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EDB Postgres AI Analytics
Bring analytics and AI to where your data lives — EDB Postgres AI Analytics runs petabyte-scale analytics and agentic AI (vectors, RAG) on your operational Postgres data, with no ETL lag, full sovereignty and open-source flexibility.
How it’s rated
Full scoreboard ↓Quick answer
EDB Postgres AI Analytics is the analytics-and-AI capability of the EDB Postgres AI platform — bringing petabyte-scale analytics and agentic AI to your operational Postgres data, with full sovereignty and open-source flexibility. Its defining idea is ‘AI (and analytics) to where your data lives,’ rather than the reverse. The conventional pattern moves operational data out to separate systems — a data warehouse for analytics, a vector database for AI — continuously, with all the ETL, latency, cost, complexity and data-governance risk that data movement involves. EDB Postgres AI Analytics inverts it: run petabyte-scale analytics (the WarehousePG lineage) and AI workloads (vector search via pgvector, and agentic AI) on your Postgres operational data directly, where it already is. That means fresher insights (no ETL lag), lower cost and complexity (no separate warehouse or vector DB to run and sync), and — critically — full data sovereignty (your sensitive data stays in one place, in your control, not scattered across multiple systems and clouds). Per the AI-security era, keeping AI on your operational data rather than copying it out is also more secure and governable. For organisations that want analytics and AI on their real operational data — with sovereignty and openness — this is EDB's answer.
The EDB Postgres platform family
This page covers AI Analytics — the analytics & AI layer. The rest of the portfolio:
Quick facts
30-second orientation- Product
- EDB Postgres AI Analytics
- Vendor
- EDB (EnterpriseDB) — part of IBM
- What
- Petabyte analytics + agentic AI on Postgres
- Key idea
- Analytics & AI to where your data lives
- Analytics
- Petabyte-scale (WarehousePG lineage)
- AI
- Vectors (pgvector) + agentic AI
- Avoids
- ETL, data movement, separate warehouse/vector DB
- The edge
- Full sovereignty + open-source flexibility
- Part of
- The EDB Postgres AI platform
- In India via
- TechBag — quotes, PoCs, GST invoicing, Tier-1 support
Understand analytics & AI on Postgres before you buy it
Most product pages skip this. We start here — so you buy a capability, not a buzzword.
What is EDB Postgres AI Analytics?
The analytics-and-AI capability of EDB Postgres AI — petabyte-scale analytics and agentic AI on your operational Postgres data, with sovereignty.
Analytics and AI to where your data lives — not the reverse.
Copy data out vs analytics & AI where it lives — the honest table
What consolidation actually replaces, dimension by dimension.
| Dimension | Warehouse + vector DB + ETL | EDB Postgres AI Analytics (in-place) |
|---|---|---|
| The pattern | Move data to the AI/warehouse | AI/analytics to the data |
| Freshness | ETL lag (stale) | Live operational data |
| Data location | Scattered (DB+warehouse+vector) | One place, in your control |
| Sovereignty | Data everywhere | Data stays home (DPDP) |
| Systems | Warehouse + vector DB + ETL | One Postgres platform |
| Cost | Multiple systems + sync | Consolidated |
| AI grounding | On a copy | On real, live data (RAG) |
| Security | Bigger attack surface | Data not copied out |
AI-on-your-data with sovereignty — for the largest specialized analytics/vector needs, Snowflake/Pinecone go deeper.
The five pieces of the platform
Vendors love diagrams; buyers need to know what they’re actually operating. Here’s the whole platform, demystified.
Petabyte Analytics
Analytical workloads at petabyte scale on Postgres (WarehousePG lineage) — query large data volumes without moving data to a separate warehouse.
Vector / pgvector
Vector search via pgvector — the foundation for semantic search, RAG and AI workloads, running in Postgres on your data.
Agentic AI
Agentic AI on your operational data — AI agents that reason and act where the data already lives, not on a stale copy.
No Data Movement
Run analytics and AI on the operational database itself — no ETL, no separate warehouse or vector DB to sync, no data scattered.
Sovereign & Open
Full data sovereignty (data stays in one place, in your control) and open-source flexibility — runs anywhere, no lock-in.
One agent on every machine, one console over all of them — modules attach without a second operational world.
Twelve capabilities. Analytics, AI, sovereign.
EDB Postgres AI Analytics runs petabyte analytics and agentic AI on your operational data — fresh, sovereign, no data movement.
Petabyte Analytics
Analytical workloads at petabyte scale on Postgres — query big data where it lives, no separate warehouse.
No ETL / Data Movement
Analyse and run AI on operational data directly — no continuous copying to a warehouse or vector DB.
Fresh Insights
Insights on live operational data — no ETL lag between the data and the analysis.
Vector Search (pgvector)
Vector/semantic search on your data — the foundation for RAG and AI, in Postgres.
Agentic AI
AI agents that reason and act on your operational data — where it already is.
RAG-Ready
Retrieval-augmented generation on your own data — AI grounded in your real business data.
