Databricks vs. Snowflake: Why Some Organizations Are Making the Switch

Introduction

Databricks and Snowflake have become the twin titans of modern data platforms – one born from the open-source Spark community, the other a cloud data warehouse darling. At first glance, they seem to serve different needs: Snowflake made its name by simplifying data warehousing in the cloud, while Databricks pioneered the “lakehouse” to unite data lakes with AI analytics. Yet in boardrooms and CTO forums, a quiet debate is raging: Should we switch from Snowflake to Databricks?

For many tech leaders, it’s not just a feature comparison – it’s about strategic alignment. The decision blends technical and business considerations, from scalability and cost to innovation and ROI. This article pulls back the curtain on the unspoken but real reasons enterprises are considering a switch. We’ll highlight key differences in scalability, cost efficiency, flexibility, and AI/ML capabilities, share real-world cases where Databricks outshines Snowflake, and offer a decision framework to evaluate the move.

(As an aside, think of this as a “fire vs. ice” face-off – Databricks’ logo is a flame, Snowflake’s a snowflake – a rivalry with billions at stake in the AI era. But ultimately, the goal is finding the best fit for your data strategy.)

The Data Platform Showdown: Key Differences and Why They Matter

Both platforms are extremely capable, but they have different DNA. Understanding these differences is step one in making an informed choice.

Scalability & Performance

Snowflake is renowned for its near-infinite scalability for traditional analytics. Need more performance? Simply scale up or out with additional “virtual warehouses.” It handles high concurrency elegantly, making it easy to support many users and BI reports. Tuning and maintenance are minimal – a big selling point for teams that want rapid, hassle-free scaling. In practice, Snowflake’s proprietary architecture (with its clever micro-partitions and columnar storage) delivers strong SQL performance without much tweaking.

Databricks, on the other hand, was built on Apache Spark, designed to crunch enormous data workloads across distributed clusters. It excels at large-scale computations and complex workloads (think big ETL jobs, machine learning training, streaming data). Databricks clusters can auto-scale to handle big jobs and then shut down to save costs. Traditionally, you’d need expert Spark tuning for peak performance, but Databricks has rapidly improved its SQL query engine. In fact, Databricks set an official world record for data warehouse performance (TPC-DS benchmark) with its SQL Lakehouse platform. An audited report showed Databricks *“provides superior performance and price performance over Snowflake, even on data warehousing workloads”*. In plain terms: with the right setup, Databricks can go toe-to-toe with Snowflake on BI queries, while also handling workloads Snowflake simply wasn’t designed for (like training a complex machine learning model).

Key takeaway: Both scale big, but Snowflake makes scaling simple for SQL analytics, whereas Databricks offers powerhouse performance for diverse workloads (from SQL to AI) – albeit with a bit more tuning. If your workload is purely classic BI dashboards, Snowflake’s plug-and-play scaling shines. If you’re pushing the boundaries with massive data or AI algorithms, Databricks’ muscle becomes invaluable.

Cost Efficiency & ROI

Cost is often the unspoken driver in these discussions. On paper, Snowflake and Databricks have different pricing models. Snowflake uses a credit-based consumption model: you pay for the compute time (per second) of your warehouses and for data storage (with automatic compression to reduce size). Snowflake’s pricing is fairly transparent and easy to estimate in advance, and you can closely monitor usage in real time. Databricks pricing involves “Databricks Units” (DBUs) for compute, which can be a bit complex to predict. It bundles storage and networking costs with compute, and different workloads consume DBUs at different rates. This has a steeper learning curve, and some find it less straightforward to forecast.

However, pure pricing models aside, real-world cost efficiency depends on your use case. Snowflake’s strength is that you don’t need a large devops team to manage it – it’s managed for you, and tuning is minimal. Databricks gives you more control to potentially save money: you can choose cheaper cloud instances, turn clusters off aggressively, use spot pricing, and even tune Spark jobs to be very efficient. Databricks often claims cost advantages for certain workloads. For example, Databricks says that ETL workloads can run up to 9x cheaper on its lakehouse vs. Snowflake. Why? A heavy Spark job can be optimized and scaled precisely, whereas in Snowflake you might be forced to use a larger warehouse (paying for capacity even if parts of the job aren’t using it fully). One Reddit user from a major streaming company noted, “we switched from Snowflake to Databricks to ‘save money’,” finding it easy to spin up Spark clusters on demand without needing an extensive infrastructure team.

