Data Lake Analytics Pricing: Including Alternatives & More

Key Takeaways

• Data Lake Analytics pricing varies based on query volume, storage, and additional services. Costs can range from pay-as-you-go models to enterprise plans, with expenses adding up through hidden fees.
• Many Data Lake solutions require additional tools for visualization, automation, and embedded analytics—leading to higher overall costs beyond the base pricing.
• Qrvey is an all-in-one alternative that eliminates the need for multiple tools. With built-in automation, embedded analytics, and AI-driven insights, Qrvey offers a more cost-effective and scalable solution for SaaS providers and businesses needing seamless analytics integration.
• Choosing the right tool depends on your goals—Data Lake Analytics is ideal for large-scale data processing, while Qrvey provides a fully embedded analytics solution with transparent pricing and lower total cost of ownership.

If you’ve ever tried to compare Data Lake Analytics pricing, you’ve probably run into the same problem: it’s a maze of variables, hidden costs, and technical fine print. One number looks low. Great! Then you start factoring in query fees, egress charges, and all the extra tools you’ll need to make sense of your data…

So, what’s the real cost of Data Lake Analytics? 

And more importantly, is there a better way to get the same (or better) results for less?

In this guide, we’ll break it all down—the pricing models, the hidden costs, and the best alternatives. Everything you need, in fact, to make a confident, informed decision for yourself. 

Let’s get to it. 

Data Lake Analytics Pricing: How Much Does Data Lake Analytics Cost?

At first glance, data lake analytics pricing looks simple—you pay for the storage you use and the queries you run. But once you start moving data, integrating other tools, or running frequent analyses, the costs can spiral fast.

Most providers use a pay-as-you-go model, meaning your costs depend on how much data you process and store. 

That’s great for flexibility but tricky for budgeting. 

Unlike a flat-rate analytics solution, your monthly bill fluctuates based on query volume, data movement, and third-party tool dependencies.

Here’s a breakdown of common pricing factors:

Pricing Component	Description	Estimated Cost
Query execution	Charged per terabyte (TB) of data scanned	$5 – $10 per TB
Data storage	Cost varies based on standard vs. long-term storage	$0.02 – $0.23 per GB/month
Egress fees	Data transfer out of the cloud incurs additional fees	$0.08 – $0.12 per GB
Machine learning & AI features	Optional add-ons for predictive analytics	Varies by provider
BI & visualization tools	Typically requires external software	Additional licensing costs

Many businesses assume data lake analytics is a low-cost solution, but once hidden costs like egress fees, long-term storage, and third-party tools are factored in, the total price can climb quickly—especially as data usage scales.

Qrvey: A Smarter Alternative

If you’re relying on Data Lake Analytics, chances are you’re piecing together multiple tools—one for storage, another for queries, and yet another for visualization. 

This means, more complexity, unpredictable costs, and a lot of time spent managing infrastructure instead of extracting insights.

Qrvey takes a different approach. 

Instead of charging based on query volume or requiring third-party BI tools, Qrvey delivers an all-in-one embedded analytics solution with built-in data visualization, automation, and AI-powered insights.

Why Qrvey?

• No surprise fees – Flat-rate pricing includes queries, storage, and AI capabilities.
• No extra BI tools needed – Built-in dashboards, automation, and self-service analytics.
• Designed for SaaS providers – Multi-tenant architecture built for seamless integration.
• AI-powered analytics included – Native integration with AWS AI/ML without additional cost.

Here’s how Qrvey stacks up against Data Lake Analytics:

Feature	Qrvey	Data Lake Analytics
Pricing model	Flat-rate	Pay-as-you-go
Query execution costs	Included	Charged per TB
Data storage	Included	Additional cost per GB
Egress fees	None	Charged per GB
Built-in data visualization	Yes	Requires third-party tools
AI/ML features	Included	Additional cost

By consolidating analytics, data management, and automation into a single platform with transparent pricing, Qrvey addresses the hidden costs and integration challenges often associated with traditional Data Lake Analytics solutions.

Get your demo today. 

Data Lake Analytics Hidden Costs

At first, data lake analytics pricing looks reasonable—you only pay for what you use. But once you start running real workloads, the hidden costs start adding up. Here’s what often gets overlooked:

• Egress fees. Moving data out of cloud storage? That’s extra. Many providers charge $0.08 – $0.12 per GB, which can quickly balloon for businesses processing large datasets.
• Long-term storage costs. Data lakes store raw data indefinitely, but archived storage isn’t free. Cold storage costs may seem low per GB, but retrieval fees can be unpredictable.
• Query execution charges. You’re billed based on data scanned, not just data used. Large queries on unoptimized datasets can burn through your budget fast.
• BI and visualization costs. Many businesses assume analytics tools are included—but they’re not. You’ll likely need third-party BI software, adding licensing and integration costs.
• Data processing and AI add-ons. Want machine learning? Many platforms charge extra for AI-powered analytics, turning what seems like an affordable tool into an expensive ecosystem.

The biggest challenge? You won’t always see these costs upfront—they accumulate over time, making budgeting unpredictable. 

Data Lake Analytics Tool Pros & Cons

Like any technology, data lake analytics has its strengths and weaknesses. Here’s a quick breakdown of what it does well—and where it falls short.

Pros

• Scalable architecture. Can handle massive datasets, making it ideal for enterprises with high-volume data needs.
• Cost-efficient model. You only pay for the queries you run, which can be economical for intermittent usage.
• Flexible data formats. Supports structured, semi-structured, and unstructured data, allowing for diverse data ingestion.
• AI and ML support. Many platforms integrate with machine learning services for advanced analytics and predictive modeling.