Full Data Sovereignty
Data stays in one place, in your control — not scattered across systems and clouds. DPDP-friendly.
Open-Source Flexibility
Open Postgres — runs anywhere (on-prem, any cloud, hybrid), no lock-in.
Secure AI-on-Data
Keeping AI on your operational data (not copied out) is more secure and governable — the AI-security era.
One Data Platform
Analytics and AI on the same platform as your operational data — fewer systems, less complexity.
Part of EDB Postgres AI
The analytics-and-AI layer of the EDB Postgres AI multi-model platform.
Lower Cost & Complexity
No separate warehouse/vector DB to run and sync — lower cost and operational complexity.
Watch EDB Postgres AI Analytics in action
Analytics and AI on your Postgres data, and why data-where-it-lives wins.
Introducing EDB Postgres AI
EDB's sovereign data-and-AI platform built on Postgres, introduced by EDB.
EDB Postgres AI: Enterprise-Grade Postgres
What makes EDB's Postgres enterprise-grade — HA, security, support.
Postgres Architect Answers AI & Data Questions
An EDB Postgres architect fields real AI and data questions.
Want a live, India-context walkthrough on your own fleet?
Book a guided demo →Stop copying data to the AI. Run AI where it lives.
Here’s what genuinely sets EDB Postgres AI Analytics apart from the alternatives.
Bring AI and analytics to your data, not the reverse
The conventional pattern moves your operational data out to separate systems continuously — a warehouse for analytics, a vector database for AI — with all the ETL, latency, cost, complexity and governance risk that data movement involves. EDB Postgres AI Analytics inverts it: run petabyte-scale analytics and AI workloads on your Postgres operational data directly, where it already lives. Doing the analysis and AI where the data is, rather than copying the data to where the analysis is, is simpler, fresher, cheaper and more secure — and it's the defining idea here.
Fresh insights, no ETL lag
When analytics and AI run on a separate copy of your data, there's always lag — the ETL pipeline that syncs operational data to the warehouse or vector DB introduces delay, so your insights and AI work on data that's minutes, hours or days stale. Running analytics and AI on the operational Postgres data directly means the analysis is on live, current data — fresher insights and AI grounded in what's actually happening now, not a delayed copy. For time-sensitive decisions and real-time AI, that freshness matters.
Sovereignty: your data stays in one place
Every time you copy operational data out to another system, you scatter it — your sensitive data now lives in the operational DB, the warehouse, the vector DB, possibly across clouds — multiplying the attack surface, the compliance scope and the governance burden. Running analytics and AI on the operational data itself keeps it in one place, in your control. Under India's DPDP Act and data-residency rules, that sovereignty — data not scattered, kept in-country, under your governance — is a real, increasingly-required advantage.
AI grounded in your real data (RAG)
The most valuable enterprise AI is grounded in your own business data — retrieval-augmented generation (RAG) and agentic AI that reason over your real operational information, not just a general model. EDB Postgres AI Analytics provides vector search (pgvector) and agentic AI on your Postgres data, so you can build AI applications grounded in your actual business data, running where that data lives. As organisations move from generic AI to AI on their own data, running it on the operational database is the natural, powerful pattern.
Fewer systems, lower cost and complexity
Maintaining a separate analytics warehouse and a separate vector database — each needing infrastructure, syncing and operational care — is expensive and complex. Consolidating analytics and AI onto the same Postgres platform as your operational data means fewer systems to run, less data movement to orchestrate, and lower cost and complexity overall. One platform doing operational, analytical and AI work is a genuine simplification over a sprawl of specialized systems each fed by ETL.
The honest positioning
EDB Postgres AI Analytics is a strong expression of the AI-on-your-data pattern, with real sovereignty and openness advantages. Dedicated analytics warehouses (Snowflake, Databricks, BigQuery) and vector databases (Pinecone, Weaviate) go deeper on their specialties, and for the largest, most specialized analytics or AI workloads, they may fit. EDB's edge is running analytics and AI where your operational data already lives, with sovereignty and no data movement — ideal when freshness, governance and simplicity matter more than specialist depth. TechBag scopes the trade-off, in INR/GST.
The numbers behind the platform
What your EDB Postgres AI Analytics journey looks like
AI/analytics scoping
Your analytics and AI goals, your operational Postgres data, and your sovereignty needs. TechBag scopes it free.
AI-on-data PoC
Run analytics and a pgvector/RAG AI workload on your operational Postgres data — prove fresh, sovereign, no-movement analytics and AI.
Consolidation design
Design consolidating analytics/AI onto Postgres; scope retiring a separate warehouse/vector DB; plan governance.
AI-on-your-data steady state
Analytics and AI on your operational data, fresh and sovereign, fewer systems. TechBag models it in INR/GST.
Trusted across regulated industries in 100+ countries
The review scoreboard
Modelled on Gartner Peer Insights structure. *Counts and breakdowns are illustrative pending verified review collection.
Quick poll — what’s driving your evaluation?
Talk to an advisor“Running AI on our operational data — not copying it out to a separate vector DB — was transformative. Fresher insights, one place for our data, full sovereignty. AI to the data, not data to the AI.”