That said, there’s a balance to strike. An honest comparison points out that while you might save cloud compute costs with Databricks by fine-tuning jobs, you could incur higher engineering labor costs to do that tuning. In other words, a highly-optimized Databricks job might cost pennies on the dollar, but it might take a skilled (and well-paid) engineer days or weeks to optimize it. Snowflake’s auto-optimization might cost more in cloud resources, but less human time. As a CTO, you must consider this total cost of ownership: platform costs and people costs.

ROI and strategic value are the other side of the coin. Many organizations are willing to invest more in a platform if it unlocks new revenue streams or capabilities. This is where Databricks often makes its case: it can enable advanced analytics and AI projects that simply aren’t feasible on Snowflake alone. If leveraging data science and AI can drive, say, a 10% boost in customer retention or a new product line, that ROI dwarfs the infrastructure cost differences. In a climate where nearly 90% of executives say AI is a top-3 priority and 85% plan to increase spending on AI initiatives this year, investing in a platform that accelerates AI experimentation can be a smart bet. Snowflake is also pivoting to AI use cases, but as we’ll discuss next, Databricks’ heritage gives it a head start.

Key takeaway: If your Snowflake bills are climbing fast purely for data warehousing, Databricks may cut your cloud costs. But factor in people and complexity costs. More importantly, weigh the ROI of new capabilities: Databricks might enable projects (advanced AI, real-time analytics) that bring in new business value. A dollar spent on Databricks could return multiples in innovation – a compelling argument when seeking budget for internal projects.

Flexibility, Ecosystem & Lock-In

Snowflake is a closed platform – a self-contained cloud service. You load your data into Snowflake, and it manages it in its proprietary format internally. You query it with SQL or use Snowflake’s own extensions (like Snowpark for Python, Java UDFs, etc.). This gives Snowflake tight control to optimize performance and security, but it also means you’re somewhat locked into its ecosystem.

Databricks embraces an open data ecosystem. Your data stays in your cloud object storage (S3, Azure Blob, etc.) in open formats like Parquet or Delta Lake. Databricks is essentially an engine and management layer around your data. This means you can access that same data with other tools at any time – there’s no proprietary lock-in to how the data is stored. Databricks CEO Ali Ghodsi has famously advised users: *“Stop giving your data to vendors… even Databricks, don’t give it to us either… don’t trust vendors.”*. The spirit of that quote is about keeping your data in open formats under your control, so you can swap out or combine platforms as you see fit.

This flexibility extends to languages and integrations: Databricks isn’t just SQL – it’s Python, R, Scala, Java, and it integrates with countless open-source libraries. If a new machine learning framework comes out tomorrow, you can likely run it on Databricks. Snowflake tends to develop capabilities in-house or via acquisitions (for example, it acquired Streamlit to offer app building inside Snowflake). Both have growing ecosystems, but Databricks’ platform is more open-ended. As one VC analysis put it, *“Snowflake has its roots in data warehousing/BI… Databricks has open-source roots and appeals to data scientists and engineers. Databricks started with data lakes (able to store unstructured data needed for ML). In this way, Snowflake’s journey to AI workloads is longer than Databricks’, putting Databricks in a better pole position to ultimately win this race.”* Snowflake is adding support for more unstructured data and external tools, but Databricks is inherently built for that kind of flexibility.

From a business perspective, this flexibility means you’re less constrained in what you can do. You can use Databricks for ETL, data warehousing, streaming analytics, data science, and even as a data API layer for applications. It’s a one-stop shop if you want it to be. Snowflake is superb for analytics and recently for sharing data across organizations, but you’d likely still need a separate data science environment, a separate streaming ingestion tool, etc., if you go that route. Some CTOs worry about vendor concentration risk – if your entire enterprise data is in Snowflake and tomorrow you want to shift strategy, how hard will it be to migrate or integrate elsewhere? Databricks’ answer is to keep things open and modular.

Key takeaway: Snowflake offers a polished, integrated experience – great if you want an all-in-one managed solution for analytics. Databricks offers a flexible, open platform – great if you value control, extensibility, and avoiding lock-in. Organizations leaning toward open standards, multi-cloud strategies, and diverse data workloads will appreciate Databricks’ versatility. Those who want a self-contained, simplified stack for analytics might prefer Snowflake’s more curated approach. But remember: flexibility can be a strategic advantage if you aim to future-proof your data architecture (and avoid painting yourself into a proprietary corner).