Cons

• Complex learning curve. Requires technical expertise to manage, optimize, and query data efficiently.
• Hidden costs. Storage, egress fees, and query execution charges can significantly increase total costs.
• Limited visualization tools. Often requires third-party BI tools to extract insights, adding to the overall expense.

For businesses looking for a simpler, all-in-one analytics solution, Qrvey provides embedded analytics, automation, and AI features without the complexity of traditional data lakes. Check out our full range of solutions. 

Who is Data Lake Analytics Best For?

Data lake analytics platforms aren’t one-size-fits-all. They work best for businesses that need highly scalable data processing and have the resources to manage complex infrastructure. Here’s a breakdown of who benefits most from data lakes:

Data engineers – ideal for large-scale data processing with custom queries

Data lakes provide flexibility for engineers to ingest, transform, and analyze massive datasets. However, they require technical expertise to optimize queries and control costs.

SaaS providers – for applications needing built-in analytics

SaaS companies that need to embed analytics into their products often turn to data lakes for backend data management. However, they still require third-party BI tools for visualization and reporting.

BI analysts – for running queries and generating reports from massive datasets

Analysts benefit from the ability to query diverse data formats, but they often need additional tools for interactive dashboards and self-service analytics.

Data Lake Analytics Tool Customer Reviews

We analyzed user reviews for AWS Lake Formation and Google BigQuery, two of the leading data lake solutions. Below is a breakdown of the most common praises and complaints from real users.

AWS Lake Formation

Positives

User-friendly setup – Many users find the initial setup process straightforward, especially when working within the AWS ecosystem.

“UI controlled setup makes it easy for the developers to make datalake up and running without the need of managing various infrastructure.” – Bimarsh P, verified G2 user

Complaints

Versatility limitations – Some users report that Lake Formation lacks flexibility when handling diverse data types, particularly rich media content.

“One of the main challenges, especially when dealing with rich media content, like in MarTech (Marketing Technology) or ad agencies, is its versatility. Some clients feel that Lake Formation doesn’t meet their needs and they tend to prefer competitor products for those specific use cases.” – Ramesh R, Peerspot review

Google BigQuery

Positives

Seamless integration – BigQuery works well with other Google Cloud services, making it a strong choice for companies already using Google’s ecosystem.

“It also works well with other Google Cloud tools.”
— Ramraja Y., verified G2 review

Complaints

Cost unpredictability – The pay-as-you-go model can lead to unexpected expenses, particularly for smaller teams or companies with frequent queries.

“The cost structure becomes expensive for complex queries. Also SQL interface has a learning curve for beginners.” – Shubham B, verified G2 user. 

Alternative to Data Lake Analytics: Qrvey

Traditional data lakes require heavy setup, ongoing maintenance, and multiple third-party tools for analytics, automation, and visualization. If you’re looking for faster deployment, cost control, and a fully embedded analytics experience, Qrvey offers a powerful alternative.

🔹 Faster time to insights – No need for months of setup, schema design, or engineering resources. Qrvey is analytics-ready from day one.

🔹 Flat-rate pricing – Eliminate unpredictable query costs, storage fees, and egress charges with a predictable pricing model.

🔹 No external BI tools required – Unlike data lakes that require third-party visualization software, Qrvey provides built-in dashboards, reporting, and self-service analytics.

🔹 Automated data preparation – Data lakes store raw data, but Qrvey automatically ingests, structures, and transforms it into analytics-ready formats.

🔹 Integrated workflows & automation – Go beyond insights with built-in alerts, automation, and decision workflows—without needing additional tools.

Key Features of Qrvey

Embedded Data Visualization

Qrvey provides interactive dashboards, self-service analytics, and reporting built directly into your SaaS platform or application. 

Unlike data lakes, which require additional BI tools for visualization, Qrvey delivers everything in one platform—reducing costs and technical complexity.

Automated Data Preparation & Transformation

Data lakes store massive amounts of raw data, but making that data useful requires complex ETL (extract, transform, load) processes. Qrvey automates data ingestion and transformation, turning raw information into analytics-ready insights without additional engineering effort.

Key Feature Comparison

Feature	Qrvey	Traditional Data Lake Analytics
Deployment Time	Instant	Requires weeks/months of setup
Pricing Model	Flat-rate	Pay-as-you-go
Query Execution Fees	None	Charged per TB
Data Preparation & Transformation	Automated	Requires manual ETL
Built-in Dashboards & BI Tools	Yes	Requires third-party software
Workflow Automation & Alerts	Yes	Requires additional tooling
Multi-Tenant Support	Yes	Not standard

For businesses that want an embedded, cost-predictable analytics platform without the complexity of managing a data lake, Qrvey provides a faster, simpler, and more scalable alternative.

Does Qrvey’s Pricing Fit Your Budget?

Most businesses don’t realize how quickly data lake costs spiral out of control. Storage fees, query execution costs, egress charges—it all adds up. By the time you optimize your workflows and plug in a separate BI tool, your “affordable” data lake might be costing you more than an all-in-one solution.

Qrvey takes a different approach. 

One platform, flat-rate pricing, and no hidden fees—just seamless embedded analytics designed for SaaS providers who need to scale without unpredictable costs.

So, the real question isn’t “Can I afford Qrvey?” It’s “How much am I actually spending on my data lake, and is it really worth it?”

If you’re ready to simplify your analytics, Qrvey is ready for you.Explore your options—book your demo today.