickly a job completes, influencing overall DBU usage.

Cluster Size and Concurrency

Enter how many nodes your cluster will use and whether jobs will run in parallel. More nodes and higher concurrency can improve performance, but increase costs.

Usage Time

Estimate the daily runtime and number of days your job will run in a typical month. Databricks pricing is usage-based, so 4 hours/day over 30 days looks very different from 24/7 pipelines.

Workspace Tier

Select the plan you are using: Standard, Premium, or Enterprise. Each tier has different DBU rates. Premium and Enterprise include additional security and governance features, but cost more per DBU.

Where Estimates Often Go Wrong

While the Databricks calculator gives a solid baseline, it doesn’t account for certain real-world behaviors that can cause budgets to slip. Here are some common missteps:

Ignoring Autoscaling Behavior

Clusters often scale up automatically based on workload demands. If you don’t account for peak scaling, your estimate will be too low.

Leaving Clusters Running

Even idle clusters can burn DBUs and incur cloud compute costs. Make sure to factor in idle time or configure auto-termination policies.

Retrying Failed Jobs

Failed jobs that retry due to schema issues, compute limits, or bad inputs can quietly double or triple DBU usage.

Concurrency Assumptions

If multiple users or jobs run simultaneously, your cluster needs more resources. Overlooking concurrency can understate cloud and platform consumption.

Assuming Reserved Pricing or Discounts

The calculator uses on-demand pricing by default. If your organization has reserved instances or enterprise discounts, you will need to apply those manually to refine the projection.

Best Practices for Forecasting Databricks Costs

To get the most value from the Databricks pricing calculator and improve long-term predictability:

• Start with historical usage patterns. If you are already using Databricks, review platform and cloud billing data for trends.

• Build reusable templates for common workload types (e.g., daily ETL, ad hoc analysis, ML training).

• Use tagging and chargeback models to track cost per team or project.

• Run what-if models with the calculator when considering new workloads or scaling up.

• Review job configurations to avoid inefficient code or redundant processing.

Databricks also provides cost reporting through native tools like the Cost and Usage Dashboard, which can validate or recalibrate your calculator assumptions.

Factors That Drive Up Databricks Costs Unexpectedly

You might assume that Databricks cost scales neatly with workload size. But the reality is more complex. While the pricing model is usage-based, a number of architectural and operational choices can cause unexpected billing spikes, sometimes without any change in user behavior.

Whether you are managing monthly budgets or optimizing resource usage, it helps to know where costs can creep in silently. Below are the most common factors that lead to Databricks billing overruns, many of which are avoidable with the right controls in place.

Cluster Sprawl and Underutilized VMs

One of the most persistent causes of runaway Databricks cost is cluster sprawl: too many clusters running simultaneously, often with overlapping or idle resources.

This usually happens when:

• Multiple teams spin up their own clusters without coordination

• Dev/test environments stay live after office hours or over weekends

• Production clusters are over-provisioned “just in case” and never scaled back

Each cluster consumes cloud infrastructure (VMs, storage) and accumulates DBU charges, whether actively processing data or not. Even a few idle nodes left running across environments can add thousands to monthly bills.

What can help:

• Consolidate workloads into shared job clusters where possible

• Enforce auto-termination policies for idle clusters

• Audit your environment regularly to shut down dormant clusters

Inefficient Queries and Unoptimized Workflows

Poorly written Spark queries or unoptimized jobs can burn through compute cycles unnecessarily. Common culprits include:

• High shuffle operations caused by bad join strategies

• Inefficient aggregations that scan large datasets without filters

• Nested UDFs (user-defined functions) that slow down executors

• Redundant reads of the same source files

These inefficiencies inflate your DBU consumption, especially on interactive or All-Purpose clusters.

Databricks provides tools like the Query Profile and Ganglia metrics to help diagnose performance bottlenecks. But unless these are actively monitored, cost inefficiencies often go unnoticed until the invoice arrives.

Long-Running Interactive Notebooks

Interactive notebooks are great for data exploration but they are also one of the most expensive ways to use Databricks, especially if left open for extended periods.

Every open session keeps the cluster alive, even if no code is being executed. And since All-Purpose clusters have higher DBU rates than Job Compute clusters, the cost accumulates quickly.