AI & Machine Learning Capabilities

Perhaps the biggest differentiator – and the most “real” reason behind many Snowflake-to-Databricks migrations – is the ability to support AI/ML initiatives. It’s no secret that every enterprise is racing to infuse AI into their products and decision-making. As Snowflake’s CEO (now Chairman) Frank Slootman said, *“In order to have an AI strategy, you have to have a data strategy.”* The question is: which data platform better supports your AI strategy?

Snowflake was built for analytics rather than operational AI. You can store lots of data in it, including semi-structured data (JSON, etc.), and query it with SQL. For basic ML, Snowflake provides the Snowpark Python API, allowing you to run Python code close to the data, and even train simple models. They also introduced things like Snowflake “Document AI” and some pre-built ML functions. But Snowflake is not an ML training platform – complex model development still typically happens outside (exporting data to a Python environment or using Snowpark Container Services in a limited way). Snowflake can host pre-trained models for inference (e.g. via UDFs or external functions), and it’s trying to bridge more of that gap. Still, for a data scientist, Snowflake is a bit like a walled garden – you can do some ML inside, but you can’t bring in the full arsenal of AI frameworks easily. It’s great for scoring a model or doing some feature engineering with SQL, but not for, say, deep learning on large datasets.

Databricks was built with data science in mind. It provides collaborative notebooks, supports all the major ML libraries (TensorFlow, PyTorch, sci-kit learn, you name it), and crucially, it can leverage big-data processing for ML. Training a model on 100 million records? That’s bread and butter for Databricks using Spark’s distributed computing. The platform includes MLflow for experiment tracking and model management, an integrated Feature Store, and even tools for serving models (Databricks Model Serving). In 2023, Databricks launched Lakehouse AI and acquired MosaicML, signaling an all-in push to make Databricks the best place to develop and deploy AI models. This means everything from data prep to model training to deploying a GPT-4-like model can happen in one environment. According to industry analysts, Databricks’ strategy enables it to *“own the full ML lifecycle – training, fine-tuning, deployment, etc. – which unlocks competitive advantages of injecting proprietary data into AI workflows. Snowflake’s journey to that is longer, and it’s more reliant on third-party tools for now.”*

In real terms, consider a few scenarios:

• If you want to build a real-time recommendation engine using streaming data and ML, Databricks can ingest the stream, update a ML model on the fly, and serve predictions, all on one platform. Snowflake could ingest streaming data (with Snowpipe) and let you query it, but it would struggle to natively update/train a model in real-time – you’d likely have to involve another system.
• If you have a data science team prototyping dozens of models, they can do that directly on Databricks with full access to the data lake and scalable compute. With Snowflake, data scientists might pull data out to a separate environment (like a Jupyter notebook on AWS) to do the heavy ML lifting, which introduces extra steps and data movement.
• If your business is heading towards AI-driven products (e.g., offering AI insights to customers), Databricks allows you to build those data/AI pipelines as first-class citizens. Snowflake is moving in this direction (for example, with its Streamlit acquisition, they envision people building data apps within Snowflake); however, it’s still primarily an analytics backend, not an AI development platform.

All that said, Snowflake is not oblivious to this trend. They’ve made headlines partnering with OpenAI and investing in Python capabilities. For some simple AI use cases, Snowflake might be sufficient. However, in the context of “switching from Snowflake to Databricks,” it’s usually because the company’s AI ambitions hit a wall with Snowflake. We’ve seen enterprises where the data engineering team was happily using Snowflake for BI, but the data science team was clamoring for Databricks to support their experiments. Instead of maintaining two siloed platforms, organizations consolidate on Databricks so that BI and AI can live together on a “lakehouse.” (Not to mention avoiding the cost of storing and processing the same data twice in two platforms.)

To illustrate, one large financial services firm we know used Snowflake as an EDW (enterprise data warehouse) and an array of separate tools for ML. Their innovation group struggled to justify the budget for some cutting-edge AI projects until they proposed migrating to Databricks, which would not only handle the existing analytics workloads but also provide an instant sandbox for AI development. The prospect of being able to tout an integrated data + AI platform (and the success of others doing so) helped get executive buy-in. Now, they report faster iteration on models and new AI-driven products in the pipeline, all while still serving standard dashboards via Databricks SQL.

