Oracle Announces Industry First In-Database LLMs and an Automated In-Database Vector Store with HeatWave GenAI
Oracle has announced the availability of HeatWave GenAI, featuring industry-first in-database large language models (LLMs) and an automated vector store. Customers can now build generative AI applications without AI expertise, data movement, or additional costs. HeatWave GenAI offers superior performance, being 30X faster than Snowflake, 18X faster than Google BigQuery, and 15X faster than Databricks for vector processing. Available in all Oracle Cloud regions and infrastructures, it allows developers to create vector stores with a single SQL command and perform natural language searches efficiently. The platform integrates with OCI Generative AI, providing access to pre-trained models and simplifying the development of AI applications. Benchmark tests show significant performance and cost advantages over competitors.
- HeatWave GenAI offers 30X faster vector processing compared to Snowflake.
- HeatWave GenAI is 18X faster and 60% cheaper than Google BigQuery.
- HeatWave GenAI is 15X faster and 85% cheaper than Databricks.
- HeatWave GenAI enables in-database LLMs and vector stores at no additional cost.
- HeatWave GenAI integrates with OCI Generative AI, providing access to pre-trained models.
- None.
Insights
Oracle's announcement of the HeatWave GenAI introduces significant advancements in database technology, particularly through the integration of large language models (LLMs) and an automated vector store within the database itself. This is a substantial shift from traditional models where generative AI applications required external processing capabilities and complex data movements. By embedding these AI capabilities directly into the HeatWave database, Oracle eliminates the need for external LLM selection and integration, simplifying the development process for enterprises.
The performance metrics presented, such as being
From a technological perspective, Oracle's approach reduces application complexity and improves data security by keeping data within the database. This is important for enterprises dealing with sensitive information. The built-in embedding models and the ability to perform natural language searches in a single step further underscore the innovative edge of HeatWave GenAI.
The introduction of a new VECTOR data type and optimized distance functions for semantic search enhances the database's capability to handle complex queries efficiently. This could be particularly beneficial for use cases involving large datasets and real-time data processing, such as fraud detection or personalized recommendations.
However, potential drawbacks include the dependence on Oracle's ecosystem and the possible challenges in transitioning from existing systems to HeatWave GenAI. Enterprises should weigh these factors carefully against the benefits outlined.
The launch of HeatWave GenAI is strategically significant for Oracle, as it positions the company as a frontrunner in the generative AI market within the cloud database arena. The claimed performance improvements over key competitors such as Snowflake, Databricks and Google BigQuery highlight Oracle's competitive edge and could influence market dynamics.
For potential retail investors, it's important to note the emphasis on cost savings and enhanced performance, which could drive broader adoption of Oracle's cloud services. By reducing the need for external AI expertise and additional hardware, Oracle is lowering the entry barriers for enterprises to leverage advanced AI capabilities, potentially expanding its customer base.
Another factor to consider is the democratization of AI. Oracle's approach could spur the development of new applications and services, further entrenching the company's presence in various industries. The ability to integrate generative AI with existing database functionalities might also lead to improved operational efficiencies and innovative solutions for business challenges.
On the flip side, the intense competition in the cloud services market means Oracle must continue to innovate to maintain its edge. While the current benchmarks are impressive, sustained performance and customer satisfaction will be important for long-term success.
Customers can build generative AI applications without AI expertise, data movement, or additional cost
HeatWave GenAI is 30X faster than Snowflake, 18X faster than Google BigQuery, and 15X faster than Databricks for vector processing
With HeatWave GenAI, developers can create a vector store for enterprise unstructured content with a single SQL command, using built-in embedding models. Users can perform natural language searches in a single step using either in-database or external LLMs. Data doesn't leave the database and, due to HeatWave's extreme scale and performance, there is no need to provision GPUs. As a result, developers can reduce application complexity, increase performance, improve data security, and lower costs.
"HeatWave's stunning pace of innovation continues with the addition of HeatWave GenAI to existing built-in HeatWave capabilities: HeatWave Lakehouse, HeatWave Autopilot, HeatWave AutoML, and HeatWave MySQL," said Edward Screven, chief corporate architect, Oracle. "Today's integrated and automated AI enhancements allow developers to build rich generative AI applications faster, without requiring AI expertise or moving data. Users now have an intuitive way to interact with their enterprise data and rapidly get the accurate answers they need for their businesses."
"HeatWave GenAI makes it extremely easy to take advantage of generative AI," said Vijay Sundhar, chief executive officer, SmarterD. "The support for in-database LLMs and in-database vector creation leads to a significant reduction in application complexity, predictable inference latency, and most of all, no additional cost to us to use the LLMs or create the embeddings. This is truly the democratization of generative AI and we believe it will result in building richer applications with HeatWave GenAI and significant gains in productivity for our customers."
New automated and built-in generative AI features include:
- In-database LLMs simplify the development of generative AI applications at a lower cost. Customers can benefit from generative AI without the complexity of external LLM selection and integration, and without worrying about the availability of LLMs in various cloud providers' data centers. The in-database LLMs enable customers to search data, generate or summarize content, and perform retrieval-augmented generation (RAG) with HeatWave Vector Store. In addition, they can combine generative AI with other built-in HeatWave capabilities such as AutoML to build richer applications. HeatWave GenAI is also integrated with the OCI Generative AI service to access pre-trained, foundational models from leading LLM providers.
- Automated in-database Vector Store enables customers to use generative AI with their business documents without moving data to a separate vector database and without AI expertise. All the steps to create a vector store and vector embeddings are automated and executed inside the database, including discovering the documents in object storage, parsing them, generating embeddings in a highly parallel and optimized way, and inserting them into the vector store making HeatWave Vector Store efficient and easy to use. Using a vector store for RAG helps solve the hallucination challenge of LLMs as the models can search proprietary data with appropriate context to provide more accurate and relevant answers.
