Build production-ready AI applications with Vela's vector capabilities, AI agent memory patterns, instant cloning for AI experiments, and enterprise-grade infrastructure. From RAG systems to conversational AI, scale AI workloads with confidence.
Everything you need for production AI applications in one platform
PostgreSQL with pgvector extension optimized for AI workloads: vector embeddings, similarity search, and hybrid queries combining structured and vector data.
Instantly clone production data for AI model training, experimentation, and testing without affecting live systems or incurring storage costs.
Purpose-built database patterns for AI agents: conversation memory, tool usage tracking, decision logs, and real-time context management.
Production-grade AI infrastructure with BYOC deployment, compliance controls, and enterprise security for AI applications at scale.
Common AI use cases optimized for Vela's architecture
Retrieval-Augmented Generation with vector embeddings and structured data
Persistent memory and context management for conversational AI agents
Real-time product and content recommendations using vector similarity
Intelligent search across documents, code, and knowledge bases
Proven patterns for different AI application types
Combine vector operations with traditional PostgreSQL features
Applications needing both structured data and vector search with strong consistency
Optimized schema for AI agent conversation and decision tracking
Conversational AI agents with persistent memory and audit requirements
Document storage with embeddings and metadata for retrieval systems
Knowledge bases, documentation search, and question-answering systems
Store and query across text, image, and other embedding types
Multi-modal AI applications with diverse data types and search requirements
How instant cloning accelerates AI development and testing
Access real production data without affecting live systems
Test with production-scale data in isolated environment
Experiment with real conversation data safely
Test performance optimizations without production impact
Native support for popular AI frameworks and tools
Popular framework for building AI applications with LLMs
Native PostgreSQL support with vector store capabilities
Data framework for connecting LLMs with external data
PostgreSQL vector store with metadata filtering
GPT models and embedding APIs for AI applications
Store embeddings and manage API usage with PostgreSQL
Open-source ML models and datasets
Store model outputs and fine-tuning data in PostgreSQL
Enterprise-grade governance for AI applications
Track model versions, training data, and performance metrics
Critical for regulated industries and enterprise AI
Protect sensitive data used in AI training and inference
Essential for customer data and proprietary information
Maintain records of AI decisions and reasoning
Required for regulated decisions and bias detection
Track AI model performance and data drift over time
Critical for maintaining AI model accuracy and reliability
How companies build production AI with Vela
HIPAA-compliant AI for medical document analysis with vector search capabilities
BYOC Vela deployment with pgvector for medical document embeddings and compliance controls
Semantic search across millions of legal documents with citation tracking
Vector embeddings for legal documents with instant cloning for case-specific analysis
AI agents with persistent memory across customer interactions
Agent memory architecture with conversation history and tool usage tracking
Step-by-step guide to building AI applications with Vela
Join AI companies using Vela to accelerate development, improve accuracy, and scale AI workloads. Get vector capabilities, instant cloning for AI experiments, and enterprise-grade infrastructure in one platform.