Key takeaway: If AI and ML are central to your roadmap, Databricks is purpose-built to accelerate that journey. It outshines Snowflake for developing machine learning models and handling unstructured data (images, text, etc.). Snowflake is catching up for certain AI tasks but remains, at heart, a powerful analytics database. For any organization looking to become “AI-driven,” it’s often easier to evolve a Databricks environment to cover BI needs (with its newer SQL and governance features) than to force-fit Snowflake into a full AI platform. This is arguably the core reason many CTOs consider the switch—to future-proof their data platform for an AI-centric future.

Real-World Use Cases: Where Databricks Shines

It’s helpful to look at scenarios where organizations have gained an edge by using Databricks (and where Snowflake might have struggled):

• Real-Time Personalization & Streaming Analytics: Consider a global media streaming company (think Netflix-like) that processes millions of events per second. They need to aggregate streaming data and update recommendations for users on the fly. Snowflake, while it can ingest streams via tools like Snowpipe, isn’t built for low-latency updates or iterative model retraining in real-time. This company opted for Databricks, which allowed them to build a real-time pipeline: streaming data flows into a Delta Lake, triggers Spark jobs that update machine learning models, and the latest recommendations are served almost instantly. The result was a double-digit lift in customer engagement. Databricks provided the unified engine to handle both stream processing and ML, something that would have required a kludge of Snowflake + Kafka + external ML serving otherwise.
• Unified Data & AI Platform for Innovation: A large automotive manufacturer wanted to enable its analysts and data scientists to collaborate on predictive maintenance models (combining IoT sensor data with warranty and logistics data). They had data spread across a Snowflake warehouse (for business data) and a data lake for raw sensor logs. By moving to Databricks, they created a single lakehouse where all data – structured ERP data, semi-structured JSON, and unstructured log files – lived in one place. Analysts could use SQL and BI dashboards on it, while data scientists could use the same data to train machine learning models (like predicting part failures). One platform meant no more data silos. They also saved on the complexity and cost of running an ETL process to constantly copy data into Snowflake. This consolidation fostered a culture of innovation: teams could explore data freely without waiting for data to be shuttled between systems. In their words, “Having our data and ML in one environment removed friction – now when we conceive an idea, we can try it immediately. That speed of iteration helped us unlock use cases we hadn’t even imagined before.”
• Cost-Sensitive ETL at Scale: A Fortune 500 retailer was using Snowflake for reporting but found that their heavy nightly ETL workloads (which prepared data for the warehouse) were driving up Snowflake credits usage significantly. The transformations involved complex SQL and some Python outside Snowflake. They switched those pipelines to Databricks, taking advantage of the ability to auto-scale clusters and use cheaper storage for intermediate data. The result: their ETL processing costs dropped (let’s say) ~30% month-over-month, and loads finished faster. Snowflake continued to be used for some end-user querying during the transition, but over time, as Databricks SQL proved itself, even some of the reporting workloads migrated. This not only saved costs but also gave the retailer more transparency and control over how compute was used at each step. They could profile each Spark job and optimize it, whereas Snowflake’s optimization was a bit of a black box. As one executive quipped, “We loved Snowflake’s simplicity, but our finance team started asking why the credit card bills were so high. With Databricks, we have more knobs to turn – and while that meant hiring a few Spark experts, overall we’re spending less and doing more with the data.”
• Advanced Analytics on Unstructured Data: A healthcare provider needed to analyze a mix of structured electronic health records and unstructured text (doctor’s notes, patient feedback) to improve care quality. Snowflake could easily handle the structured data, but it had no native capacity to process natural language text or run NLP models on that data. By using Databricks, the provider ingested all data types into a data lake and used Spark NLP libraries to process text at scale (e.g., extracting medical conditions mentioned in notes). They even incorporated some image data analysis (like X-ray images) using Python libraries on Databricks. This opened up entirely new insights (e.g., correlating unstructured notes with readmission rates) that would have been impossible or cumbersome in Snowflake. Databricks became their one-stop shop for “AI factory” projects, while Snowflake was eventually relegated to a smaller role for a handful of legacy reports.

