Dremio Cloud - Pay As You Go

What do you like best about the product?

The single most helpful thing about Dremio is how it completely changes the game on data movement. Before Dremio, our analytics team spent about 70 percent of their time just moving data around. We had ETL pipelines that took days to build, data copies sitting everywhere, and constant debates about which system should be the source of truth. Dremio just eliminated all of that. You connect it to your data sources, and you query them directly. No copying, no staging, no duplication. It sounds simple, but the impact has been enormous.

The user interface is clean and intuitive. It is web-based, which means no client installations to manage, and the layout is straightforward. The SQL editor is responsive with autocomplete and syntax highlighting, making query writing feel natural. The visual query builder is helpful for users who are not comfortable with SQL, though most of our team prefers writing queries directly. The dashboarding and visualization features are functional but not as polished as dedicated BI tools like Tableau or Power BI. For quick exploratory analysis, they work perfectly, but for polished executive presentations, we still export to other tools. Overall, the user experience is pleasant and focused on getting work done without unnecessary friction.

The integrations are where Dremio truly excels. The platform connects to over 36 different data sources, including all the major databases, data warehouses, and cloud storage platforms. We have it connected to PostgreSQL for transactional data, Snowflake for our warehouse, and S3 for our data lake files. The integration process is straightforward. You add a source, provide credentials, and the system catalogs the metadata automatically. No complex configuration required. The federated query engine means we can join data across these sources in a single query, which was impossible for us before. The platform also integrates well with BI tools like Tableau and Power BI through standard JDBC and ODBC connectors, allowing our analysts to work in the tools they already know.

The performance gains are genuinely impressive. When we first set up Dremio, we ran a test query that used to take about 8 minutes in our legacy environment. Dremio returned it in about 12 seconds. I actually thought it was broken. The Autonomous Reflections feature is magical. It learns what queries we run most frequently and automatically creates optimized data structures in the background. We do not have to think about performance tuning anymore. It just happens. Even with complex joins across multiple data sources, the query engine handles it efficiently. We have seen query times improve by 10x to 100x depending on the complexity of the workload.

The cost savings are another major win. We were paying a fortune for cloud data warehouse storage because we were duplicating everything. We had data in S3, data in Snowflake, data in Redshift, and we were paying for all of it. Dremio let us store everything in open formats on S3 and query it directly. We cut our storage costs by about 60 percent and our compute costs by about 40 percent. The open architecture is a big part of this. We are not locked into any proprietary format. If we decide to move away from Dremio, our data is still there in standard formats like Apache Iceberg. No vendor lock-in, no expensive migration project. The pricing model is consumption-based, so we only pay for what we use, which aligns well with our variable workload patterns. For organizations with significant data volumes, the ROI is substantial and measurable within the first year.

The onboarding experience was remarkably smooth compared to other enterprise platforms we have implemented. Dremio offers a Community Edition that we used to prototype and test before committing to the enterprise version. This allowed us to validate the platform's capabilities without any financial risk. The enterprise version includes formal support, and we have found the support team to be responsive and knowledgeable. Tickets are resolved quickly, and the documentation is thorough and well-organized. The platform is self-managed in our environment, which gives us full control, but Dremio also offers a fully managed cloud option if you prefer not to handle the infrastructure. The open-source community is active and helpful, which is a nice supplement to the formal support channels. Overall, getting started was straightforward and we were productive within weeks rather than months.

The AI Agent is a nice addition. It handles basic natural language questions well. A user can ask "Show me revenue by region for the last quarter" and it will generate the SQL and visualize the results instantly. It saves time on simple queries and helps non-technical users get started. The AI_GENERATE function is particularly interesting. It can extract structured information from unstructured files like PDFs or text documents directly within a SQL query, which opens up new use cases for us. The AI Semantic Layer ensures that both human analysts and AI tools are working with consistent business definitions, which improves the accuracy of AI-generated answers. However, the AI capabilities are not yet at the level where they can replace a skilled analyst for complex analytical questions. For day-to-day exploration and quick insights, it is genuinely helpful, but complex multi-condition analysis still requires human expertise.

Overall, Dremio has made us more agile, more cost-effective, and more data-driven as an organization. The reduction in complexity has been liberating. We are spending less time moving data and more time actually analyzing it. I would recommend it to any organization struggling with data silos, high costs, or slow access to insights. It is not perfect, but for what it does, it is remarkably effective.

What do you dislike about the product?

The most significant downside is that the platform often feels unfinished in production scenarios. It is not that the core functionality is broken, but the polish and robustness you expect from enterprise software isn't always there. Multiple reviewers have described that bugs surface frequently during actual use, which can be frustrating when you are trying to deliver reliable analytics to your business stakeholders.

Integrations are a particular sore spot. The connection with Power BI has been described as problematic by multiple reviewers, with customer support teams apparently having a poor understanding of how this integration works internally. This makes troubleshooting incredibly difficult when something goes wrong. The CI/CD capabilities also feel incomplete. There is an unsupported Python script available to handle development and production environments, but it cannot deal with some native Dremio objects like Row-Level Security policies. This means you might develop a dataset with proper access controls in your development environment, but the script simply will not deploy it to production, forcing you to find workarounds.

