Oracle AI Database: 23ai, Select AI, OCI Explained

Unlock data insights w/ plain English! Oracle’s AI database (23ai, Select AI) brings AI to YOUR data. Secure, efficient, powerful.#OracleAI #AIDatabase #Database23ai

Table of Contents

Explanation in video

Oracle’s AI Renaissance: Bringing Intelligence Directly to Your Data

John: It’s quite a turnaround we’re seeing with Oracle, Lila. For years, many, including myself, have pointed out their somewhat leisurely pace in adopting cloud technologies. But their recent Q4 2025 earnings, showing an 11% revenue jump to $15.9 billion and a significant stock surge, tells a new story. It seems their big bet on integrating Artificial Intelligence (AI) directly with their core database offerings is starting to pay off handsomely, driven by surging infrastructure and AI demand.

Lila: That’s fascinating, John! So, this isn’t just about Oracle finally catching up with cloud, but more about them finding a unique angle with AI? What’s the core of this new strategy? Is it about creating new AI models like OpenAI or Google?

John: Precisely. It’s less about competing head-on in the foundation model arms race and more about leveraging their immense existing strength. Oracle Database has been the bedrock for critical enterprise data for decades. Their strategy, exemplified by the new Oracle Database 23ai, is to embed AI capabilities directly *within* the database. This allows companies to derive AI-driven insights from their own data, securely, without the complex and often risky process of moving massive datasets to external third-party AI services.

Lila: “AI capabilities directly into the database”… That sounds powerful. So, instead of taking the data to the AI, Oracle is bringing the AI to the data? How does that change things for businesses that already use Oracle?

John: Exactly. It changes the game in terms of security, efficiency, and relevance. Enterprises already have vast amounts of proprietary data sitting in Oracle databases. This new approach means they can apply sophisticated AI techniques, like those used in generative AI, to *their* specific data, within their trusted Oracle environment. This is what Larry Ellison, Oracle’s CTO, meant when he emphasized that while others claim to have data, Oracle has “most of the world’s valuable data” already within its systems. It’s a compelling proposition: AI tailored to your business, running on your data, in your environment.

Understanding Oracle’s AI-Powered Database Offerings

Lila: I’ve seen “Oracle Autonomous Database Select AI” mentioned a lot in the context of this. What exactly is Select AI, and how does it fit into this picture for, say, a business analyst who isn’t a SQL (Structured Query Language – the standard language for managing and querying databases) expert?

John: That’s a great question, Lila, because Select AI is a cornerstone of making this power accessible. Oracle Autonomous Database Select AI essentially allows users to interact with their database using natural language. Imagine, instead of writing complex SQL queries, a business manager could simply ask: “Show me the top-selling products in the Western region for the last quarter, with a year-over-year comparison.” Select AI, leveraging generative AI, translates that plain English question into the necessary SQL query, executes it, and returns the answer. It democratizes data access.

Lila: Wow, so no more struggling with SQL syntax for simple insights? That sounds like a huge productivity boost! You mentioned generative AI – is Oracle building its own large language models (LLMs – AI models trained on vast amounts of text data to understand and generate human-like language) for this, or integrating with others?

John: Oracle provides a multi-faceted approach. They offer the Oracle Cloud Infrastructure (OCI) Generative AI service, which is a fully managed service providing access to state-of-the-art, customizable LLMs from companies like Cohere, as well as Meta’s Llama 2. This allows businesses to fine-tune these models with their own data for specific tasks. Furthermore, as seen in some of their labs, they are enabling connections to models from Azure OpenAI and Google Gemini, promoting a more open ecosystem. The key is that these models can be used in conjunction with the data residing in the Oracle database.

Lila: So, it’s a combination of providing access to powerful LLMs and making it easy to apply them to your own data securely. What about the database itself? Is Oracle Database 23ai just a regular database with some AI features tacked on, or is it fundamentally different?

John: Oracle Database 23ai, which they’ve even dubbed “the AI database,” represents a significant evolution. It’s designed from the ground up to support AI workloads. A key feature is the integration of AI Vector Search. This is crucial for many modern AI applications, especially those involving Retrieval-Augmented Generation (RAG – a technique that allows LLMs to access and use external, up-to-date information to improve their responses).

The Technical Mechanics: Vectors, RAG, and In-Database AI

Lila: Okay, “AI Vector Search” and “RAG” – those are terms I hear a lot. Can you break those down for our readers who might be new to this? What are vectors in this context, and why do they need their own search?

