Oracle Cloud Infrastructure (OCI) Generative AI is a fully managed service that provides enterprises with access to state-of-the-art, customizable large language models through a comprehensive API. As organizations increasingly adopt AI to transform their operations, OCI has positioned itself as a neutral, enterprise-focused platform offering unprecedented choice and flexibility in the generative AI space.
What is OCI Generative AI?
OCI Generative AI is designed to help enterprises seamlessly integrate advanced language comprehension capabilities into a wide range of applications. The service provides a complete end-to-end platform for building, customizing, and deploying LLM-powered applications at scale.
Key Capabilities:
- Access to pretrained foundational models from multiple leading AI providers
- Flexible fine-tuning with custom datasets on dedicated infrastructure
- Enterprise-grade security, compliance, and data sovereignty
- Integration with Oracle's broader AI ecosystem including databases and applications
- Support for both on-demand usage and dedicated hosting
The Model-Agnostic Advantage
Unlike other cloud providers that primarily push their proprietary AI solutions, Oracle has positioned itself as the "Switzerland of large language models"—offering choice, sovereignty, and enterprise-grade security without vendor lock-in.
Available Model Families (as of 2025)
Cohere Models:
The newest addition, Cohere Command A (03-2025), is the most performant Cohere chat model to date with better throughput than Command R and a 256,000 token context length. This model excels at tool use, agents, retrieval-augmented generation (RAG), and multilingual use cases.
- Command R (08-2024): Designed for RAG applications and enterprise use cases with a 128K context window
- Command R+ (08-2024): Enhanced version with deeper language understanding for complex, specialized use cases
- Embed models: English v3.0, Multilingual v3.0, and the latest Embed 4 for text and image embeddings
Meta Llama Models:
Oracle offers the complete Meta Llama 4 series, including the flagship Llama 4 Maverick with 17B active parameters from ~400B total (mixture-of-experts architecture). The more efficient Llama 4 Scout provides 17B active parameters from ~109B total parameters, optimized for smaller GPU deployments.
- Llama 3.3 (70B): Delivers better performance with improved reasoning and instruction-following
- Llama 3.2 Vision models: 90B and 11B parameter variants for multimodal understanding
- Llama 3.1: Available in 405B and 70B parameters for maximum capability
Google Gemini Models:
In the coming months, Google Gemini will become available on OCI Generative AI, making Oracle the only hyperscaler aside from Google Cloud Platform to offer the Gemini model as a managed service.
Available models include:
- Gemini 2.5 Pro: For complex reasoning and understanding
- Gemini 2.5 Flash: Optimized for speed and efficiency
- Gemini 2.5 Flash-Lite: Lightweight variant for resource-constrained scenarios
xAI Grok Models:
Oracle added xAI's complete Grok suite in 2025:
- Grok 4 and Grok 4 Fast: Latest generation models
- Grok 3 series: Including standard, Mini, and Fast variants
- Grok Code Fast 1: Specialized for code generation
OpenAI Models:
- gpt-oss-120b and gpt-oss-20b: Open-source-style OpenAI models
Core Features and Capabilities
1. Pretrained Foundational Models
OCI Generative AI provides immediate access to dozens of pretrained models across multiple categories:
Chat Models:
Ask questions and receive conversational, context-aware responses. Chat models keep the context of previous prompts, allowing natural multi-turn conversations where you can ask follow-up questions.
Text Generation:
Create text for any purpose including content creation, code generation, email drafting, and document summarization.
Embedding Models:
Convert text into vector embeddings for semantic search, recommendation systems, and similarity analysis. Light embedding models are smaller but faster at generating shorter vector representations—for example, English Light V3 generates 384-dimensional vectors while English V3 produces 1024 dimensions.
Rerank Models:
Input a query and a list of texts to get an ordered array with each text assigned a relevance score based on how well each text matches the query.
2. Flexible Fine-Tuning
One of the most powerful features of OCI Generative AI is the ability to fine-tune pretrained models with your own data, optimizing them for your specific domain and use cases.
Fine-Tuning Strategies:
Two fine-tuning strategies are offered for Cohere models: T-Few and Vanilla. For Vanilla fine-tuning, you can specify the number of layers to optimize, providing granular control over the adaptation process.
