5 Reasons Why Autonomous Database Is Better than Snowflake

Why Autonomous Database is better than Snowflake

Here are the top five reasons customers choose Oracle Autonomous Database over Snowflake.

  1. Maximize data protection: Autonomous Database offers a defense-in-depth strategy by providing comprehensive and automated security tools for network access, authentication, and database security. Autonomous Database fully automates database administration and provides wizard-driven advanced security features to help protect data throughout its lifecycle. This makes it straightforward to address regulatory requirements and disrupt malicious intent. The Oracle Data Safe service is included with Autonomous Database, empowering organizations to understand data sensitivity, evaluate data risks, mask sensitive data, implement and monitor security controls, assess user security, and monitor user activity, all in a single, unified console.
  2. Build apps and analytics with automated, integrated and open GenAI: Autonomous Database Select AI provides automated and integrated generative AI with in-database large language models (LLMs); an automated, in-database vector store; scale-out vector processing; and the ability to have contextual conversations in natural language—allowing users take advantage of generative AI without existing AI expertise or data movement. Unlike copilots and advisors, Select AI is immediately available directly for any application that can connect to the database.
  3. Simplify data management: Autonomous Database has extensive integrated capabilities that eliminate the need for separate, standalone services—making data warehousing and analytics easier to convert into data-driven insights.
  4. Lower data management cost at any scale: Autonomous Database costs less due to its built-in features and autoscaling capability, which closely matches consumption costs to overall workload requirements. It delivers significantly lower TCO for all types of data warehousing workloads by eliminating the complexity, latency, cost, and risks of ETL and data duplication across multiple cloud services.
  5. Benefit from machine learning–powered automation: Autonomous Database provides workload-aware, machine learning–powered automation of various aspects of the application lifecycle, including provisioning, data loading, query execution, and failure handling.

1. Help maximize data protection

Autonomous Database offers a defense-in-depth strategy by providing comprehensive and automated security tools for network access, authentication, and database security. Autonomous Database provides wizard-driven advanced security features to help protect data throughout its lifecycle. This makes it straightforward to address regulatory requirements and disrupt malicious intent. The Oracle Data Safe service is included with Autonomous Database, empowering organizations to understand data sensitivity, evaluate data risks, mask sensitive data, implement and monitor security controls, assess user security, and monitor user activity, all in a single, unified console.

Capability and evidence
Oracle Autonomous Database
Snowflake

Base network security features

Are base network security features such as private endpoints, ACLs, and wallets included as core data warehouse capabilities?
yes

Autonomous Database uses Private Endpoints to keep all traffic to and from the database off the public internet. In addition, it provides network access control lists (ACLs) so customers can specifically list the IP addresses, CIDR blocks, or VCNs that can connect to their database. All connectivity to Autonomous Database is allowed through encrypted wallets. These capabilities are part of the core functionality of Autonomous Database at no extra cost.

For more details, see Security and Authentication in Oracle Autonomous Database.
no

Private connectivity to Snowflake is only available for the higher cost Business Critical Edition and is not part of its core functionality.

See Snowflake Editions for more information.

Out-of-box tools for sensitive data

Does the database provide out-of-box automatic tools to detect sensitive data, evaluate risks, and mask that data at no additional cost?
yes

Oracle Data Safe, included with Autonomous Database at no additional cost, automatically discovers sensitive data in the database, evaluates the risks, enables users to mask the data, and continuously monitors and alerts users on preventive measures. All these capabilities are available via a unified console.
no

Snowflake doesn’t provide extensive tools for static data masking or reporting to support proactive security measures. Snowflake’s Dynamic Data Masking and Tokenization are available for higher cost Snowflake editions (Enterprise and above), and reporting requires extra compute credits, so users have to pay more for proactive protection of their own data.

SQL firewall

Does the database have an in-built SQL firewall to protect against SQL injections?
yes

Oracle SQL Firewall is built into Autonomous Database, providing complete protection against SQL injections. Oracle SQL Firewall inspects all incoming database connections and SQL statements, whether local or over the network, or encrypted or clear text. It evaluates the complete SQL and processing context to help ensure that any direct access to the database comes exclusively from trusted endpoints. This is not an extra-cost option.
no

Snowflake doesn’t provide in-built SQL firewall protection, which can leave databases vulnerable to SQL injections.

For more information, see SQL executing in Snowflake.

Privileged user safeguards

Does the database provide tools to protect sensitive data from privileged users?
yes

Oracle Database Vault can help prevent even privileged users, such as DBAs, from accessing sensitive user data while still allowing them to do their regular database management activities. This helps customers implement a separation of duties between administrators and data owners to comply with privacy and regulatory requirements. Learn more about Oracle Database Vault users and roles.