John: Certainly. In the realm of AI, particularly with LLMs, data like text, images, or even audio is often converted into numerical representations called “vectors” or “embeddings.” These vectors capture the semantic meaning or context of the data. For example, words with similar meanings will have vectors that are “close” to each other in a multi-dimensional space.
AI Vector Search is a technology that allows you to find the most similar items in a vast collection of these vectors. So, if you have a user’s query, you can convert it into a vector and then use vector search to find documents, product descriptions, or customer service records whose vectors are closest, meaning they are semantically most relevant. This is far more powerful than traditional keyword search.

Lila: So, vector search helps the AI understand the *meaning* behind a query, not just matching words? And how does RAG fit in with this and the LLMs we talked about?

John: Precisely. Now, LLMs are incredibly knowledgeable based on their training data, but that data is often general and not specific to a particular enterprise, nor is it always up-to-date. This is where Retrieval-Augmented Generation (RAG) comes in.
With RAG, when a user asks the LLM a question, the system first uses vector search to find relevant information from a specific, trusted knowledge base – like an enterprise’s internal documents, product manuals, or recent customer interactions stored in their Oracle database. This retrieved information is then provided to the LLM along with the original question as additional context. The LLM uses this fresh, specific context to generate a much more accurate, relevant, and reliable answer. Oracle’s strategy of integrating vector search directly into the database makes this RAG process highly efficient because the data and the vector search capability reside in the same place.

Lila: That makes a lot of sense! So, the LLM isn’t just relying on its (potentially outdated) general knowledge, but gets a real-time briefing from the company’s own data. You mentioned “integrating AI capabilities directly into the database.” What are the tangible benefits of this tight integration versus, say, using a separate vector database and a separate LLM service?

John: The benefits are significant:

Reduced Data Movement: Performing AI operations, including vector search and even some model inferencing, directly within the database minimizes the need to move large volumes of data around. This is faster, cheaper, and reduces complexity.
Enhanced Security and Governance: Keeping data within the secure confines of the Oracle database means all existing security protocols, access controls, and auditing mechanisms remain in place. This is paramount for enterprises dealing with sensitive information.
Streamlined Operations and Development: Developers can use familiar SQL and database tools, extended with AI functions, rather than having to learn and manage a disparate set of AI-specific tools and data pipelines. This accelerates development and simplifies maintenance.
Data Consistency and Freshness: AI models operate on the most current data available in the database, ensuring insights are based on the latest information, not stale copies.
Transactional Integrity: For applications that require it, changes driven by AI insights can be part of the same transactional scope as other database operations, ensuring data integrity.

Oracle highlights these points, emphasizing that “embedding AI within the database reduces data movement costs, improves security, and streamlines operations.” The `DBMS_CLOUD_AI` package, for instance, is a PL/SQL (Oracle’s procedural extension language for SQL) package that directly facilitates the translation of natural language prompts into SQL and interacts with AI services, all from within the database environment.

Lila: It sounds like a very cohesive ecosystem. And for developers or smaller teams wanting to experiment, is there an accessible entry point? I saw a mention of “Oracle Database 23ai Free.”

John: Yes, Oracle Database 23ai Free is a very important part of this. It’s a free, developer-focused version of their latest database that includes many of these advanced features, including the AI Vector Search capabilities. This lowers the barrier to entry significantly, allowing developers to explore and build applications leveraging these AI functionalities without upfront licensing costs. It’s a smart move to encourage grassroots adoption and experimentation, which, as we’ll discuss, is an area Oracle needs to cultivate.

The Developer Dilemma: Oracle’s Missing Piece?

John: This brings us to a crucial point, one I’ve highlighted for over a decade regarding Oracle: developer engagement. While their enterprise sales and CIO-level relationships are incredibly strong, and their current AI-on-data strategy is resonating well there, they’ve historically struggled to capture the hearts and minds of the broader developer community. The InfoWorld piece you might have seen, “The key to Oracle’s AI future,” touches upon this. Sustaining this newfound AI momentum may require Oracle to succeed where it has historically lagged.

Lila: That’s interesting. If they’re signing big enterprise deals and their revenue is up, why is grassroots developer enthusiasm so critical for a giant like Oracle? Don’t the CIOs make the ultimate decisions anyway?

John: CIOs do sign the checks, especially for large, established systems. However, in the cloud era, developers are increasingly the “new enterprise kingmakers.” They are the ones building the next generation of applications, experimenting with new technologies, and often influencing architectural choices from the ground up. If developers aren’t familiar with or excited about building on Oracle Cloud Infrastructure (OCI) or using Oracle’s AI database features for their *new* projects, Oracle risks missing out on future workloads that aren’t tied to its existing footprint. Growth from migrating existing customers is good, but capturing net-new innovation driven by developers is key for long-term dominance in a fast-evolving tech landscape.