The Llama 3 models support Low-Rank Adaptation (LoRA) fine-tuning, which makes fine-tuning large models more efficient by adding smaller matrices that transform inputs and outputs rather than updating all original parameters.
Hyperparameter Control:
You can customize key hyperparameters before starting a fine-tuning job:
- Number of training epochs
- Learning rate
- Training batch size
- Early stopping patience and threshold
- Logging intervals for model metrics
Data Requirements:
Fine-tuning jobs require a labeled training dataset in JSONL format, with each example containing prompt and completion keys.
3. Dedicated AI Clusters
OCI Generative AI uses dedicated AI clusters—GPU-based compute resources that belong exclusively to your tenancy. These clusters provide:
For Fine-Tuning:
- Isolated compute resources sized specifically for training workloads
- Full control over training infrastructure
- Secure environment for proprietary data
For Hosting:
- Stable, high-throughput performance required for production use cases
- Private GPUs ensuring data never leaves your environment
- Zero-downtime scaling to handle changes in traffic volume
Cluster Types:
Different cluster unit types are available based on model size and performance requirements:
- Small Cohere/Generic units: For smaller models and lower throughput needs
- Large Generic units: For 70B parameter models
- Large Generic 2/4 units: For massive 405B parameter models with optimized cost-performance
4. Deployment Models
On-Demand (Pay-as-You-Go):
- Low barrier to entry, great for experimentation and proof-of-concept
- Pay only for what you consume, charged per character for input and output
- Dynamic throttling adjusts request limits based on model demand and system capacity to ensure fair access
- Available in multiple regions for pretrained models
Dedicated AI Clusters:
- Full control over compute resources
- Predictable performance and costs
- Required for fine-tuning and hosting custom models
- Ideal for production workloads with consistent traffic
5. OCI Generative AI Agents
In 2024, Oracle introduced OCI Generative AI Agents—a fully managed RAG (Retrieval-Augmented Generation) service that combines LLMs with enterprise search capabilities.
Agent Hub Features (Released March 2025):
Ready-to-use SQL Tool with self-correction for syntax errors, SQL execution, schema linking, in-context learning examples, and multi-dialect support including Oracle SQL and SQLite.
Enhanced RAG Tool with hybrid search combining keyword and vector search, improved multi-modal parsing for images and charts, custom instructions, multi-lingual support (French, Spanish, Portuguese, Arabic, German, Italian, Japanese), and cross-region access to vector data.
Key Capabilities:
RAG Agents connect to data sources, retrieve pertinent information, and enhance model responses with this data, ensuring more accurate and relevant outputs.
- Multi-turn conversational capabilities with context retention
- Integration with OCI Object Storage, OpenSearch, and Oracle Database 23ai
- Customizable workflows through tool orchestration
- Metadata ingestion and filtering for refined searches
- Support for multiple knowledge bases
Use Cases and Applications
Text Generation and Content Creation
Generate text for virtually any purpose:
- Content creation: Blog posts, marketing copy, product descriptions
- Email and communication: Professional emails, customer responses
- Documentation: Technical documentation, user guides, FAQs
- Creative writing: Stories, scripts, creative narratives
Semantic Search and Retrieval
Replace keyword-based searches with semantic searches to improve search results relevance. Use embedding models to:
- Build intelligent search systems that understand intent
- Create recommendation engines
- Implement similarity-based document retrieval
- Enable question-answering over document collections
Document Summarization
Generate executive summaries for documents that are too long to read, or summarize any type of text including support tickets, research papers, legal documents, and meeting transcripts.
Classification and Categorization
- Classify support tickets by department
- Categorize companies by sector
- Sentiment analysis on customer feedback
- Intent detection in user queries
Style Transfer and Rewriting
- Rewrite text in different styles, formats, or tones
- Paraphrase content for clarity or uniqueness
- Suggest grammatical improvements
- Adapt content for different audiences
Question Answering
Submit text such as documents, emails, and product reviews to the LLM, which reasons over the text and provides intelligent answers.
Enterprise Knowledge Management
Use RAG agents for customer support to retrieve information from knowledge bases and provide correct, contextually relevant answers, reducing response times and improving satisfaction.
