HeatWave GenAI

Oracle HeatWave GenAI provides integrated and automated 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—letting you take advantage of generative AI without AI expertise, data movement, or additional cost.

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HeatWave GenAI: Integrated and automated generative AI

Watch the Oracle HeatWave GenAI announcement, featuring Edward Screven, Oracle’s chief corporate architect, and Nipun Agarwal, senior vice president of MySQL and HeatWave development. Learn how you can build generative AI applications without AI expertise, data movement, or additional cost.

Why use HeatWave GenAI?

  • Quickly use generative AI anywhere

    Use in-database, optimized LLMs across clouds and regions to help retrieve data and generate or summarize content—without the hassle of external LLM selection and integration.

  • Easily get more accurate and relevant answers

    Let LLMs search your proprietary documents to help get more accurate and contextually relevant answers—without AI expertise or moving data to a separate vector database. HeatWave GenAI automates embedding generation.

  • Get faster results at lower cost

    For similarity search, HeatWave GenAI is less expensive and is 15X faster than Databricks, 18X faster than Google BigQuery, and 30X faster than Snowflake.

  • Converse in natural language

    Get rapid insights from your documents via natural language conversations. The HeatWave Chat interface preserves context to help enable human-like conversations with follow-up questions.

Key features of HeatWave GenAI

In-database LLMs

Use the built-in, optimized LLMs in all Oracle Cloud Infrastructure (OCI) regions, OCI Dedicated Region, and across clouds; and obtain consistent results with predictable performance across deployments. Help reduce infrastructure costs by eliminating the need to provision GPUs.

Integrated with OCI Generative AI

Access pretrained, foundational models from Cohere and Meta via the OCI Generative AI service.

HeatWave Chat

Have contextual conversations in natural language informed by your unstructured data in HeatWave Vector Store. Use the integrated Lakehouse Navigator to help guide LLMs to search through specific documents, helping you reduce costs while getting more accurate results faster.

In-database vector store

HeatWave Vector Store houses your proprietary documents in various formats, acting as the knowledge base for retrieval-augmented generation (RAG) to help you get more accurate and contextually relevant answers—without moving data to a separate vector database.

Automated generation of embeddings

Leverage the automated pipeline to help discover and ingest proprietary documents in HeatWave Vector Store, making it easier for developers and analysts without AI expertise to use the vector store.

Scale-out vector processing

Vector processing is parallelized across up to 512 HeatWave cluster nodes and executed at memory bandwidth, helping to deliver fast results with a reduced likelihood of accuracy loss.

Read the HeatWave GenAI technical brief

Customer perspectives on HeatWave GenAI

  • “HeatWave GenAI makes it extremely simple to take advantage of generative AI. The support for in-database LLMs and in-database vector creation leads to 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.”

    —Vijay Sundhar, CEO, SmarterD

  • “We heavily use the in-database HeatWave AutoML for making various recommendations to our customers. 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.”

    —Safarath Shafi, CEO, EatEasy

  • “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. 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.”

    —Eric Aguilar, Founder, Aiwifi

Who benefits from HeatWave GenAI?

  • Developers can deliver apps with built-in AI

    Built-in LLMs and HeatWave Chat help enable you to deliver apps that are preconfigured for contextual conversations in natural language. There’s no need for external LLMs and GPUs.

  • Analysts can rapidly get new insights

    HeatWave GenAI can help you can easily converse with your data, perform similarity searches across documents, and retrieve information from your proprietary data.

  • IT can help accelerate AI innovation

    Empower developers and business teams with integrated capabilities and automation to take advantage of generative AI. Easily enable natural language conversations and RAG.

You can use the in-database LLMs to help generate or summarize content based on your unstructured documents. Users can ask questions in natural language via applications, and the LLM will process the request and deliver the content.


Content generation diagram, description below:

A user is asking a question in natural language “Can you generate a summary of this solution brief?”. The large language model (LLM) processes this input and generates the summary as output.