In addition, Privilege analysis in Autonomous Database helps users implement least privilege best practices for database roles and privileges.
no

Auditing user access is only available with costlier editions, and not as a base feature.

Key management

Does the database protect data using vendor- or customer-managed keys, rotating keys as needed at zero cost?
yes

Autonomous Database encrypts data at rest and in transit. Customers can configure customer-managed keys based on Oracle Cloud Infrastructure Vault, Oracle Key Vault, Azure Key Vault, or AWS Key Management Service.

The TDE key can be rotated on demand at no additional cost.
no

Snowflake does not support key rotation for customer-managed keys and does not recommend an automatic key rotation policy for them.


Perspectives from customers

  • Thomson Reuters

    Thomson Reuters rolls out ONESOURCE with zero downtime on Oracle Cloud

    The high availability of Oracle’s autonomous cloud database enables ONESOURCE staff to update content and functionality for calculating indirect taxes while maintaining critical transaction processing. Meanwhile, Oracle Data Safe and Oracle Cloud Guard enhance data security and insights.

  • Cognizant

    Cognizant modernizes data warehouse and analytics with Oracle Cloud

    The company’s security is tighter than ever with OCI networking security, including OCI Logging, Virtual Private Vault, Oracle Data Safe, and Autonomous Data Warehouse security. Cognizant has control over the encryption keys for data, while the IT team has ready access to database audit data and centralized event logging.



2. Build apps and analytics with automated, integrated, and open GenAI

Autonomous Database Select AI provides automated and integrated generative AI with in-database large language models (LLMs); an automated, in-database vector store; scale-out vector processing; and the ability to have contextual conversations in natural language—allowing users to take advantage of generative AI without existing AI expertise or data movement. Unlike copilots and advisors, Select AI is immediately available directly for any application that can connect to the database.

Capability and evidence
Autonomous Database
Snowflake

In-database machine learning

Can developers and data analysts build, train, deploy, and explain machine learning models within the database? Do data and ML models remain inside the database to speed up results and reduce the latency and risk of data movement between data stores?
yes

See the full list of in-database ML capabilities for Autonomous Database.

Additionally, Autonomous Database customers can use popular open source ML algorithms with Oracle Machine Learning for Python.
no

With Snowflake, users must rely on third-party machine learning tools or publicly available libraries to build, train, and deploy ML models.

Automated machine learning life-cycle

Is the machine learning lifecycle fully automated, including algorithm selection, intelligent data sampling, feature selection, and hyperparameter tuning for all model types?
yes

A fully integrated AutoML user interface is provided within the autonomous database.
no

Snowflake doesn’t support automated, in-database machine learning.

Explainable data models and predictions

Are all models and predictions explainable, which increases trust, fairness, causality, and repeatability and helps with regulatory compliance?
yes
no

Snowflake doesn’t provide in-database machine learning with built-in explainability.

Integrate with LLM of choice

Can customers choose from a range of popular LLMs across their preferred cloud to simplify integration and registration, or are they locked into a specific LLM?
yes

Select AI can be used with today’s most popular LLMs and—given the rapid pace of change in this space—is designed to easily integrate with new LLMs as they become available.

Refer to DBMS_CLOUD_AI Package (oracle.com) for the most up-to-date model support.
no

With Snowflake, users must rely on a separate service, Snowflake Cortex, to use external LLMs. This service has limited regional availability.

Automated generation of vector embeddings

Can the database automate the generation of vector embeddings, including parsing, extracting metadata, creating chunks, and choosing embedding models for data and queries—without requiring AI expertise from the user?

Can all steps be completed in the database, without requiring data movement, to help separate client resources, simplify the process, and reduce costs?
yes

Using the ONNX model format, Oracle provides a framework for an augmented pipeline that includes tokenization and all required post-processing steps for generating vector embeddings seamlessly with AI Vector Search.

Learn more about the prebuilt embedding generation model for Oracle Database 23ai.
no

Snowflake requires AI expertise and manual operations to create vector embeddings. First, users must create chunks with user-defined functions that execute external third-party libraries via Snowflake’s proprietary development framework. Then, they must rely on Snowflake Cortex to generate the vector embeddings. The inclusion of these separate resources can add both complexity and cost.

Accelerated vector processing

Is vector processing parallelized across database processors to deliver speedy results?
yes

AI vector processing and similarity search have been fully optimized to leverage the underlying enterprise hardware and enterprise software features of the OCI infrastructure that runs Autonomous Database.
no

Vector processing is executed on a proprietary development framework using third-party libraries.