- Scale-out vector processing delivers very fast semantic search results without any loss of accuracy. HeatWave supports a new, native VECTOR data type and an optimized implementation of the distance function, enabling customers to perform semantic queries with standard SQL. In-memory hybrid columnar representation and the scale-out architecture of HeatWave enable vector processing to execute at near-memory bandwidth and parallelize across up to 512 HeatWave nodes. As a result, customers get their questions answered rapidly. Users can also combine semantic search with other SQL operators to, for example, join several tables with different documents and perform similarity searches across all documents.
- HeatWave Chat is a Visual Code plug-in for MySQL Shell which provides a graphical interface for HeatWave GenAI and enables developers to ask questions in natural language or SQL. The integrated Lakehouse Navigator enables users to select files from object storage and create a vector store. Users can search across the entire database or restrict the search to a folder. HeatWave maintains context with the history of questions asked, citations of the source documents, and the prompt to the LLM. This facilitates a contextual conversation and allows users to verify the source of answers generated by the LLM. This context is maintained in HeatWave and is available to any application using HeatWave.
Vector Store Creation and Vector Processing Benchmarks
Creating a vector store for documents in PDF, PPT, WORD, and HTML formats is up to 23X faster with HeatWave GenAI and 1/4th the cost of using Knowledge base for Amazon Bedrock.
As demonstrated by a third-party benchmark using a variety of similarity search queries on tables ranging from 1.6GB to 300GB in size, HeatWave GenAI is 30X faster than Snowflake and costs 25 percent less, 15X faster than Databricks and costs 85 percent less, and 18X faster than Google BigQuery and costs 60 percent less.
A separate benchmark reveals that vector indexes in Amazon Aurora PostgreSQL with pgvector can have a high degree of inaccuracy and can yield incorrect results. In contrast, HeatWave similarity search processing always provides accurate results, has predictable response time, is performed at near memory speed, and is up to 10X-80X faster than Aurora using the same number of cores.
"We are thrilled to continue our strong collaboration with Oracle to deliver the power and productivity of AI with HeatWave GenAI for critical enterprise workloads and data sets," said Dan McNamara, senior vice president and general manager, Server Business Unit, AMD. "The joint engineering work undertaken by AMD and Oracle is enabling developers to design innovative enterprise AI solutions by leveraging HeatWave GenAI powered by the core density and outstanding price-performance of AMD EPYC processors."
Additional Customer and Analyst Commentary on HeatWave GenAI
"We heavily use the in-database HeatWave AutoML for making various recommendations to our customers," said Safarath Shafi, chief executive officer, EatEasy. "HeatWave's support for in-database LLMs and in-database vector store is differentiated and the ability to integrate generative AI with AutoML provides further differentiation for HeatWave in the industry, enabling us to offer new kinds of capabilities to our customers. The synergy with AutoML also improves the performance and quality of the LLM results."
"HeatWave in-database LLMs, in-database vector store, scale-out in-memory vector processing, and HeatWave Chat, are very differentiated capabilities from Oracle that democratize generative AI and make it very simple, secure, and inexpensive to use," said Eric Aguilar, founder, Aiwifi. "Using HeatWave and AutoML for our enterprise needs has already transformed our business in several ways, and the introduction of this innovation from Oracle will likely spur growth of a new class of applications where customers are looking for ways to leverage generative AI on their enterprise content."
"HeatWave's engineering innovation continues to deliver on the vision of a universal cloud database," said Holger Mueller, vice president and principal analyst, Constellation Research. "The latest is generative AI done 'HeatWave style'—which includes the integration of an automated, in-database vector store and in-database LLMs directly into the HeatWave core. This enables developers to create new classes of applications as they combine HeatWave elements. For example, they can combine HeatWave AutoML and HeatWave GenAI in a fraud detection application that not only detects suspicious transactions—but also provides an understandable explanation. This all runs in the database, so there's no need to move data to external vector databases, keeping the data more secure. It also makes HeatWave GenAI highly performant at a fraction of the cost as demonstrated in competitive benchmarks."
HeatWave
HeatWave is the only cloud service that provides automated and integrated generative AI and machine learning in one offering for transactions and lakehouse-scale analytics. A core component of Oracle's distributed cloud strategy, HeatWave is available natively on OCI and Amazon Web Services, on Microsoft Azure via the Oracle Interconnect for Azure, and in customers' data centers with OCI Dedicated Region and Oracle Alloy.
Additional Resources
- Watch Edward Screven announce new GenAI enhancements to HeatWave
- Read the HeatWave technical blog
- Read what industry analysts are saying about HeatWave
About Oracle
Oracle offers integrated suites of applications plus secure, autonomous infrastructure in the Oracle Cloud. For more information about Oracle (NYSE: ORCL), please visit us at www.oracle.com.
Trademarks
Oracle, Java, MySQL and NetSuite are registered trademarks of Oracle Corporation. NetSuite was the first cloud company—ushering in the new era of cloud computing.
View original content to download multimedia:https://www.prnewswire.com/news-releases/oracle-announces-industry-first-in-database-llms-and-an-automated-in-database-vector-store-with-heatwave-genai-302182466.html
SOURCE Oracle
FAQ
What is Oracle's HeatWave GenAI?
How does HeatWave GenAI compare in performance to competitors?
When was HeatWave GenAI announced?
What are the cost benefits of using HeatWave GenAI?
Is HeatWave GenAI available across all Oracle Cloud regions?
