What is the Difference Between Oracle Database 23c and 23ai?

Oracle Database has been the backbone of enterprise applications for decades, powering mission-critical systems with reliability and performance. With the release of Oracle Database 23c and the introduction of Oracle Database 23ai, many developers, DBAs, and businesses are curious about the differences between these two versions. While both share the same foundation, 23ai introduces a new era of AI-powered database features, advanced analytics, and built-in support for emerging workloads such as vector data and graph analysis.

In this tutorial, you will learn what sets Oracle Database 23c apart from Oracle Database 23ai, including architectural updates, new AI features, developer-focused enhancements, and practical use cases.

Overview of Oracle Database 23c

Oracle Database 23c, also called Oracle Database 23c Long-Term Support (LTS), is a significant release designed to be the foundation for the next decade of database applications. It emphasizes stability, security, and innovation for transactional and analytical workloads.

Key pillars of Oracle Database 23c include:

• Data consistency and scalability: Strong ACID compliance, distributed transactions, and horizontal scaling for cloud environments.
• JSON Relational Duality: A new feature that lets developers store JSON and relational data together seamlessly without duplicating logic or storage.
• Property Graph and RDF Graph improvements: Enhanced graph data management for complex relationships and AI use cases.
• Blockchain tables: Tamper-resistant tables that maintain an immutable ledger for compliance and security.
• Automatic indexing and zone maps: Smarter optimization to improve query performance without manual tuning.

These features make 23c a robust platform for OLTP, analytics, and hybrid applications that require both performance and reliability.

Overview of Oracle Database 23ai

Oracle Database 23ai builds upon the foundation of 23c but shifts focus toward artificial intelligence integration and generative AI support. It is positioned as the industry’s first “AI database,” designed to help organizations embed AI into everyday applications without needing complex third-party ML infrastructure.

Notable aspects of 23ai include:

• Integrated AI Vector Search: Native support for vector data types (VECTOR) and high-performance similarity search.
• In-database machine learning (AutoML): Automated model training, selection, and deployment directly in the database, eliminating the need to move data.
• Enhanced JSON relational duality with AI functions: Combine relational and unstructured data with built-in machine learning models.
• Support for Generative AI: Integrations that allow applications to call LLMs (Large Language Models) alongside relational and vector data queries.
• Improved developer experience: SQL enhancements, new JSON relational syntax, and REST APIs for AI-driven workloads.

23ai takes Oracle’s Autonomous Database capabilities further, making it easier to build AI-native applications on the same platform used for traditional enterprise data.

Key Differences Between Oracle Database 23c and 23ai

While the two versions share the same code base, there are important distinctions:

• Release Type:
  • 23c: A long-term support (LTS) release, supported for many years, ideal for enterprises seeking stability.
  • 23ai: A feature-focused release, showcasing Oracle’s AI and machine learning innovations. It builds on 23c but emphasizes new AI-driven capabilities.
• AI and Machine Learning Features:
  • 23c: Provides JSON Relational Duality, improved SQL, blockchain tables, and developer-friendly syntax.
  • 23ai: Adds AutoML, vector data type (VECTOR) support, vector search, and generative AI integrations directly inside the database.
• Use Cases:
  • 23c: Best for general enterprise applications, transactional systems, and analytics requiring long-term stability.
  • 23ai: Designed for AI-powered applications, recommendation engines, semantic search, fraud detection, and LLM-based solutions.
• Support Lifecycle:
  • 23c: Long-term support until 2036, ensuring stability for mission-critical workloads.
  • 23ai: May follow an innovation release model, focusing on delivering cutting-edge features with shorter support cycles before being merged into a future LTS release.

Example 1: JSON Relational Duality in Oracle Database 23c

One of the flagship features of 23c is JSON Relational Duality, which allows you to work with JSON documents and relational tables interchangeably.