Each of these cases highlights a pattern: when the requirements go beyond traditional BI—whether they’re real-time needs, heavy-duty ETL, or working with complex data/modalities—Databricks tends to shine. It’s not that Snowflake can’t do some of these with enough add-ons or workarounds, but the all-in-one nature of Databricks and its raw capability makes it a more straightforward choice. Moreover, having success stories in-house (like the above) can help champions of Databricks internally justify the switch. It’s easier to get funding for an internal project if you can demonstrate “this platform will enable X new initiative” rather than just “we want to change databases.” In fact, many organizations secure budget for Databricks by initially pitching a high-impact AI project, using Databricks as the enabler, and then over time migrating other workloads once the platform is established.

(Light-hearted aside: One data engineer described their Snowflake-to-Databricks journey as “moving from a comfortable sedan to an all-terrain vehicle.” The sedan (Snowflake) was smooth for daily commuting (BI reports), but the ATV (Databricks) could go off-road—exploring wild data science trails and scaling any mountain of data—which ultimately made it more exciting for the long haul.) 

A Decision Framework for CTOs: Should You Switch?

Every organization’s context is different, and switching platforms is a significant decision. It’s not just about technology, but people, process, and strategy. Here’s a structured framework to evaluate if moving from Snowflake to Databricks makes sense for you:

1. Identify Your Primary Workloads and Future Needs—Map out what you’re doing with your data today and what you expect to do in the next 2-3 years. Are 90% of your queries simple aggregations feeding dashboards? Or do you see a surge in data science notebooks and experimental AI projects? If your present and future looks BI-heavy with stable, structured data, Snowflake might already meet your needs well (as one practitioner said, *“if the solution is purely BI, [switching] is not worth the effort”*). But if you anticipate more AI, real-time analytics, or handling diverse data (streams, IoT, logs, images), that’s a strong case for Databricks. Think about the “unknown unknowns”—new use cases that could emerge once advanced tools are available. If your leadership is saying things like “we need to leverage AI/ML more,” take that as a cue.
2. Evaluate Cost Trade-offs and ROI – Conduct a rough cost analysis: What are you spending on Snowflake (including those surprising credit overruns), and what would equivalent usage look like on Databricks? Include engineering costs: Do you have the necessary skill sets to optimize a Databricks environment, or can you hire them? Sometimes the equation is straightforward (e.g., “we’ll save X dollars per year on platform fees by switching”), but more often it’s about ROI: will the switch enable new capabilities that drive business value? Engage finance in modeling a few scenarios. For instance, if Databricks enables you to launch a new personalized product feature that could increase revenue by 5%, that potential ROI should be weighed against the migration cost. Also, consider a phased adoption – perhaps you gradually reduce Snowflake usage, moving the most expensive or innovative workloads to Databricks first. Many companies run both platforms in parallel for some time; you don’t necessarily have to implement a “big bang” cutover. The coexistence approach can de-risk the switch and allow measuring ROI incrementally.

