CockroachDB vs SQL Server comparison

Cockroach Labs is the creator of CockroachDB, the cloud-native, resilient, distributed SQL database enterprises worldwide trust to run mission-critical AI and other applications that scale fast, avert and survive disaster, and thrive everywhere. It runs on the Big 3 clouds, on prem, and in hybrid configurations powering Fortune 500, Forbes Global 2000, and Inc. 5000 brands, and game-changing innovators, including OpenAI, CoreWeave, Adobe, Netflix, Booking.com, DoorDash, FanDuel, Cisco, P&G, UiPath, Fortinet, Roblox, EA, BestBuy, SpaceX, Nvidia, the USVA, and HPE. Cockroach Labs has customers in 40+ countries across all world regions, 25+ verticals, and 50+ Use Cases. Cockroach Labs operates its own ISV Partner Ecosystem powering Payments, Identity Management (IDM/IAM), Banking & Wallet, Trading, and other high-demand use cases. Cockroach Labs is an AWS Partner of the Year finalist and has achieved AWS Competency Partner certifications in Data & Analytics and Financial Services (FSI). CockroachDB pricing is available at https://www.cockroachlabs.com/pricing/

Vector, RAG, and GenAI Workloads

CockroachDB includes native support for the VECTOR data type and pgvector API compatibility, enabling storage and retrieval of high-dimensional embeddings. These vector capabilities are critical for Retrieval-Augmented Generation (RAG) pipelines and GenAI workloads that rely on similarity search and contextual embeddings. By supporting distributed vector indexing within the database itself, CockroachDB removes the need for external vector stores and allows AI applications to operate against a single, consistent data layer.

C-SPANN Distributed Indexing

At the core of CockroachDB’s vector search capabilities is the C-SPANN indexing engine. C-SPANN provides scalable approximate nearest neighbor (ANN) search across billions of vectors while supporting incremental updates, real-time writes, and partitioned indexing. This ensures low-latency retrieval in the tens of milliseconds, even under high query throughput. The algorithm eliminates central coordinators, avoids large in-memory structures, and leverages CockroachDB’s sharding and replication to deliver scale, resilience, and global consistency.

Machine Learning and Apache Spark Integration

CockroachDB integrates with modern ML workflows by supporting embeddings generated through frameworks such as AWS Bedrock and Google Vertex AI. Its compatibility with the PostgreSQL JDBC driver allows seamless integration with Apache Spark, enabling distributed processing and advanced analytics on CockroachDB data.

PostgreSQL Compatibility and JSON Support

CockroachDB speaks the PostgreSQL wire protocol, so applications, drivers, and tools designed to work with Postgres can connect to CockroachDB without modification, enabling seamless use of familiar SQL features and integration with the wider Postgres ecosystem. This includes support for advanced data types such as JSON and JSONB, which allow developers to store and query semi-structured data natively.

Geospatial and Graph Capabilities

CockroachDB also provides first-class geospatial data support, allowing developers to store, query, and analyze spatial data directly in SQL. For graph workloads, CockroachDB employs JSON flexibility to represent relationships and delivers query capabilities for graph-like traversals. This combination enables hybrid applications that merge relational, geospatial, document, and graph data within a single platform.

Analytics, BI, and Integration

To support high-performance analytics and BI, CockroachDB supports core analytical use cases and functions including Enterprise Data Warehouse, Lakehouse, and Event Analytics, and offers materialized views for precomputing complex joins and aggregations. Its PostgreSQL wire compatibility ensures direct connectivity with all relevant BI and analytics apps and tools including Amazon Redshift, Snowflake, Kafka, Google BigQuery, Salesforce Tableau, Databricks, Cognos, Looker, Grafana, Power BI, Qlik Sense, SAP, SAS, Sisense, and TIBCO Spotfire. Data scientists can interact with CockroachDB through Jupyter Notebooks, querying structured and semi-structured data and loading results for analysis. Change data capture (CDC) streams provide real-time updates to analytics pipelines and feature stores, keeping downstream systems fresh and reliable. Columnar vectorized execution accelerates query processing, optimizes transactional throughput, and minimizes latency for demanding distributed workloads.