Lila: So, it’s about future-proofing and expanding beyond their traditional customer base. What is Oracle doing to address this “developer gap,” especially in the context of AI?

John: They are making efforts. As we mentioned, Oracle Database 23ai Free is a step in the right direction. At their recent CloudWorld conferences, they’ve unveiled tools like Oracle Code Assist, an AI-powered programming assistant for Java developers on OCI, and enhancements to their Oracle Kubernetes Engine to better support cloud-native apps and AI workloads. They’re also providing more resources, labs, and documentation aimed at developers. For example, Oracle LiveLabs offers hands-on workshops like integrating Generative AI models with Autonomous Database.

Lila: Those sound like positive steps. But based on your earlier critiques, what more could they do to truly win over developers who might currently favor AWS, Azure, or Google Cloud for new AI projects?

John: It’s a multifaceted challenge. Firstly, continuing to improve the ease of onboarding and providing truly frictionless access is key. AWS became popular with developers partly due to the simplicity of swiping a credit card and getting started immediately with transparent, usage-based pricing. Oracle has made progress with OCI’s free tier and pricing, but they can go further. Generous free credits for startups, individual developer accounts, or open-source projects using OCI could significantly boost grassroots adoption.
Secondly, they need to foster a genuine community. This means a more visible and active presence in developer forums, at non-Oracle conferences, and through robust developer advocacy programs. Highlighting success stories of innovative startups “built on OCI” would also help change perceptions.
Thirdly, for technologies like MySQL, which Oracle owns, they might consider models that encourage broader community participation and governance, similar to what has made PostgreSQL so popular. MySQL has great engineering, but it needs a more vibrant, open community feel to compete effectively for developer mindshare in certain segments.

Lila: It sounds like a cultural shift as much as a technical one. Making their powerful tech easily accessible and appealing to individual developers, not just large enterprises.

John: Exactly. They need to ensure a developer can quickly provision a development instance of Database 23ai, load some data, and start calling an API or using a driver to build an AI-powered app within minutes or hours, not days or weeks involving complex procurement. If developers get a taste of the performance and unique capabilities, like Select AI or in-database vector search, without major hurdles, advocacy will build organically.

Use Cases & The Future Outlook: AI Where Your Data Lives

Lila: Let’s talk about some concrete use cases. We mentioned business analysts using Select AI for natural language queries. What other kinds of applications or benefits are we seeing from this “AI in the database” approach?

John: The applications are broad and impactful across various industries:

Enhanced Business Intelligence (BI): Beyond simple queries, imagine BI dashboards that proactively surface anomalies or insights discovered by AI models running continuously on live data. For instance, Oracle mentions AI-powered anomaly detection in meter data management for utilities, helping deliver fast, accurate meter data.
Smarter Customer Relationship Management (CRM): AI can analyze customer interaction data within the database to predict churn, personalize offers, or optimize sales strategies in real-time.
Intelligent Financial Systems: Fraud detection models can run directly against transaction data as it flows into the database, identifying suspicious patterns much faster.
Supply Chain Optimization: AI can analyze logistics data to predict disruptions, optimize inventory, or improve demand forecasting.
Personalized Healthcare: In a secure manner, AI could analyze patient data (with appropriate consents and privacy safeguards) to assist in diagnostics or treatment planning.
AI-Powered Application Features: Developers can build new application features that leverage in-database AI, such as semantic search for product catalogs, intelligent content recommendation, or automated data summarization.

The core idea is leveraging the “Oracle Autonomous Database Data Studio” features to quickly load data, transform it, generate business models, and find immediate insights and anomalies with simple no-code or low-code workflows, further empowered by AI.

Lila: So it’s not just about asking questions, but also about the database becoming more proactive and intelligent in how it helps businesses operate. What does the future roadmap look like for Oracle in this AI database space? Are they pushing further into specific AI capabilities?

John: The roadmap clearly points towards deeper integration and broader capabilities. We’ll likely see:

More Sophisticated In-Database AI Models: Expanding the types of machine learning models that can be trained and run directly within the database.
Enhanced AI Vector Search: Continuous improvements in performance, scalability, and the types of data that can be vectorized and searched.
Tighter OCI AI Service Integration: Seamless workflows between the database and the broader suite of OCI AI services, including vision, speech, language, and decision AI.
Multicloud AI Capabilities: Further enabling AI workloads on Oracle databases running in other clouds, like AWS, Azure, and Google Cloud, through their interconnect partnerships. This addresses data sovereignty and latency concerns.
Industry-Specific AI Solutions: Pre-built AI models and solutions tailored for specific sectors, leveraging Oracle’s deep industry knowledge from its applications business (e.g., finance, healthcare, retail).