Uncontrolled session time is one of the easiest ways costs spiral during exploration. Teams often forget to shut down notebooks or allow them to run indefinitely during lunch breaks, meetings, or even overnight.

Recommendations:

• Use Job clusters for production tasks instead of All-Purpose clusters

• Set idle timeout thresholds for notebook sessions

• Train users to explicitly shut down clusters when finished

High-Frequency Job Scheduling and Automatic Retries

Scheduling a job to run every few minutes might seem harmless until you realize it creates dozens or even hundreds of job instances per day. If these jobs include Spark transformations or large file reads, your Databricks cost will escalate rapidly.

The risk is higher when:

• The job retry policy is aggressive

• Upstream data dependencies are unstable

• Error handling is weak, causing jobs to loop unnecessarily

Retries count as new runs, and they consume DBUs just like the originals. Without guardrails in place, this can become a silent multiplier on your cloud bill.

Tips:

• Review scheduling frequency against actual business need

• Tune retry policies to cap attempts or alert after failure

• Consolidate smaller jobs into batches where possible

Premium Features That Cost Regardless of Usage

Some Databricks capabilities, especially those in Premium and Enterprise tiers, incur costs whether or not you are actively using them.

For example:

• Photon acceleration is billed at a higher DBU rate

• Serverless SQL can include minimum billing thresholds

• Delta Live Tables (DLT) pricing varies by pipeline complexity and tier

• Unity Catalog and Audit Logs require Premium, impacting base DBU rate

Many organizations choose higher tiers to access governance or compliance tools. But if those features aren’t fully rolled out or used across teams, the pricing uplift might not be justifiable, at least in the short term.

Before committing to Premium or Enterprise tiers, evaluate which features you actually need today versus in six months. Tier choice influences every other cost layer, including per-second DBU rates.

Strategic Recommendations to Control Databricks Cost

Databricks offers immense power and flexibility but without the right cost governance practices, it is easy to lose visibility and overspend. Whether you are optimizing for budget predictability or operational efficiency, taking proactive steps to control your Databricks cost can have a major impact on your ROI.

Here are five strategic cost control practices every data team should consider.

Use Job Clusters Instead of All-Purpose Clusters

One of the most impactful changes teams can make is switching from All-Purpose to Job Clusters for scheduled production workloads.

All-Purpose clusters are designed for interactive sessions and collaborative notebooks. They stay up longer, have a higher baseline DBU rate, and are more prone to idle time, especially if users forget to shut them down.

Job Clusters, on the other hand:

• Are created just-in-time to run a scheduled job

• Automatically terminate when the job completes

• Use fewer resources for the same task

• Cost less per DBU

All-Purpose compute typically incurs a higher rate across all workspace tiers. Moving to Job Clusters ensures tighter control and cleaner usage billing, especially for recurring pipelines.

Tune Autoscaling and Limit Idle Time

Autoscaling is a powerful feature in Databricks, but left unchecked, it can also inflate your costs.

By default, clusters may scale up quickly to handle spikes but take longer to scale down, leading to unused capacity. Worse, idle clusters without auto-termination enabled can continue to run and bill, long after a job ends.

To control this:

• Define minimum and maximum worker limits based on workload types

• Monitor scale-up and scale-down patterns using native dashboards

• Set auto-termination policies for all interactive and job clusters (e.g., terminate after 15 minutes of inactivity)

Monitor Usage with Cost Observability Tools

If you are not tracking usage in detail, you can’t manage it effectively. Databricks offers several built-in tools, like Cluster Event Logs, Cost Usage Dashboard, Ganglia Metrics, and Spark UI.

Observability enables accountability. Teams that see how their usage affects cost are more likely to adopt efficient development practices.

Rightsize Clusters and Enforce Tagging

Defaulting to large instance types or over-provisioned clusters is a common mistake. It may “just work” at first, but it is also wasteful.

To rightsize:

• Review past workload performance (CPU, memory usage) to select optimal instance types

• Use smaller, more specialized VM families where applicable

• Reserve high-capacity instances (e.g., GPUs) for jobs that truly require them

Pair this with resource tagging. Enforcing consistent cluster-level tags (team name, workload type, environment) allows for:

• Easier tracking in billing tools

• Internal chargeback models

• Better prioritization when optimizing across projects

The Databricks platform supports cluster tags natively, making this a low-effort but high-return strategy.