MOLT AI-Powered Migration

Organizations often know their data infrastructure is not supporting the business, but find it too painful to change. CockroachDB’s MOLT (Migrate Off Legacy Technology) is designed to enable safe, minimal-downtime database migrations from legacy systems to CockroachDB. MOLT Fetch supports data migration from PostgreSQL, MySQL, SQL Server, and Oracle, with SQL Server and DB2 coming soon. CockroachDB also has a portfolio of data replication platform integrations including Precisely, Striim, Qlik, Confluent, IBM, etc.

Together, these capabilities ensure that CockroachDB supports both operational and analytical workloads, bridging traditional SQL applications with emerging Gen AI and ML use cases.

SQL Server is a relational database management system (RDBMS) by Microsoft. The product's main purposes are to store data and retrieve it as requested by other software applications - on the same computer or on another computer across a shared network. The solution is built on top of Structured Query Language (SQL), which is a standardized programming language used for relational database management.

The product is tied to Transact-SQL (T-SQL), which is an implementation of SQL from Microsoft that adds several proprietary programming extensions to the standard language. SQL Server is built similarly to other RDBMS products, as its structure is a row-based table that connects related data elements in different tables to one another. One of its most important components is the SQL Server Database Engine, as it controls data processing, storage, and security. Beneath the Database Engine is the SQL Server Operating System, which is used for memory and I/O management, locking data to avoid unneeded upgrades, and job scheduling.

The solution has four editions with different sets of services and tools. They include:

• SQL Server Developer
• SQL Server Expression
• SQL Server Standard 
• SQL Server Enterprise

The first two are available for free and are typically utilized by smaller companies, as they work with fewer functions and storage. The second two editions are generally used by bigger organizations and enterprises and offer more features.

The solution has several functions through which users can facilitate different data-related processes. These include:

• Aggregate functions: These functions are used to calculate one or more values and return a single value.
  
  
• Window functions: Window functions calculate an aggregate value based on a group of rows and return multiple rows for each involved group.
  
  
• Date functions: These functions allow clients to handle data and time data efficiently.
  
  
• String functions: Through the string functions, SQL Server processes an input string and returns a string or numeric value.
  
  
• System functions: The system functions of the product return objects, values, and settings.

SQL Server Services

SQL Server has a wide range of add-on services that provide additional benefits beyond database management. These services include:

• Machine learning: SQL Server machine learning services allow users to do machine learning and data analytics within the product.
   
• Service broker: This feature is a part of the database engine and offers messaging and message queuing for applications on the platform.
  
  
• Replication: This feature consists of transaction replication, merge replication, and snapshot replication. 
  
  
• Analysis: SQL Server Analysis Services (SSAS) adds online analytical processing (OLAP) and data mining capabilities to SQL Server databases.
  
  
• Reporting: This feature supports the development of custom reporting applications.
  
  
• Notifications: This feature generates data-driven notifications which are sent to subscribers of Notification Services.
  
  
• Integration: This SQL Server feature provides capabilities for data integration, data import, and data warehousing.
  
  
• Full-text search: Through this feature, users can utilize specialized indexing and query for unstructured text stored in the solution.
  
  
• Visual studio: This feature supports data programming with SQL Server.
  
  
• Azure Data Studio: This is a cross-platform query editor that can be downloaded and used alongside SQL Server.

SQL Server Benefits

The solution has many benefits for users. These include the following:

• The platform ensures a consistent experience across other platforms while being fast and agile.
  
  
• Through the built-in intelligence features, users can understand their data better.
  
  
• The solution has enterprise-level security, which ensures the smooth and safe performance of users' data.
  
  
• SQL Server offers the ability to retrieve large amounts of data quickly and efficiently.
  
  
• The product installation process is fairly easy, and it offers a user-friendly interface.
  
  
• SQL Server offers improved data storage and retrieval functions through frequent upgrades.
  
  
• The solution reduces the risk of database server attacks, since it is not an open-source database.

Reviews from Real Users

A president at a consultancy evaluates SQL Server as a veteran solution with critical log shipping feature. 

Harkamal S., a user at a manufacturing company, rates SQL Server with a high mark because it is a stable, scalable, and easy-to-deploy solution that pretty much covers everything.