The emphasis will remain on making it easier for enterprises to “bring AI to their data,” accelerating their AI initiatives without the massive overhead of data migration and separate AI infrastructure management.

Oracle vs. The World: Competitive Landscape

Lila: How does Oracle’s strategy stack up against competitors like AWS, Microsoft Azure, and Google Cloud, who also have strong database and AI offerings?

John: It’s a fascinating competitive dynamic. The hyperscalers (AWS, Azure, GCP) have very comprehensive AI platforms and a wide array of database services, including their own vector databases and LLM services. Their strength often lies in the breadth of their offerings and massive developer ecosystems.
Oracle’s differentiated play is its deep entrenchment in the enterprise with mission-critical database workloads. Their argument, as Ellison puts it, is that they already manage “the vast majority of the world’s valuable [enterprise] data.” So, their primary pitch is: “Your most important data is already here; let us help you activate its AI potential securely and efficiently where it lives.” This “home-field advantage” is compelling for existing Oracle customers.
While AWS and Azure pulled ahead of Oracle in overall database revenue a few years back largely due to cloud adoption, Oracle is now fighting back by making its database indispensable for AI on proprietary enterprise data. They are also, somewhat uncharacteristically for the Oracle of old, embracing multicloud, allowing Oracle Database to run on competitor clouds, which is a pragmatic move.

Lila: So, it’s a strategy of focusing on their core strength – the enterprise database – and making it AI-native, rather than trying to out-Amazon Amazon on breadth of cloud services? Is this “home-field advantage” enough to keep them competitive long-term?

John: It’s a very strong position for a significant segment of the market. However, as we discussed, long-term growth also depends on capturing new workloads and innovations. This is where developer engagement becomes critical again. If new AI applications are predominantly built on other platforms by developers who don’t consider Oracle, then Oracle’s “home field” might not expand as rapidly. So, they need both: to leverage their existing enterprise footprint and to attract the next generation of builders. Their investment in OCI data centers, now exceeding $21 billion this year, shows they are serious about having the infrastructure to support this growth.

Navigating Risks and Cautions

Lila: What are some of the risks or cautions for businesses considering going all-in on Oracle’s AI database strategy? Is there a danger of vendor lock-in, for example?

John: That’s always a valid concern with any deeply integrated platform.

Vendor Lock-in: The more an organization leverages Oracle-specific AI features within the database, the more dependent they become on Oracle’s ecosystem. While Oracle’s multicloud strategy offers some flexibility for database deployment, the AI services tightly coupled with the database might be harder to replicate elsewhere.
Execution Risk: Oracle’s success hinges on flawlessly executing its developer outreach and continuing to innovate at the rapid pace of the AI industry. Any stumbles here could cede ground to more agile competitors.
Complexity: While Select AI simplifies some interactions, managing an AI-enabled database environment, fine-tuning models, and ensuring responsible AI practices still requires significant expertise.
Cost: While Oracle Database 23ai Free offers an entry point, enterprise-scale deployments of Oracle’s advanced database features and OCI AI services will have associated costs that need careful evaluation.
Pace of AI Evolution: The AI landscape is evolving incredibly fast. Oracle needs to ensure its platform remains open enough to integrate new third-party AI innovations and standards as they emerge, not just its own.

Businesses need to weigh the benefits of this integrated approach – security, performance on existing data, streamlined operations – against these potential risks.

Lila: Those are important considerations. It sounds like a powerful offering, but one that requires careful planning and evaluation from the enterprise side.

John: Absolutely. The promise of “unlocking data with plain English” and getting immediate insights is incredibly attractive, but the underlying architecture and long-term implications need to be understood.

Expert Takes and Recent Developments

Lila: You’ve followed Oracle for a long time, John. What’s your overall take on this AI-centric shift? Does it feel like a genuine, sustainable transformation?

John: I believe it’s one of the most significant and promising strategic shifts Oracle has made in years. As the InfoWorld article “The key to Oracle’s AI future” aptly puts it, they found a “workaround” to the cloud dominance of others by focusing on AI where their data already resides. For decades, Oracle’s strength has been managing mission-critical enterprise data. Now, they’re not just storing it; they’re making it intelligent.
The criticism I and others had about Oracle being slow to embrace the cloud was valid. They let competitors gain significant ground. But this focus on bringing AI to existing enterprise data, within their secure infrastructure, is playing to their historical strengths while addressing a very current and pressing enterprise need. IDC’s analysis supports this, stating, “By embedding AI within the database, Oracle reduces data movement costs, improves security, and streamlines operations.”