Integration and Developer Experience
Access Methods
OCI Generative AI can be accessed through multiple interfaces:
- OCI Console Playground: Interactive testing environment with visual interface
- REST API: Full programmatic access for production applications
- OCI CLI: Command-line interface for automation and scripting
- SDKs: Native support for Python, Java, TypeScript, and Node.js
Framework Integration
LangChain Integration: OCI Generative AI is integrated with LangChain, making it easy to swap out abstractions and components necessary to work with language models.
LlamaIndex Support: Use LlamaIndex for building context-augmented applications and easily building RAG solutions or agents.
Both frameworks provide pre-built components and utilities for:
- Prompt templating and management
- Memory and conversation history
- Chain-of-thought reasoning
- Tool use and function calling
- Vector database integration
Tool Use and Function Calling
OCI Generative AI has tool support for pretrained chat models, enabling them to integrate with external tools and APIs to enhance responses and handle complex queries requiring external data.
With Tool Use, you can create API payloads based on user interactions and chat history to instruct other applications—for example, automatically categorizing and routing support tickets.
Security and Compliance
Data Sovereignty and Privacy
Dedicated AI clusters run LLMs in private OCI environments with no external access to data, implementing role-based access control (RBAC) and automated threat detection.
Key Security Features:
- Data Isolation: Models and data remain within your tenancy
- Encryption: Data encrypted at rest and in transit
- Access Controls: Fine-grained IAM policies and RBAC
- Audit Trails: Comprehensive logging for compliance
- Network Security: Private endpoints and VPN connectivity
- Compliance: Meets enterprise and regulatory requirements
Regional Availability
OCI Generative AI is hosted in multiple regions globally, including:
- US regions (Chicago, Phoenix, Ashburn)
- Europe (Frankfurt, London, Amsterdam)
- Asia Pacific (Tokyo, Mumbai, Seoul)
- Middle East (Dubai, Jeddah)
- Latin America (São Paulo)
- Sovereign regions: Oracle EU Sovereign Cloud for data residency requirements
Note: Not all models are available in every region—check documentation for specific model availability.
Content Moderation
OCI Generative AI provides content moderation controls, with optional safety modes that can be enabled during chat sessions to filter inappropriate content.
Advanced Features
Configurable Parameters
Fine-tune generation behavior with extensive parameters:
Sampling Controls:
- Temperature: Controls randomness (0.0-1.0)
- Top-k sampling: Limits selection to k most likely tokens
- Top-p (nucleus) sampling: Dynamic token selection based on cumulative probability
- Frequency penalty: Discourages token repetition
- Presence penalty: Encourages novel token usage
Reproducibility:
Seed parameter: Makes best effort to sample tokens deterministically—when assigned a value, the LLM aims to return the same result for repeated requests with the same seed and parameters.
Performance Benchmarks
OCI provides detailed benchmarks for different traffic scenarios:
The RAG scenario with very long prompts (2,000 tokens) and short responses (200 tokens) is benchmarked across different cluster types to help customers understand throughput and latency characteristics.
Benchmarks consider:
- Number of concurrent requests
- Prompt and response token counts
- Variance across requests
- Model-specific performance characteristics
Pricing and Cost Management
Free Tier:
Oracle offers a free pricing tier for most AI services as well as a free trial account with $300 in credits to try additional cloud services.
On-Demand Pricing:
- Pay per character processed (input and output)
- No minimum commitments
- Ideal for variable workloads and experimentation
Dedicated Clusters:
- Predictable monthly costs based on cluster size
- Optimal for consistent, high-volume workloads
- More cost-effective at scale compared to on-demand
Cost Optimization:
- Choose appropriate model sizes for your needs
- Use lighter models for simpler tasks
- Leverage caching for repeated queries
- Monitor usage with built-in analytics
Getting Started
Prerequisites
- OCI account (free tier available)
- Appropriate IAM permissions for Generative AI service
- Identity Domain for using AI Agents
- Subscription to desired region
Quick Start Steps
- Access the Service: Navigate to Analytics & AI → Generative AI in OCI Console
-
Choose Your Approach:
- Playground: Test models interactively without code
- API/SDK: Integrate into applications programmatically
- Fine-tune: Create custom models with your data
- Select a Model: Choose from available pretrained models
- Configure Parameters: Set temperature, max tokens, and other settings
- Start Building: Generate text, create embeddings, or build RAG applications
Example Use Case: Building a Support Chatbot
The OCI 2025 Generative AI Professional certification course covers building complete RAG-based AI pipelines, including vectorization, embedding techniques, indexing strategies, and similarity search within Oracle Database 23ai.