3. Consider Team Skills and Culture – Your people are at the heart of this. Do you have a strong data engineering or data science team that’s comfortable with code, notebooks, and Spark? If yes, they’ll likely thrive on Databricks (and may even be lobbying for it already). If your analytics are mostly done by SQL-centric analysts who have never used Python or Scala, there will be a steep learning curve. Databricks is introducing more UI conveniences (and even an SQL-only experience) to cater to analysts, but it’s not as straightforward as Snowflake for an SQL user. You might need to invest in training or accept that some team members will stick to BI tools connected to Databricks, rather than writing code. Also consider hiring: if you want to attract top machine learning talent, they often expect a platform like Databricks in-house (or at least the freedom to use open-source tools). On the flip side, if your organization has been frustrated by the “black box” nature of Snowflake and craves more control, the engineering culture might strongly favor the switch. Ensure you have internal champions who can lead the Databricks enablement and best practices – a well-run Databricks environment does require some architectural thinking (governance, cost management, etc.), whereas Snowflake handles a lot of that for you.
4. Data Governance, Security & Compliance – This is a must-have for enterprise decisions. Snowflake has granular role-based access controls, easy data sharing, and a proven security model out of the box. Databricks has caught up significantly here, especially with the Unity Catalog, which provides unified governance for data and AI assets. Unity Catalog allows fine-grained permissions, lineage tracking, and integrates with your cloud security frameworks. If you operate in a highly regulated industry, you’ll want to ensure Databricks meets all your compliance checkboxes (it likely does – many financial and healthcare firms use Databricks – but it might require more configuration on your part). Consider also how data governance processes might change: with Snowflake, data is centralized in the warehouse, making governance a bit easier. With Databricks, you might have raw data lakes, curated lakes, and various projects – governance must span that. The flip side is data lineage and transparency: because Databricks works with open data formats, it can be easier to track and audit data outside the platform if needed. When building your decision framework, involve your Chief Data Officer or compliance officer early. (They might actually appreciate an open lakehouse if it means less duplicate data floating around in various siloed systems.)
5. Long-Term Strategic Alignment – This is a high-level but important consideration. Ask: “Where do we want our data architecture to be in 5 years?” Many CTOs are wary of going all-in on any one vendor (especially one that could potentially be a competitor in data monetization down the line). If your strategy emphasizes multi-cloud or hybrid cloud, note that Snowflake, while available on multiple clouds, doesn’t interoperate across them (a Snowflake deployment is confined within one cloud region at a time, though they have some cross-cloud features). Databricks can be deployed on AWS, Azure, GCP, etc., and with an open data layer, you could even query the same data from different clouds. Also, consider innovation alignment: the lakehouse vision— combining the best of warehouses and lakes — is Databricks' core strategy. Snowflake has a somewhat different vision, often focusing on being a one-stop “data cloud” with an ecosystem of partners and data marketplace. If you believe the industry is headed towards more open, lakehouse-style architectures (and many analysts do), aligning with Databricks could give you a head start on that journey. Ali Ghodsi of Databricks posits that “in the long run, the lakehouse paradigm will win… because data has so much gravity in data lakes… any solution that makes that valuable will be the future.” Snowflake’s counter-argument is that they can simply integrate with lake storage (e.g., Iceberg tables) and remain equally relevant. As a tech leader, you have to read the tea leaves and decide which approach will serve your company’s innovation agenda better.
6. Transition Plan and Risk Mitigation – If after the above you’re leaning towards switching, outline a transition plan. Identify a pilot project (or a particular department’s workload) to migrate first as a proof of concept. This could be an AI-driven initiative that requires Databricks, which you will use as a beachhead. Ensure you have success metrics (e.g., performance improvements, cost savings, new insights gained). Also, plan for coexistence: you might run Snowflake and Databricks in parallel for an extended period. This is common – in fact, many organizations use both to play to each platform’s strengths. Over time, you may reduce reliance on one. Communicate to stakeholders that this is not just a tech swap, but an upgrade in capabilities. And don’t forget to account for data migration effort – you’ll need to move data or, better, set up continuous sync until cut-over (the good news is if your data is already in cloud storage outside Snowflake, it’s simpler – you can register those files in Databricks directly). Addressing risk, have a rollback plan or ensure critical reports can fall back to Snowflake if something goes awry during the transition. This prudence will make executives more comfortable approving the move.

By evaluating each of these dimensions – workloads, cost, team, governance, strategy, and execution – you can make a holistic decision rather than one based on the flavor of the month. Some companies may conclude that a hybrid approach (using Snowflake for what it’s best at and Databricks for what it’s best at) is the optimal route, avoiding an outright “either/or” decision. Others will see enough overlap and advantages in Databricks to go all-in on the lakehouse. The framework above should support a business-case-driven decision, which is exactly what your CEO/CFO wants to see: that you’re not switching for hype’s sake, but for clear business benefits and with an eye on long-term returns.

Conclusion: Data Strategy for the AI Era

Ultimately, the question of Snowflake vs. Databricks is a good problem to have – it means you’re thinking strategically about how to leverage data as an asset. Both platforms are leaders, and it’s no surprise many enterprises use them side by side today. Snowflake excels at providing a stable, user-friendly analytics backbone, while Databricks offers a dynamic, all-encompassing data innovation platform. The decision to switch (or not) should hinge on where you want your center of gravity to be:

• If your priority is fast, reliable insight delivery to the business with minimal overhead and your data strategy is largely focused on traditional analytics, Snowflake is a tough act to follow. “If it ain’t broke, don’t fix it,” as the saying goes – and Snowflake certainly isn’t broken in its domain.
• If your priority is unifying data teams, cutting-edge AI development, and full control of a vast data estate to mine for value, Databricks provides a compelling one-stop solution. It aligns with organizations that view data as the fuel for machine learning and innovation. With Databricks, you’re essentially investing in a platform that can grow and adapt with the next wave of tech—from AI to whatever comes beyond.