Does the database come with a chat-like application to enable the quick use of GenAI within applications?
yes

APEX ChatDB accelerates the development of GenAI chat-driven apps that are business-focused.
no

Users must build a custom chatbot via Snowflake’s proprietary application framework and Streamlit. Using several application frameworks can increase compute and operational costs while slowing the development cycle of GenAI apps.


Perspectives from customers

  • Pianoforte

    Pianoforte gains real-time insights with Oracle Cloud

    Autonomous Database has freed IT specialists to work more closely with business units and spend more time on strategically valuable tasks, such as finding machine-learning techniques that create additional insights through clustering and data-driven predictions.

  • Sensa Analytics

    Sensa Analytics speeds healthcare data insights

    Using OML, Sensa improved payment reimbursement timelines for providers from two to three months to just two weeks. It also processed 100,000 claims per day in minutes to automatically correct medical codes, and reduced accounts receivable outstanding by 39%.





Experience automated and integrated ML and GenAI



3. Simplify data management

Autonomous Database has extensive integrated capabilities that eliminate the need for separate, standalone services—making data warehousing and analytics easier to consume and convert into data-driven insights.

Capability and evidence
Autonomous Database
Snowflake

Unified experience for transactions and analytics across data warehouses and lakes

Can customers run OLTP and analytics workloads across data warehouses and lakes in a single cloud service?
yes
no

Snowflake is designed for analytics workloads only. Customers can’t directly run rich, mature, mission-critical transactional workloads on Snowflake.

More than two years after it was announced, Snowflake’s attempt at lightweight transaction support via Unistore remains in very limited availability and only one cloud.

Built-in, self-service ELT

Does the database offer built-in, self-service ELT to help all users get from data to insights as quickly as possible?
yes

Built-in data transforms allow you to design data transformations in the form of data loads, data flows, and workflows, without requiring you to write any code.
no

Additional third-party ETL/ELT services are generally required to automate and manage data movement from most sources to Snowflake.

This adds cost, risk, time to market, and data latency to any project that requires external data.

Transactional, real-time analytics

Can the database provide accurate, real-time data insights?
yes

Oracle receives the highest scores across every category from multiple industry analysts. See what industry experts are saying.
no

Snowflake’s analytics-only architecture and lack of robust transactional capabilities make data movement necessary. This means accurate, real-time data insights are impossible.

Support for all data types

Can the database manage structured, application-based, unstructured, and semi-structured data, including JSON, Parquet, Avro, CSV, spatial, graph, and newer technologies such as blockchain?
yes

All modern datatypes are supported.
no

There is no support for unstructured and blockchain data, and there is limited support for graph analysis. These deficiencies severely limit the development of modern, intelligent applications.


Perspectives from leading analysts

  • WinterCorp

    “The entire process of creating, managing, and operating a database is greatly simplified, costs are in general reduced substantially, agility is increased, and real elasticity is delivered. The author has experimented hands-on with ADB and found these benefits real and substantial.”

    —Richard Winter, WinterCorp

  • Omdia

    “Oracle has waited for the marketplace to mature to the point where an automated converged database that delivers simplicity, governance, security, and manageability will win out over the latest disjointed collection of shiny objects.”

    —Bradley Shimmin, Omdia

  • Cloud Wars

    “Better, hardened security; a fully converged autonomous data warehouse service that greatly simplifies management and maintenance; plus nonstop self-tuning, self-monitoring, and patching...are all big pluses in Oracle’s favor.”

    —Bob Evans, Cloud Wars





Experience the simplicity of Autonomous Database



4. Lower data management cost at any scale

Autonomous Database costs less due to its built-in features and autoscaling capability, which closely matches consumption costs to overall workload requirements. It delivers significantly lower TCO for all types of data warehousing workloads by eliminating the complexity, latency, cost, and risks of ETL and data duplication across multiple cloud services.

Capability and evidence
Autonomous Database
Snowflake

Real-time elasticity to any number of nodes

Can customers increase or decrease the number of nodes to precisely match the requirements of their workload? If so, can they do this without incurring any downtime or read-only time, or rebalancing data across the available nodes?
yes
no

Snowflake provides compute resources only in blocks of 1, 2, 4, 8, 16, 32, 64, 128, 256, and 512 nodes. Customers have no choice but to overprovision their deployment by choosing a larger size than they need, spending more money than necessary.

For example, to scale up from 32 nodes, users would have to jump to 64 nodes, even if they only needed a small increase in compute resources. See Snowflake documentation for details.