Step 1: Create a table with a JSON column

CREATE TABLE prc_customers (
   customer_id     NUMBER PRIMARY KEY,
   customer_data   JSON
);

Step 2: Insert sample data

INSERT INTO prc_customers VALUES (
   101,
   '{
      "name": "Alice Johnson",
      "email": "alice@example.com",
      "tier": "Gold",
      "preferences": {
         "newsletter": true,
         "offers": "weekly"
      }
   }'
);

INSERT INTO prc_customers VALUES (
   102,
   '{
      "name": "Bob Smith",
      "email": "bob@example.com",
      "tier": "Silver",
      "preferences": {
         "newsletter": false,
         "offers": "monthly"
      }
   }'
);

COMMIT;

Step 3: Query relational and JSON data together

SELECT c.customer_id,
       c.customer_data.name AS customer_name,
       c.customer_data.tier AS membership,
       c.customer_data.preferences.newsletter AS subscribed
FROM   prc_customers c;

Result:

This shows how 23c allows developers to store and query JSON documents like native relational data, removing the need to maintain separate NoSQL systems.

Example 2: Vector Data and AI in Oracle Database 23ai

Oracle Database 23ai introduces the VECTOR data type and vector search functions. This is crucial for AI use cases such as recommendation systems, semantic search, and generative AI applications.

Step 1: Create a table with vector data

CREATE TABLE prc_documents (
   doc_id     NUMBER PRIMARY KEY,
   content    CLOB,
   embedding  VECTOR(4)   -- 4-dimensional vector for simplicity
);

Step 2: Insert sample data

INSERT INTO prc_documents VALUES (
   1,
   'Oracle Database 23ai introduces native vector search for AI applications',
   TO_VECTOR('[0.11, 0.85, 0.32, 0.74]')
);

INSERT INTO prc_documents VALUES (
   2,
   'Machine learning models can run directly inside Oracle Database',
   TO_VECTOR('[0.15, 0.80, 0.30, 0.70]')
);

INSERT INTO prc_documents VALUES (
   3,
   'Traditional relational and JSON data combined for flexible queries',
   TO_VECTOR('[0.90, 0.10, 0.20, 0.05]')
);

COMMIT;

Step 3: Run a vector similarity search

Suppose you want to find the document most similar to a query vector [0.10, 0.82, 0.29, 0.75]:

SELECT doc_id, content,
       VECTOR_DISTANCE(
           embedding,
           TO_VECTOR('[0.10, 0.82, 0.29, 0.75]'),
           COSINE
       ) AS similarity_score
FROM   prc_documents
ORDER BY similarity_score
FETCH FIRST 2 ROWS ONLY;

Result:

Here, Oracle 23ai directly computes vector similarity using cosine distance inside SQL, making it much easier to power search or AI applications without external systems.

Developer Experience Enhancements

Both 23c and 23ai focus on improving developer productivity, but 23ai goes further by embedding AI-ready SQL constructs and AutoML pipelines.

In 23c, developers gain features such as:

• SQL Domains for consistent data semantics.
• Native JSON data handling with relational duality.
• Operational property graphs for relationship-driven queries.

In 23ai, developers also get:

• Vector data types and similarity operators.
• Simplified integration with AI models.
• Extended PL/SQL support for invoking machine learning pipelines.

This shift demonstrates Oracle’s goal to make the database not only a data store but also an AI runtime environment.

Choosing Between Oracle 23c and 23ai

Your choice depends on use case, support requirements, and innovation needs:

• Choose Oracle Database 23c if:
  • You need a stable, long-term support database.
  • Your workloads are primarily transactional and analytical without immediate AI integration needs.
  • You want enterprise-grade reliability and decades of support commitments.
• Choose Oracle Database 23ai if:
  • You are building AI-driven applications such as recommendation systems, semantic search, or generative AI integrations.
  • You need vector search and AutoML features built directly into the database.
  • You want early access to cutting-edge Oracle AI capabilities, even if support timelines are shorter.

Conclusion

Oracle Database 23c and Oracle Database 23ai are closely related, but they serve different purposes. Oracle 23c is the stable long-term support release, perfect for critical enterprise workloads that demand consistency and long lifecycle support. Oracle 23ai, on the other hand, extends 23c’s capabilities into the realm of AI, vector search, and generative AI integration, making it an ideal choice for organizations preparing for AI-driven innovation.

In short, use 23c for stability and 23ai for innovation. Both are part of Oracle’s broader vision of a converged database that combines relational, JSON, graph, blockchain, and AI workloads into a single, unified platform.