Leverage Spot Instances and Runtime Optimizations

For workloads that tolerate interruption, like ETL jobs, data refreshes, or model training, spot instances offer significant discounts compared to on-demand VMs.

All three cloud providers (AWS, Azure, GCP) support spot pricing. You can configure spot policies in Databricks clusters directly, allowing you to blend reliability with savings.

In addition, consider using Photon-enabled runtimes or Delta Live Tables (DLT) where applicable:

• Photon speeds up query execution, reducing total runtime (and thus DBU spend)

• DLT offers simplified orchestration with cost-aware features like built-in monitoring and error handling

These enhancements don’t reduce per-unit price, but they lower the total number of units consumed by making workloads more efficient.

There’s no single switch to cut your Databricks cost overnight. But by combining smart architecture choices, observability, and cultural practices around cost accountability, teams can avoid the most common traps and build a usage strategy that scales with their business goals.

Budgeting Databricks for Long-Term Value

By now, it is clear that managing Databricks cost isn’t just about choosing the right instance type or cluster size. It’s about long-term planning, ongoing visibility, and making technical decisions in tandem with financial accountability.

That’s where Total Cost of Ownership (TCO) comes in and why it should be the central metric in your Databricks budgeting strategy.

Why TCO Matters More Than DBU Price Alone

It is tempting to zero in on DBU pricing when forecasting budgets. And while DBUs are a fundamental component of Databricks pricing, they don’t tell the whole story.

What the DBU model doesn’t capture:

• Cloud infrastructure spend (e.g., VM types, storage, data egress)

• Cluster sprawl and overprovisioning

• Idle time and inefficient workflows

• Premium features enabled but underutilized

• Team-wide productivity losses due to poor cost visibility

In other words, even if your DBU rate is competitive, your actual spend can be high if workloads aren’t optimized or monitored.

Focusing on TCO means considering all these hidden variables and building a system that tracks what’s being spent, where, and why, not just how much a DBU costs on paper.

Build a FinOps-Aligned Review Process

Financial Operations (FinOps) is becoming a core function in modern data organizations. When applied to Databricks, FinOps principles help bridge the gap between engineering and finance by:

• Establishing budget guardrails for teams running jobs and pipelines

• Tagging and attributing usage to departments or business functions

• Reviewing cost vs. value of workloads over time

• Incentivizing efficient design through real-time visibility and accountability

Databricks provides native tools (like usage dashboards and audit logs) that support FinOps practices. But these tools are only as valuable as the cost governance culture behind them.

If your team is scaling quickly or planning to deploy Databricks across multiple business units, now is the time to formalize a review cadence. Quarterly or even monthly cost reviews, combined with defined optimization goals, can help prevent small inefficiencies from turning into long-term waste.

When to Engage a Certified Databricks Consulting Partner

As Databricks adoption grows, so does its complexity. From multi-cloud deployments to scaling AI workloads, many organizations reach a point where internal teams need expert guidance not just for platform implementation, but for cost management at scale.

That’s where Closeloop comes in.

As a Certified Databricks Consulting Partner, Closeloop works with engineering and finance leaders to design cost-conscious architectures, automate observability, and build optimization into every stage of the data lifecycle.

We help teams:

• Select the right cluster strategies for their specific use case

• Configure autoscaling, tagging, and idle shutdown policies

• Leverage cloud-specific discounts and spot pricing without disrupting SLAs

• Build dashboards that give stakeholders real-time cost visibility

• Conduct workload reviews and performance tuning sessions

To see how we can support your Databricks journey, from migration to optimization, explore our Databricks consulting services.

Final Takeaway

Databricks delivers unmatched flexibility, performance, and scalability for modern data workloads. But flexibility comes with responsibility. Without a cost-conscious strategy in place, teams often pay more than they need to, or struggle to justify the spend after the fact.

Focusing on total cost of ownership, enforcing usage governance, and partnering with experts who understand both data engineering and economics can help you get the most from your Databricks investment.

At Closeloop, we partner with growth-focused companies to bring both sides of that equation into balance. If your team is ready to take Databricks from powerful to predictable, we are here to help.

Get more from your Databricks investment. Talk to our certified Databricks consulting experts about architecture, governance, and cost visibility.