You can combine the power of generative AI with other built-in HeatWave capabilities, such as machine learning, to help reduce costs and obtain more accurate results faster. In this example, a manufacturing company does so for predictive maintenance. Engineers can use Oracle HeatWave AutoML to help automatically produce a report of anomalous production logs and HeatWave GenAI helps to rapidly determine the root cause of the issue by simply asking a question in natural language, instead of manually analyzing the logs.


Analysis generation diagram, description below:

A user asks via HeatWave Chat “What is the main problem in this collection of logs? Provide a two-sentence summary.”. First, HeatWave AutoML produces a filtered list of anomalous logs based on all the production logs that it continuously ingests. Then HeatWave Vector Store provides additional context to the LLM based on the logs knowledge base. The LLM takes that augmented prompt, produces a report, and provides the user with a detailed answer explaining the issue in natural language.



Chatbots can use RAG to, for example, help answer employees’ questions about internal company policies. Internal documents detailing policies are stored as embeddings in HeatWave Vector Store. For a given user query, the vector store helps to identify the most similar documents by performing a similarity search against the stored embeddings. These documents are used to augment the prompt given to the LLM so that it provides an accurate answer.


RAG diagram, description below:

A user asks via HeatWave Chat “Which laptops can I order and what is the process?”. HeatWave processes the question by accessing internal policy documents housed in HeatWave Vector Store. It then provides an augmented prompt to the LLM that can generate the response “Here is the list of approved vendors and the steps to follow to order.”



Developers can build applications leveraging the combined power of built-in machine learning, generative AI, and vector store to deliver personalized recommendations. In this example, the application uses the HeatWave AutoML recommender system to recommend restaurants based on the user’s preferences or what the user previously ordered. With HeatWave Vector Store, the application can additionally search through restaurants’ menus in PDF format to suggest specific dishes, providing greater value to customers.


RAG enhanced with ML diagram, description below:

A user asks via HeatWave Chat “What vegan dishes do you suggest for me today?”. First, the HeatWave AutoML recommender system suggests a list of restaurants based on what the user previously ordered. Then, HeatWave Vector Store provides an augmented prompt to the LLM based on the restaurants’ menus that it houses. The LLM can then generates a personalized recommendation of dishes in natural language.



JUNE 26, 2024

Announcing the General Availability of HeatWave GenAI

Nipun Agarwal, Oracle Senior Vice President, HeatWave Development

HeatWave has enabled organizations to run transaction processing, analytics across data warehouses and data lakes, and machine learning within a single, fully managed cloud service. Today, we’re announcing the general availability of HeatWave GenAI—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.

Read the complete post

See what top industry analysts say about HeatWave GenAI

  • NAND Research logo

    “With in-database LLMs that are ready to go and a fully automated vector store that’s ready for vector processing on day one, HeatWave GenAI takes AI simplicity—and price performance—to a level that its competitors such as Snowflake, Google BigQuery and Databricks can’t remotely begin to approach.”

    Steve McDowell
    Principal Analyst and Founding Partner, NAND Research
  • Constellation Research logo

    “HeatWave’s engineering innovation continues to deliver on the vision of a universal cloud database. 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.”

    Holger Mueller
    Vice President and Principal Analyst, Constellation Research
  • The Futurum Group logo

    “HeatWave GenAI has delivered vector processing performance that is 30X faster than Snowflake, 18X faster than Google BigQuery and 15X faster than Databricks—at up to 6X lower cost. For any organization serious about high performance generative AI workloads, spending company resources on any of these three or other vector database offerings is the equivalent of burning money and trying to justify it as a good idea.”

    Ron Westfall
    Senior Analyst and Research Director, The Futurum Group
  • dbInsight logo

    “HeatWave is taking a big step in making generative AI and Retrieval-Augmented Generation (RAG) more accessible by pushing all the complexity of creating vector embeddings under the hood. Developers simply point to the source files sitting in cloud object storage, and HeatWave then handles the heavy lift.”

    Tony Baer
    Founder and CEO, dbInsight

Get started with HeatWave GenAI

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