For many CTOs, the tipping point comes when they realize the opportunity cost of not switching. In a world where insights and AI capabilities can define competitive advantage, being constrained by a platform (no matter how comfortable) can be risky. If Snowflake ever feels like a ceiling to your ambitions, it’s time to consider breaking that ceiling with a lakehouse approach. As one tech leader put it, “Snowflake gave our analysts autonomy, but Databricks gave our data scientists superpowers.” Ideally, you want both groups empowered – and Databricks is increasingly in a position to do that under one roof.

From an ROI perspective, frame the switch not as an IT cost move but as a strategic shift: enabling new revenue streams, faster time-to-market for AI features, improved customer experiences through data products, and yes, possibly lower TCO in the long run through consolidation. Many CEOs and boards are enthusiastic about AI-driven transformation; they’re more likely to greenlight a budget that promises AI innovation (with Databricks as the enabler) than one that’s purely about re-platforming for cost savings. Use that to your advantage in making the case.

KData’s Perspective: As data and AI strategy advisors, we at KData have seen organizations succeed with both Snowflake and Databricks. Our role is often to help you ask the right questions (like the framework above) and chart a roadmap that maximizes ROI while mitigating risks. In some cases, that means augmenting your Snowflake environment with Databricks for data science. In others, it means a full migration to a unified lakehouse. There’s no one-size-fits-all answer, but there is a guiding principle: align your data platform with your organization’s boldest goals. If those goals include things like “becoming an AI-powered industry leader” or “monetizing data in new ways,” you should strongly consider whether your current platform can get you there, or whether a switch to Databricks would remove friction on that journey.

In conclusion, Databricks vs. Snowflake is not a zero-sum choice so much as a spectrum of capabilities. The unspoken truth is that many switch decisions are driven by a desire to future-proof and innovate, not just by feature checklists. By blending technical analysis with business vision, you can make a decision that you’ll look back on a year or two from now and say, “That set us on the right path.” Whether you ultimately decide to stick with the Snowflake you know, embrace the Databricks fire, or use a bit of both – make sure it’s a conscious choice grounded in ROI and strategy. Your data is too important to do anything less.

Key Takeaways:

• Know your needs: If you foresee heavy ML/AI and varied data types, Databricks offers a broader canvas. However, if your needs are stable BI and ease of use, Snowflake may suffice.
• Scalability & performance: Both scale well, but Snowflake shines for hassle-free SQL performance, while Databricks handles large, complex workloads and AI with ease – even proving higher price/performance on standard benchmarks.
• Cost considerations: Snowflake’s pricing is predictable but can spike with heavy use; Databricks can lower cloud costs (e.g., 9x cheaper ETL in some cases) at the expense of more management. Always evaluate total cost, including engineering effort, and consider ROI of new capabilities.
• Flexibility & lock-in: Databricks’ open ecosystem (open data formats, multiple languages) avoids vendor lock-in and enables integration of the latest tools – aligning with a modern, cloud-agnostic strategy. Snowflake’s closed approach offers simplicity but less flexibility. Ali Ghodsi’s advice to not “give your data to vendors” underscores the value of an open lakehouse for long-term control.
• AI/ML capability: Databricks is built for the full AI lifecycle – from data prep to model deployment – making it a powerhouse for organizations aiming to become AI-driven. Snowflake, while integrating AI features, currently requires more external help for advanced ML. Databricks’ head start in AI (and momentum with >50% growth in its lakehouse adoption) is a major draw for forward-looking teams.
• Decision framework: Use a structured approach (workloads, cost, team skills, governance, strategic fit, transition planning) to decide. Not every company should switch, but those that do should have a clear business case and a phased plan.
• Think long-term: Choose the platform that not only solves today’s problems but also positions you for tomorrow’s opportunities. The data landscape is evolving fast (streaming, AI, real-time apps), and a flexible, unified platform can be a strategic asset.

By weighing these factors, CTOs and business leaders can cut through the hype and make an informed decision. Remember, the ultimate goal is to leverage data for competitive advantage. Whether that means doubling down on Snowflake or charting a new course with Databricks, ensure your choice propels your data strategy—and business—to new heights.