Lila: And Larry Ellison’s bold claims about Oracle having “all the data”? Is that just typical Ellison swagger, or is there real substance there that gives them an edge?

John: It’s classic Ellison, for sure, and he’s a master showman. But there’s a kernel of truth that’s very relevant to their AI strategy. While “all the data” is an overstatement, Oracle databases do hold a colossal amount of the world’s structured, business-critical data – the systems of record for countless large enterprises. This isn’t just random internet data; it’s curated, validated operational data. Being able to apply AI directly to *that* data, within the systems that already manage it, is a powerful differentiator. The new Oracle Database 23ai is a testament to this strategy.

Lila: Looking at recent news, the Q4 2025 earnings definitely reflect market optimism. What are the key recent announcements that our readers should be aware of regarding Oracle’s AI and database direction?

John: The momentum is strong. Key things to note:

Oracle Database 23ai (“AI Database”): This is central, with its built-in AI Vector Search and features designed for AI/ML workloads. The “Oracle Database 23ai Free” version is crucial for developer access.
Oracle Autonomous Database Select AI: This capability, allowing natural language queries, is a game-changer for data accessibility and business intelligence.
OCI Generative AI Service: Provides access to leading LLMs that can be customized and integrated with enterprise data. They’re also supporting integration with models from other providers like Azure OpenAI and Google Gemini.
Continued OCI Expansion: Massive investment in new data centers globally to support the demand for OCI and AI services.
Multicloud Partnerships: Making Oracle Database available on AWS, Azure, and Google Cloud, acknowledging that customer data and workloads are often distributed.
Spring AI 1.0 GA with Oracle Vector Database Support: This is significant for the Java developer community, making it easier to build AI applications using popular frameworks with Oracle’s database capabilities.

These developments show a concerted effort to make Oracle’s database not just a repository but an active, intelligent platform for the AI era.

Quick FAQ on Oracle AI and Databases

Lila: This has been incredibly insightful, John. To help our readers digest all this, let’s do a quick FAQ round. First off, in simple terms, what is Oracle’s core AI database offering?

John: At its heart, it’s about embedding AI capabilities directly into the Oracle Database, primarily through Oracle Database 23ai (which includes AI Vector Search) and enhancing it with user-friendly tools like Oracle Autonomous Database Select AI for natural language querying. This is all supported by the broader Oracle Cloud Infrastructure (OCI) AI services.

Lila: Who is this primarily for? Is it just for existing Oracle enterprise customers?

John: While existing Oracle enterprise customers are a primary audience – as they can leverage AI on their vast existing data troves – Oracle is increasingly targeting developers and data scientists who want to build new AI-powered applications. Offerings like Oracle Database 23ai Free are aimed squarely at this group.

Lila: What’s the main benefit of using AI *within* the Oracle database as opposed to a separate AI platform?

John: The key benefits are:

Security: AI operates on data within its existing secure environment.
Reduced Data Movement: Eliminates the cost, complexity, and risk of moving large datasets to separate AI systems.
Leveraging Existing Data: Enables companies to gain insights from the valuable data they already manage in Oracle.
Faster Insights & Real-time Capabilities: AI can analyze live, transactional data.
Simplified Architecture: Reduces the number of moving parts in an AI solution.

Lila: So, can I really just ask my Oracle database questions in plain English now?

John: Yes, with Oracle Autonomous Database Select AI, you can. It uses generative AI to translate your natural language questions into SQL queries and fetch the answers, making data interaction much more intuitive for non-technical users.

Lila: And if I’m a developer wanting to try out these AI database features, is there a free way to get started?

John: Absolutely. Oracle Database 23ai Free is available for developers. It includes many of the advanced features found in the commercial versions, including AI Vector Search, allowing you to build and test AI-driven applications without initial licensing costs.

Final Thoughts and Where to Learn More

John: Oracle is undeniably making a powerful resurgence, driven by a smart strategy of integrating AI deep within its core database technology. They are leveraging their enterprise incumbency while aiming to become a key enabler for companies to use their own data with AI models. The challenge, as ever, will be sustained execution, particularly in broadening their appeal to the wider developer community who are the architects of future applications.

Lila: It’s an exciting time in the AI and data space! Oracle’s approach of bringing AI to where the data lives, rather than the other way around, seems incredibly practical for many businesses. It will be fascinating to watch how they balance their enterprise strengths with the need to cultivate that grassroots developer enthusiasm.

John: Indeed. For businesses with significant investments in Oracle, these developments offer a compelling path to harness AI. For the broader tech community, it signals that the database is becoming an even more critical and intelligent component of the modern data stack.

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