Cloud compatibility

Can customers run their existing data warehouse ecosystems in the cloud without re-architecting them to an entirely new platform?
yes
no

Snowflake is not compatible with customers’ existing Oracle Database implementations. It requires time-consuming replatforming. For example, schemas must be redesigned to accommodate Snowflake’s write-once storage model, and PL/SQL procedures and functions must be rewritten in JavaScript.

Self-service data warehouses

Does the database include built-in capabilities that address a broad set of analytical and machine learning use cases in modern data warehouses?
yes
no

Snowflake offers limited built-in analytical capabilities, requiring third-party services to create a complete solution. Organizations that want to use machine learning, multidimensional models, graph analytics, spatial analytics, and low-code development tools, or ingest streaming data, need to subscribe to one or more services at an additional cost.

Data discovery and masking

Does the data warehouse include automated, intelligent data discovery and masking tools to secure sensitive data?
yes

See Oracle Data Safe for more detailed information.
no

Snowflake provides core security features, but lacks built-in functionality equivalent to Oracle Data Safe. Instead, customers must implement similar features using additional services and tools, which increases operational and administrative costs for securing data.


Perspectives from leading analysts and customers

  • IDC

    IDC finds that Autonomous Data Warehouse customers obtain a 417%

    “Our research, based on interviews with several customers around the globe, shows that those Oracle ADW customers have achieved approximately 63% reduced total cost of operations while increasing the productivity of data analytics teams by 27%.”

  • Lyft

    Lyft uses Oracle to build a single source of information across finance and operations—and gets data to stakeholders faster.

    “The world’s going a thousand miles an hour, so for me to say, ‘Wait, I need another week’ just doesn’t work anymore… It’s a true transformation at Lyft with the products we’re implementing.”

    —Lisa Blackwood-Kapral, Chief Accounting Officer, Lyft

  • Experian

    Experian improves data workloads with Oracle Cloud.

    Experian moved multiple data workloads onto Oracle’s cloud, including fraud detection, call center analytics, financial analytics, and credit data. The company saw a 40% increase in performance, a 60% reduction in costs, and increased reliability and resilience overall.



5. Benefit from machine learning–powered automation

Autonomous Database provides workload-aware, machine learning–powered automation of various aspects of the application lifecycle, including provisioning, data loading, query execution, and failure handling.

Capability and evidence
Autonomous Database
Snowflake

Automated scaling

Does the platform automatically expand and shrink compute resources to the optimal size?
yes

Autonomous Database uses expert systems to automatically expand and shrink the compute resources, with zero downtime, as the overall workload changes. This results in optimal performance and cost, since customers only pay for resources via per-second billing.
no

Snowflake’s scaling doubles the size of the cluster each time the cluster scales. Developers and DBAs must guess or manually estimate the optimal size of the cluster through trial and error to prevent escalating costs.

Automated query performance tuning

Does the database continuously learn from and improve query performance?
yes

Autonomous Database learns from the execution of queries and uses machine learning to support optimal performance of subsequent queries automatically.
no

Query plans aren’t automatically improved using machine learning models.

Data re-engineering during loading

Can the database analyze and extend a data set as it loads?
yes

Autonomous Database analyzes the data to be loaded. It can automatically extend the data set to identify sensitive data within columns, perform sentiment analysis and key phrase extraction, determine the language of textual columns, and provide a translation.
no

Snowflake’s APIs load data into a target table.


Perspectives from customers

  • McDonald’s

    McDonald’s Hong Kong leverages machine learning to improve the customer experience

    “Machine learning can help unleash the potential value of data and drive business growth. It is such an exciting journey of transformation that we can’t afford to miss. We can now be more surgical in coupon planning for our targeted segments.”

    —Keith Chan, CFO and CTO, McDonald’s Hong Kong

  • Halldis

    Halldis finds the ideal stay using Oracle Cloud

    The company relies on machine learning to help sales and marketing teams provide guests with personalized accommodations, guide advertising decisions, and increase repeat bookings.



Try Oracle Autonomous Database for free.

Try Oracle Autonomous Database for free

What is an autonomous database?

Oracle Autonomous Database is a cloud native data warehouse service that eliminates the complexities of operating, securing, and developing data-driven analytics.

Its built-in support of rich native data types, diverse algorithms, and native generative AI capabilities enable you to quickly and easily build intelligent applications without multiple siloed and specialized data repositories that weaken overall security and governance, increase costs, and introduce data integration complexities.

Will it work with any cloud?

Autonomous Database is available in Oracle Cloud Infrastructure (OCI), Microsoft Azure, and Google Cloud.

Oracle vs. Snowflake resources

Interested in learning more about Oracle Autonomous Data Warehouse? Let one of our experts help.

Get in touch