Databricks Competitors: 4 Top Alternatives in 2024
Discover the best Databricks competitors and find a tool that can help you save on data analytics and management solutions.
Databricks is a great solution if you want to build and manage complex data engineering, data science, and machine learning workflows on a unified, scalable platform. However, it’s less intuitive than other solutions and can be more expensive, especially with the additional costs of underlying cloud storage.
That’s why, in this guide, we look at some of the best Databricks alternatives. We use our buyer data and expert insights to help you find a solution that fits your budget and includes the right features for your needs.
We cover:
- Snowflake
- MongoDB
- Google BigQuery
- Oracle Database
Plus, you can learn how Vendr uses internal pricing data and contract negotiation to get you the lowest price on new tools.
Databricks & alternatives comparison table
Snowflake
Snowflake is a cloud-based data platform based on a combination of shared-disk and shared-nothing architecture. It offers businesses a powerful and flexible solution, excelling at analytics and storage both in warehouses and lakes.
Key features
- Storage and analysis of data in the petabytes with scalability ensured through automatic resource adjustment based on workload
- Compatibility across multiple cloud platforms including AWS, Azure, and Google Cloud
- Integrated data marketplace to access and share live, ready-to-query data sets from multiple providers
Who it’s for
Snowflake is a great option for businesses needing a highly scalable and flexible platform for warehousing and analytics. It particularly shines in multi-cloud environments and when data processing or sharing large-scale data across organizational boundaries.
Unique selling points and things to consider
Snowflake offers businesses a powerful and flexible solution, excelling at analytics and storage both in data warehouses and lakes. This makes it ideal for managing large volumes of data.
Snowflake pricing breakdown
Snowflake bases its pricing on the consumption model. That means you pay for the storage and compute resources you actually use. Storage generally starts at about $23 per terabyte monthly, while compute varies based on the size and duration of workloads. The average contract value for Vendr users comes in at $473K.
- Deals handled: 473
- Unique purchasers: 253
- Average savings: 8%
Learn more about Snowflake pricing and negotiation strategies with our Buyer Guide.
MongoDB
MongoDB is an open-source, NoSQL database platform widely used for applications that require large-scale data storage and complex querying capability. Focusing on performance and flexibility, MongoDB’s architecture fully supports document-based storage.
Key features
- Storage in flexible, JSON-like documents for varied structures and rapid access
- Advanced indexing options to enable complex queries
- Powerful aggregation framework for real-time analytics
Who it’s for
MongoDB is best for businesses that handle large amounts of unstructured data. It’s also great for those looking for a flexible database that can evolve with changing requirements. This works particularly well in industries such as e-commerce where fluctuating data needs to be stored and processed in a highly scalable way.
Unique selling points and things to consider
MongoDB’s document-based model offers high levels of flexibility in app development. This makes it easy to iterate and evolve data models. Scalability features such as sharding make it a strong choice for apps expecting significant short-term growth. Its no SQL nature will, however, come with a learning curve for teams more experienced in relational databases.
MongoDB pricing breakdown
MongoDB offers multiple pricing options based on deployment. Atlas is a fully managed cloud service with pricing starting at $0.08 per hour for the free-tier ‘M0 cluster’. This figure scales up based on storage, memory, and bolt-on features. MongoDB does offer custom pricing for enterprise plans, with the average contract value for Vendr users being around $392K annually.
- Deals handled: 116
- Unique purchasers: 74
- Average savings: 8%
Learn more about MongoDB pricing and negotiation strategies with our Buyer Guide.
Google BigQuery
Google BigQuery is a fully managed service based on serverless data warehouses. It offers scalable data analysis and cloud-based machine learning functionality.
Key features
- Real-time analytics on petabytes of data using standard SQL
- Integration with other Google Cloud services like Dataflow and Dataproc
- Built-in machine learning with BigQuery ML for predictive analytics
Who it’s for
Google BigQuery is perfect for organizations of any size looking for a fast, scaleable, and generally cost-effective analytics solution. It works particularly well if you need real-time analytics on big datasets or processing workflows with machine learning integration.
Unique selling points and things to consider
BigQuery stands out with its serverless and highly scalable architecture. This lets businesses run complex queries on huge datasets with minimal latency or infrastructure concerns.
The integration of machine learning also makes it a powerful tool for companies where both analysts and data scientists need to make high numbers of queries.
Google BigQuery pricing breakdown
BigQuery is based on a pay-as-you-go model. That means the cost is calculated based primarily on storage and query usage, plus any extra features added on.
Here’s how that looks:
- $5 per terabyte (TB) of data processed.
- $0.02 per gigabyte (GB) per month for active storage
- $0.01 per GB per month for long-term storage.
New features like Autoscaler and Compressed Storage can reduce costs by adjusting compute resources in real-time or billing based on post-compression size. Three are also options for flat-rate pricing if you have highly predictable workloads. This starts at about $10,000 per month for dedicated query processing capacity.
Vendr's Google BigQuery expertise:
- Deals handled: 726
- Unique purchasers: 426
- Average savings: 17%
Learn more about Google pricing and negotiation strategies with our Buyer Guide.
Oracle Database
Oracle Database is a versatile platform that can be deployed on-premises, in hybrid environments, or fully in the cloud.
Key features
- Robust support for VMware and seamless integration with Azure
- Fault domains and cross-region disaster recovery solutions
- Integration with Oracle's Autonomous Database for automatic patching, backups, and tuning.
Who it’s for
Oracle Database is aimed at large enterprises, particularly those that are looking to migrate from legacy systems to the cloud or create apps with cloud-native tech.
Unique selling points and things to consider
Oracle Database stands out with its performance-oriented infrastructure. Its ability to support complex workloads and demanding I/O requirements makes it ideal for applications involving high transactions per second like financial systems or online transaction processing (OLTP) databases.
Oracle pricing breakdown
Oracle Database’s pricing works on a pay-as-you-go model, fluctuating based on the specific services you use. Compute instances start at around $0.0165 per OCPU hour, while storage is $0.0255 per GB monthly.
Average contract value comes in at $223K for Vendr users. Oracle also offers savings through Reserved Instances and Annual Universal Credits if you plan on committing to a long-term plan with them.
- Deals handled: 268
- Unique purchasers: 155
- Average savings: 15%
Learn more about Oracle pricing and negotiation strategies with our Buyer Guide.
Negotiate the best terms and save on data intelligence tools
Vendr can help you navigate the procurement process and find a fair price on big data analytics and management solutions. Here’s how.
Vendr’s procurement support
We assess your business needs by finding out, for example, your storage requirements before helping you set an accurate budget. Then, using SKU-level pricing benchmarks, negotiation tips from our community, and insights from industry experts, we help you negotiate. This means assessing discount levers such as optimized contract length and payment terms and leveraging competitor quotes to help you save.
How Deepgram saved $300,000 by partnering with Vendr
By allowing Vendr to negotiate deals on new cloud-based software solutions and having us look through their existing contracts, Deepgram has saved $366k in just two years.
VP of Finance, Jason Rubenstein explains, “The top things I try to spend my time on are financial forecasting, improving unit economics, and achieving efficient revenue growth for the company. If Vendr’s involved, it saves me time to focus on those key areas.”