Performance at enterprise scale has been a documented weak point. The federation engine struggles with concurrency on large datasets, and standardized performance testing shows that the architecture routinely fails to complete queries when multiple users are running queries simultaneously. For AI use cases, this is more pronounced because an AI agent querying your data may be running dozens of complex federated queries in parallel as part of a single task, and the failure mode compounds quickly.

There are also technical limitations to be aware of. The platform enforces various system limits, such as maximums on autoingest pipes, sources, spaces, and total reflections. Query results returned via the console are limited to one million records, though you can use ODBC or JDBC to see the full result set. The platform also has limits on leaf columns in tables and queries, which can impact organizations working with wide datasets.

Support quality has been inconsistent. Some users have noted that support can be lacking at times, which they attribute to Dremio being a younger company compared to its competitors. The documentation around certain features, like Kubernetes deployment requirements, indicates significant administrative overhead, and managing the Kubernetes cluster requires considerable effort.

The AI capabilities, while promising, are somewhat lagging compared to other platforms. In a world where competitors have already integrated the latest AI techniques, Dremio is still catching up. The AI Semantic Layer, while useful for labeling data, does not enforce business rules. It organizes and labels data without packaging metadata, access rules, or business constraints into an enforceable framework. Different teams and AI agents can still interpret the same underlying data in incompatible ways, and the platform has no mechanism to stop them.

There is also a broader strategic concern. Dremio was recently acquired by SAP, which puts its roadmap in question. Features that Dremio was building independently now have to compete for budget and attention inside a much larger organization. Strategic shifts are already visible, with some cloud editions effectively put on hold while the deal works through approvals. For organizations with strict data governance requirements, the mandatory transmission of operational telemetry data back to Dremio's corporate endpoint introduces a compliance headache that cannot be ignored.

Despite these challenges, I still find Dremio valuable for our use case. The query performance is genuinely impressive, the cost savings are real, and the platform has transformed how we access data. The downsides are real and you need to go in with your eyes open. Budget extra time for troubleshooting integrations, be prepared for some administrative overhead, and have realistic expectations about the AI capabilities. If you have a focused use case like fast SQL acceleration on an Iceberg lakehouse where you own all the data, Dremio's core engine is still remarkably capable. But for teams needing broad federation across many sources, high concurrency, and polished enterprise features, it is worth evaluating carefully.

What problems is the product solving and how is that benefiting you?

Before Dremio, our data landscape was fragmented and inefficient. We had data scattered across multiple databases, cloud storage, and on-premises systems, each with its own access patterns and performance characteristics. Our analytics team spent the majority of their time moving data around rather than actually analyzing it. Dremio has fundamentally changed that.

The most significant business problem Dremio solved for us is eliminating the traditional ETL bottleneck. Instead of building complex pipelines to extract, transform, and load data into a central warehouse, we now query data directly where it lives. This "query, don't move" approach has dramatically accelerated our time to insight. When we need to combine data from our transactional database with our data lake, we simply write a query and Dremio handles the federation. What used to take days of pipeline development now takes minutes of query writing.

We were also struggling with data silos that made it nearly impossible to get a unified view of our business. Different teams had different versions of the truth, leading to endless debates about whose numbers were correct. Dremio connects to all our data sources and presents them through a unified semantic layer, creating a single source of truth for the entire organization. Our finance team can now confidently use the same data as operations, and we no longer waste time reconciling conflicting reports.

The cost savings have been substantial as well. We were paying a premium to store duplicated data across multiple cloud warehouses. Dremio's open architecture, built on Apache Iceberg, allows us to store data once in open formats on object storage and query it efficiently without duplication. We eliminated redundant storage costs and reduced our cloud compute spending because we are not constantly moving and transforming data.

Perhaps most importantly, Dremio has enabled self-service analytics across our organization. Previously, every data request had to go through a centralized IT team that was perpetually backlogged. Business users waited weeks or months for access to the data they needed. Now, our analysts can query data independently using standard SQL, without waiting for engineering support. The platform has democratized access to data, and we have seen adoption grow as more teams realize they can explore data on their own terms.

Dremio has fundamentally shifted our organization from being reactive to proactive with data. We can now answer business questions in hours instead of weeks, spot trends earlier, and make decisions based on current information rather than stale reports. The platform has given us the agility we needed to compete in a data-driven world, and I would not want to go back to our old way of working.

What do you like best about the product?

SQL allows for a lot of flexibility when handling data from various sources.

What do you dislike about the product?

It's threads can sometimes get stuck, which makes the queries unable to run leading to frustration

What problems is the product solving and how is that benefiting you?

Data ETL becomes much easier. It is a great solution at great cost with great interface compared to what else is available on the market. I personally use it to make sure that the filtered data comes in and provides enough transformation to fit the destination database.

