RelytONE：All In One Postgres

Relyt ONE feels like the future of Postgres. It ships everything — transactions, analytics, vector, full-text, graph,Time-Series,GIS — into a single, serverless engine. Unified, simple, free.

As a product architect with over a decade in the database trenches—scaling systems for everything from fintech unicorns to LLM stars—I’ve seen the industry pivot hard toward AI. What started as siloed experiments with vector embeddings and RAG pipelines has exploded into full-blown agentic architectures that demand more from our data layers. Today, in late 2025, we’re not just storing data; we’re orchestrating it for autonomous agents that reason across modalities, handle real-time streams, and deliver insights without the ops overhead that used to keep teams up at night. That’s why I’m thrilled to pull back the curtain on Relyt ONE, the serverless, PostgreSQL-compatible database we’ve built from the ground up for this exact moment.

I’m Philip, co-founder of DataCloud Tech, and over the past six months, we’ve watched hundreds of startups and AI teams ditch their fractured stacks—think Elasticsearch for search, DuckDB for analytics, Redis for caching—in favor of Relyt ONE. It’s now powering over 200 million AI data queries daily, from RAG services in e-commerce chatbots to intelligent agents parsing multimodal feeds in healthcare diagnostics. In a world where MIT Sloan is calling out agentic AI as the inescapable trend for 2025, and vector databases are finally facing scrutiny for reliability issues in production RAG apps , Relyt ONE isn’t just another tool—it’s the unified engine that lets you build AI apps that scale without breaking the bank or your sanity.

The AI Data Crunch: Pains That No Longer Need to Be

If you’re knee-deep in building RAG pipelines, agentic workflows, or BI dashboards laced with LLMs, you know the drill. Traditional setups fracture your toolchain: one database for vectors, another for analytics, a cache layer to paper over latency spikes. Queries drag on while your dashboards bleed red, costs balloon from overprovisioned clusters (hello, 3 a.m. alerts), and every architecture tweak means refactoring 80% of your codebase. It’s not just inefficient—it’s a creativity killer.

Add to that the 2025 reality: AI workloads aren’t predictable anymore. Agentic systems, as OpenAI’s o1 models and Microsoft’s Copilot agents demonstrate, spike erratically with multimodal inputs—text, images, audio, even sensor streams from edge devices. Vector embeddings fail silently on bad data, multimodal search demands hybrid retrieval across formats, and serverless expectations mean no one wants to manage infra anymore. Per recent InfoQ trends, data engineering teams are scrambling to blend HTAP (hybrid transactional/analytical processing) with vector capabilities for real-time AI . SMEs and indie devs can’t afford the polyglot persistence nightmare that’s become the norm.

How Relyt ONE Delivers the AI-Native Fix

We didn’t set out to build another database; we built the one AI devs have been whispering about in Slack channels—the one that feels like it was designed yesterday, for tomorrow’s workloads. Relyt ONE collapses multimodal search, analytics, and serverless scaling into a single, PostgreSQL-compatible engine. Here’s the breakdown:

All-in-One Multimodal Engine: Forget stitching tools together. Relyt ONE handles full-text, vector similarity, JSON documents, and even GIS for spatial AI apps. Query billion-scale vectors in milliseconds using HNSW indexing, all while blending modalities—like text queries pulling image embeddings or audio clips feeding into RAG for voice agents. This isn’t bolted-on; it’s core, aligning with 2025’s push toward multimodal RAG that integrates text, images, and audio for hyper-personalized outputs.

Postgres Ecosystem, Zero Friction: Full SQL compatibility means your existing queries, ORMs, and tools migrate seamlessly—no vendor lock, no rewrite hell. Leverage pgvector-like extensions for embeddings generated on-the-fly with pgai, or tap into graph support for semantic relationships in agentic flows. As Databricks’ Neon acquisition underscores, Postgres is the de facto for AI in 2025, and Relyt ONE amplifies it with built-in GPU acceleration for in-database ML ops.

Serverless by Design: Instant provisioning, auto-scaling to zero, and pay-as-you-go economics that crush overprovisioning. No more sizing clusters for peaks—Relyt ONE handles agentic spikes while keeping costs 60%+ lower. In a year where serverless DBaaS is exploding to $23B markets with AI-native features, this means unbeatable efficiency for variable AI loads.

The result? Real-world wins: 70% latency drops in production RAG apps, seamless scaling for multimodal agents, and a forever-free plan with unlimited compute for solos and small teams prototyping the next big thing.

Echoes from the Field: Why Devs Can’t Stop Talking About It

What gets me most excited aren’t the benchmarks—it’s the stories. A seed-stage AI startup building voice-enabled diagnostics swapped their ES-DuckDB mess for Relyt ONE and cut query times from seconds to sub-100ms, freeing their lone data engineer for model tuning. An SME in logistics now runs geospatial-vector hybrids for predictive routing agents, all without a dedicated DBA. As Towards Data Science notes, 2025’s vector DB reckoning is pushing teams toward reliable, all-in-one platforms like this—and Relyt ONE is delivering.

We’re not alone in seeing the shift. With trends like real-time RAG and hybrid semantic-graph search dominating , and Postgres extensions like pgml enabling in-DB ML , the ecosystem is converging on unified, AI-first data layers. Relyt ONE leads that charge, optimized for the unstructured data stacks.

TL;DR: Ready for the AI Era, Today Whether you’re an SME streamlining BI or a dev hacking the next agentic breakthrough, Relyt ONE is your unfair advantage. Let’s build the future—together.

PostgreSQL-compatible. Multimodal search + analytics + serverless.

Built for AI. Optimized for agents. Free to start.

https://relytone.data.cloud/