Architecture:
- Ingest support documentation into Oracle Database 23ai vector store
- Create embeddings using Cohere Embed models
- Deploy RAG Agent connecting to the vector store
- Implement chat interface using LangChain
- Enable multi-turn conversations with context retention
- Add tool calling for ticket routing and escalation
Oracle's Broader AI Ecosystem
OCI Generative AI is part of Oracle's comprehensive AI platform:
Oracle Database 23ai:
- Native AI Vector Search for similarity queries
- In-database LLM integration
- Support for RAG workflows
- Secure vector storage with encryption and access controls
MySQL HeatWave:
- In-database LLMs (HeatWave GenAI)
- Automated vector store
- Integrated generative AI capabilities
Oracle Fusion Applications:
Oracle embeds generative AI capabilities across its portfolio of cloud applications—including ERP, HCM, SCM, and CX—enabling customers to leverage innovations within existing business processes.
OCI Data Science:
OCI Data Science AI Quick Actions provide no-code access to open-source LLMs from providers like Meta and Mistral AI, enabling custom model development using frameworks like Hugging Face Transformers or PyTorch.
Certification and Learning
Oracle offers the OCI 2025 Generative AI Professional Certification designed for AI practitioners, developers, and data scientists.
Learning Path Covers:
- LLM Fundamentals: Architecture, transformer models, attention mechanisms
- Prompt Engineering: Designing and optimizing effective prompts
- Fine-Tuning Techniques: Domain adaptation and model customization
- OCI Generative AI Deep-Dive: Models, clusters, fine-tuning, security
- Building Applications: RAG workflows, vector databases, chatbot development
- Agent Development: OCI Generative AI Agents, knowledge base integration
Resources:
- Free tutorials and hands-on labs
- Coursera courses (free for Oracle University partners)
- Oracle MyLearn platform
- Comprehensive documentation and code samples
The Competitive Advantage
Why OCI Generative AI?
1. Model Choice and Flexibility
Unlike competitors focused on proprietary models, OCI offers access to the best models from multiple providers—Cohere, Meta, Google, xAI, and OpenAI—all through a unified platform.
2. Enterprise-First Approach
Built specifically for enterprise needs with strong security, compliance, data sovereignty, and seamless integration with Oracle's ecosystem.
3. Cost-Effective Infrastructure
Oracle's next-generation cloud infrastructure provides better price-performance than alternatives, with transparent pricing and flexible deployment options.
4. Database Integration
Unique tight integration with Oracle Database 23ai and MySQL HeatWave enables in-database AI workflows that competitors cannot match.
5. Sovereign Cloud Options
For organizations with strict data residency requirements, Oracle offers sovereign cloud regions ensuring data never leaves specific jurisdictions.
Looking Forward
The OCI Generative AI roadmap includes:
- Expanding model catalog with latest releases
- Enhanced Agent Hub capabilities
- Deeper integration with Oracle applications
- Improved fine-tuning efficiency and cost
- More regions and sovereign cloud options
- Advanced governance and observability tools
Agent Hub, a new OCI Generative AI feature designed to enhance the creation and deployment of AI agents, entered beta access in November 2024, providing streamlined ways to build, deploy, and manage advanced AI-powered agents.
OCI Generative AI represents Oracle's comprehensive, enterprise-focused approach to making large language models accessible, customizable, and production-ready. By offering:
- Wide model selection from leading AI providers
- Flexible customization through fine-tuning
- Dedicated infrastructure for performance and security
- Powerful RAG capabilities with Agents
- Deep ecosystem integration with databases and applications
Oracle has created a platform that addresses the real-world needs of enterprise AI adoption—security, sovereignty, choice, and integration—while maintaining the flexibility and cutting-edge capabilities that AI applications demand.
Whether you're building chatbots, implementing semantic search, creating content generation tools, or developing complex multi-agent systems, OCI Generative AI provides the foundation for enterprise-grade AI applications.
Are you using OCI Generative AI in your organization? What use cases are you exploring? Share your experiences and questions in the comments below
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