What do you like best about the product?

Its just how easy it is to use. When we first onboarded, I was surprised at how fast we could connect to, like, multiple data sources. Didn't have a huge setup headache, which was awesome.The implementation wasn't that bad, especially comparing to some other BI tools we used. I mean, it wasn't 100% smooth, had a few little hiccups, but overall we got it running way easier than I expected.
It's got pretty rich feature set—the reflections and acceleration stuff is cool for performance, even if it feels a bit overwhelming at the start. Integrating it with our existing stuff, like our AWS S3 buckets and Snowflake, was pretty straightforward. No major drama there,Oh, and the SQL editor is way better than I thought it'd be..Overall, it just feels like a tool built for speed and flexibility. we use sometimes multiple times a day when I have to do ad-hoc analysis or explore big datasets Yeah, there's definitely a learning curve, no lie. But once you get past that, you realize how powerful it is.

What do you dislike about the product?

Their customer support is decent. Sometimes they take a bit to get back to you, but most of the time I've gotten a proper solution that actually fixes the problem. The performance is weird sometimes, like one day a query runs blazing fast, and then the next day the exact same query is just... slower. For no obvious reason, The UI also feels a little clunky at times, not gonna lie. Especially when you're trying to handle a really large dataset, it'll just freeze up for a second , laggy . Makes the whole experience feel less smooth than it should.And the documentation... yeah, it could definitely be better. A lot of times I've had to just google around on forums or actually reach out to support just to find some small configuration detail that should really be in the main docs. Wastes a bunch of time.
Also it's not exactly cheap. When you start to really scale it up, especially running on our own cloud infra, the bills start to add up. I feel like for smaller teams, the admin side of things can feel too complex for what you need. Just setting up user permissions and everything is a whole thing.

What problems is the product solving and how is that benefiting you?

So Dremio's basically solved our whole issue with data being scattered everywhere. Before this, we were always having to copy and move data into some central system just to be able to run a query on it. Super time-consuming .We can now just query right on top of where the data lives. Like, directly on S3, or Snowflake, even some of our old legacy databases. We don't need to build these massive ETL pipelines just for a simple question, which is a game changer.It's also helped a ton with speed. Those reflections they have? They make a huge difference on heavy queries. Our reporting team used to have to wait like, hours for their results to come back, and now it's way faster. Saves a ton of times for our day-to-day analysis and helps us make decisions way quicker.

What do you like best about the product?

I love the platform’s ability to connect to a wide array of data sources, including relational databases (PostgreSQL, MySQL, Oracle, MS SQL), NoSQL systems (MongoDB, Elasticsearch), and cloud or file-based storage like S3 and HDFS, without requiring complex ETL pipelines.

This approach simplifies data integration and reduces engineering overhead.

The SQL query engine is highly performant, delivering sub-second response times even on large datasets, and supports live data visualization and dynamic previews during query preparation.

Data reflections feature acts as an intelligent caching layer, optimizing query performance and enabling low-latency dashboard refreshes for BI workloads.

The platform’s virtual datasets allow for complex query logic to be encapsulated and reused, supporting data-as-code principles such as Git-like version control and experimentation.


Cloud-native architecture offers elastic compute scaling and is available as a managed service on AWS and Azure, making it suitable for both on-premises and cloud deployments. It supports role-based access control and multitenancy, which is essential for enterprise environments with strong data governance requirements.

What do you dislike about the product?

The learning curve can be significant, especially when configuring advanced features like data reflections, multitenancy, and integrating with complex enterprise authentication systems.

While the UI is functional, some administrative and monitoring functions feel less intuitive compared to other modern analytics platforms.

I have also found that fine-grained access controls and tenant isolation require careful configuration to avoid inadvertent data exposure in multi-tenant scenarios.

What problems is the product solving and how is that benefiting you?

Dremio has eliminated the need for traditional ETL pipelines in my analytics workflows, allowing direct querying and data exploration across disparate sources without data movement.

This has resulted in faster dataset creation cycles and reduced bottlenecks between data engineering and analytics teams.

The platform’s autonomous performance optimization and use of data reflections have significantly improved query speeds, enabling real-time analytics and interactive BI dashboarding even on large, complex datasets.

By adopting Dremio, I achieved unified access to both structured and semi-structured data in a single platform, which streamlined data governance and cataloging.

The self-service model empowered business analysts to experiment and iterate on data products without constant engineering intervention, accelerating time-to-insight for AI and analytics projects.

The platform’s open, standards-based approach has also made it easier to integrate with existing tools and future-proof my data infrastructure against vendor lock-in concerns.


✅ My overall insight: Dremio has enabled a more agile, scalable, and cost-effective analytics environment, supporting both operational BI and advanced data science initiatives in a unified, governed, and performant manner.

What do you like best about the product?

I like the fact that you can query directly s3 and hdfs and it also support power bi as integration

What do you dislike about the product?

Its not etl friendly so I have to link it with apache ariflow

What problems is the product solving and how is that benefiting you?

The easy integration so i save time