4 Digital Life Runs on Data Every seamless experience, from tapping your Suica card to receiving real-time updates, is powered by a reliable and fast database working behind the scenes.
6 YugabyteDB is the modern Shinkansen for your data: cloud-native, distributed, and built for zero downtime. It brings resilience to your most critical workloads without sacrificing performance.
Preview: Lessons from the Year 2027 11 For example: • All your apps will need updating! • LLM models will keep evolving! • Devs/ops need to do more with less. • Wait, is RAG in or out? MCP vs A2A? • Scale apps, but in seconds not months • No manual performance tuning • More options for users to switch apps Key principle: build to last design for change AI will accelerate things beyond our imagination – exponential growth
AI app modernization put a *huge* strain on 2027 software teams • Expectation: build and iterate AI apps, quickly ◦ Building with familiar Postgres saves resources • Massive amounts of re-architecting to be done ◦ Your architecture and tech stack requires flexibility • Success can be viral, will it scale? ◦ What worked in prototyping and small usage may not scale 14
“IDC predicts that in 2026, over one-third of organizations will be stuck in the experimental, point-solution phase of AI experimentation, requiring a shift of focus to enterprise use cases to deliver ROI.” - IDC, 2025 15 Some companies were left behind last year…
Application Lifecycle (waaaaaay back in 2025) Birth of a cloud-native app Innovation Getting ready for growth Dealing with infrastructure outages Planned and unplanned scaling 16
Application Lifecycle (in 2027) GenAI app Birth of a cloud-native app Innovation Getting ready for growth Dealing with infrastructure outages Planned and unplanned scaling 17
How we do it in 2027: Steps for building a modern GenAI app 18 Existing App + DB 01 Prepare Knowledge Base Collect and preprocess data for retrieval 02 Create Indexes Organize data into searchable indexes 03 Set Up MCP Server Deploy server to interface with the knowledge base 04 Advertise Server Capabilities Ensure server functionalities are known 05 Configure MCP Client Integrate server with AI environment 06 Create A2A Application Enable agents to communicate and collaborate +
Focus on the core: data and indexes 19 01 Prepare Knowledge Base Collect and preprocess data for retrieval 02 Create Indexes Organize data into searchable indexes 03 Set Up MCP Server Deploy server to interface with the knowledge base 04 Advertise Server Capabilities Ensure server functionalities are known Existing App + DB + 05 Configure MCP Client Integrate server with AI environment 06 Create A2A Application Enable agents to communicate and collaborate
AI-powered apps - not built to last, but designed for change! 20 Load data PgVector does not support generating embeddings) \COPY public.ybarticles FROM 'File.csv' Create tables with vector columns CREATE TABLE images( id INTEGER NOT NULL, title TEXT, image_vector vector(1536)); Create vector indexes with different distance functions CREATE INDEX ON images USING ybhnsw (image_vector vector_l2_ops); Search for vectors closest to an input SELECT id, title FROM images ORDER BY image_vector <=> '[-0.01,-0.02,0.01...]'::vector LIMIT 10; HNSW Index Different distance functions Vector Search
Choose your infrastructure wisely to accelerate change 22 Disadvantages of Standalone Vector Database Advantages of YugabyteDB as a Vector Database • API-based, prone to desync. • May offer proprietary indexing algorithms. • Lack full database features ACID compliance, row-level security). • Faster query times, but with potential network latency. • More cost-effective for smaller, proof-of-concept POC) projects. • Requires additional effort for data synchronization and management. • Built-in PostgreSQL data sync. • Supports multiple indexing algorithms (e.g., IVFFlat, HNSW • PostgreSQLʼs full ACID backup, and security. • Higher accuracy and often outperforms in QPS, especially with HNSW indexing • More economical at scale, leverages existing PostgreSQL infrastructure • Seamless integration with PostgreSQL data and familiar tools
Oh, sorry, in 2027 we say “ROG”, not RAG Retrieval-Optimized Generation ROG • Leans on the database to reduce (expensive) LLM utilization • Reduces latency, which 2027 users expect • Focused on your business data first and foremost • Requires multi-modal approach 23
PostgreSQL continues to grow in 2027 24 PostgreSQL is used by 49% of developers and is the most popular database for the second year in a row. Source: Stack Overflow 2025
PostgreSQL Compatibility and Cloud Native Architecture are Critical Can use PostgreSQL client drivers and psql shell Parse PG syntax - but execution is different Syntax Supports some advanced PG features - but they will work differently Feature Exactly like Postgres. Port over all existing apps, PG developers instantly at home. Runtime Wire How much Postgres compatibility? How cloud native (distributed) is the architecture? Low Medium High Can benefit from Postgres innovation (like pg_vector for genAI, QoS for multi-tenancy, etc) Can innovate on distributed, cloud native architecture (like zero downtime, global apps, fast auto-scaling, connection scaling, etc.) Can innovate on both dimensions PG Innovation Threshold Cloud DBMS Innovation Threshold Cannot deliver high data durability, availability, scale, best in class DR Delivers data durability and some vertical scale. Weak HA, horizontally scale, DR High data durability, availability, scalability, DR, multi-region 26
PostgreSQL Compatibility and Cloud Native Architecture are Critical Can use PostgreSQL client drivers and psql shell Parse PG syntax - but execution is different Syntax Supports some advanced PG features - but they will work differently Feature Exactly like Postgres. Port over all existing apps, PG developers instantly at home. Runtime Wire How much Postgres compatibility? How cloud native (distributed) is the architecture? Cannot deliver high data durability, availability, scale, best in class DR Low Delivers data durability and some vertical scale. Weak HA, horizontally scale, DR Medium High data durability, availability, scalability, DR, multi-region High Cloud DBMS Innovation Threshold PG Innovation Threshold 27
YugabyteDB makes PostgreSQL enterprise-ready 28 Postgres YugabyteDB High availability and DR Global database, xCluster Online upgrades and downgrades Yes Blue-green deployments Yes Security and SecOps Yes Scalable CDC Yes Connection pooling and scaling Built in Observability and Perf Tuning Agentic App Multi tenancy Yes (more coming) YugabyteDB is bringing capabilities available in commercial RDBMS to the PG world.
Six elements of Ultra Resilience for future apps In-region resilience Multi-region BCDR Zero-downtime operations Data protection Peak and freak events Grey failures 30
Immediate business value for modern apps Reduce Cloud Risk High Availability Reduce Support Costs Increase Customer Sat Increase Operational Efficiency Reduce Technical Debt Reduce Upgrade Stress Automate Backups 31
First data, now LLM…new laws and regulations id geo 1 US id geo 4 UK id geo 2 IND 3 IND • For regulatory and compliance purposes • Pin rows of a table or indexes to specific regions • Strong consistency • Low read and write latency in-region 32
35 It’s easy: Building AI apps with the Yugabyte MCP Server • Bridge the gap between your AI application (and its LLM) and your YugabyteDB data • Intuitive data exploration, without needing to write a single line of SQL • Provides a secure, extensible foundation for LLM-enhanced applications
Agentic AI for Ops: YugabyteDB Performance Advisor 36 • A single load chart to provide an overview of system activity • Anomaly correlation helps users to understand how detected issues relate to overall performance • Query-focused analysis allows users to see how specific queries contribute to database load • Interact with Performance Advisor using natural language chat
Ready now (not in 2027)! YugabyteDB extends pgvector indexing Native Vector Search powered by pgvector • In-database vector similarity search • Query vector and relational data • PostgreSQL compatibility • Fully ACID compliant • Fully open-source • Support for multiple distance functions, indexing methods Scalable & Extensible • Built natively for YugabyteDB, Vector index automatically sharded across nodes for horizontal scale and high throughput • Pluggable indexing design enables integration with new algorithms and libraries as the vector search ecosystem evolves • Integration with other Postgres extensions to enhance value Resilience & Global Distribution • Inherits all of YugabyteDBʼs fault tolerance, high availability, and geo-distribution for low-latency vector search across regions 37
AI-ready, multi-modal Distributed PostgreSQL for business-critical applications. Run modern business-critical applications, including GenAI apps, with built-in resilience, seamless scalability, and flexible geo-distribution. 38
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