“No ETL lag — our analytics run on live operational data. For time-sensitive decisions, the freshness beat our old warehouse-with-a-nightly-sync completely.”
“Sovereignty was the clincher — under DPDP, keeping our sensitive data in one place, in our control, not scattered across a warehouse and vector DB and clouds, mattered enormously.”
“pgvector and agentic AI on our real business data let us build grounded RAG applications — AI on what's actually happening, not a general model or a stale copy.”
“We retired a separate warehouse and a vector DB — analytics and AI on the same Postgres as our operational data. Fewer systems, less ETL, lower cost.”
“For the largest specialized analytics we'd still weigh Snowflake — but for AI-on-our-operational-data with sovereignty and no movement, EDB fit. Scope specialist depth vs data-where-it-lives.”
“Keeping AI on our operational data rather than copying it out is genuinely more secure and governable — in the AI-security era, that mattered to our CISO.”
“Petabyte analytics on Postgres, where our data already lives — no separate warehouse to feed. The consolidation was real.”
Where everyone sits — the grids
Analyst firms bury this view behind paywalls, and G2 retired its Grid. So here’s TechBag’s synthesis of the analytics & AI on Postgres market — tap any vendor to see why it sits where it does.
TechBag AI-on-Data Grid
Execution strength vs product vision — the classic market map, minus the paywall.
Analytics + AI on your Postgres data, sovereign — this page.
Data-Where-It-Lives × Sovereignty
The grid nobody publishes — running analytics/AI where the data lives vs sovereignty/openness (no data scattered).
Data-where-it-lives + sovereign — the corner it fills.
Positions are TechBag’s illustrative synthesis of public review-platform data and vendor documentation — not a reproduction of any analyst graphic. Verify before relying on it.
EDB Postgres AI Analytics vs the field
The cloud warehouses and vector databases — honest lanes; the edge is analytics/AI on your data, sovereign and open.
| Dimension | EDB Postgres AI Analytics | Snowflake / Databricks | Pinecone / Weaviate | BigQuery | Postgres + DIY |
|---|---|---|---|---|---|
| Approach | Analytics + AI on your Postgres data | Cloud data warehouse | Vector DB | Cloud warehouse | DIY on Postgres |
| Data movement / freshness | None / live | ETL required | Sync required | ETL required | DIY |
| Sovereignty / openness | Data stays, runs anywhere | Cloud | Cloud/self | Google cloud | Open (DIY) |
| AI (vectors, agentic) | pgvector + agentic | Growing | Vector specialist | Vertex AI | pgvector (DIY) |
| Best fit | AI/analytics on operational data, with sovereignty | Largest specialized analytics | Dedicated vector needs | Google-cloud analytics | DIY-capable teams |
Which analytics/AI approach fits you?
Honest fit signals — because the fastest way to lose your trust is to pretend one product wins every scenario.
Choose EDB Postgres AI Analytics if…
- ✓You want analytics and AI on your operational data (no movement)
- ✓Fresh insights (no ETL lag) matter
- ✓Data sovereignty and openness are priorities (DPDP)
- ✓You want to consolidate away from a warehouse + vector DB sprawl
Choose Snowflake / Databricks if…
- ✓You need the largest, most specialized cloud analytics
Choose Pinecone / Weaviate if…
- ✓You need a dedicated, specialized vector database
Choose BigQuery if…
- ✓You're Google-cloud-centric and want its warehouse
Postgres + DIY if…
- ✓You have the engineering to assemble pgvector/analytics yourself
What does a data-movement sprawl cost you?
Drag the sliders (count data pipelines/systems scaled here as instances; IT-hour cost as loaded rate). Estimates assume ~4 hours per pipeline per year of ETL, sync and multi-system operational overhead, with ~60% removed by running analytics and AI on the operational data directly — the reduced complexity, fresher insights and sovereignty (avoided data scatter) are the wins. Illustrative.
Loaded cost = salary + overheads per productive hour. Illustrative only — your TechBag quote models actual device counts and modules.
Three ways to consume it
EDB Postgres AI Analytics prices within the EDB Postgres AI platform. TechBag models it vs a warehouse + vector DB sprawl, in INR/GST.
AI Analytics
Best for AI-on-data
- Petabyte analytics on Postgres
- pgvector + agentic AI
- No data movement, fresh insights
+ Sovereignty
Best for governance (DPDP)
- Data stays in one place
- Runs anywhere, no lock-in
- More secure (no data copied out)
+ Platform
Best for consolidation
- On the EDB Postgres AI platform
- Retire warehouse + vector DB
- TechBag scopes the mix
Buy it for less — TechBag pricing beats list
Whatever the list prices above, TechBag negotiates a significantly better deal — with GST-compliant INR invoicing and local support. Ask us for your discounted quote.
Get an India-ready quote
Tell us your device counts and current tools — we’ll model it against what you spend today.
The 8 questions to ask every analytics & AI vendor
Take this into your next vendor call — including ours.
Confirm analytics and AI run on your operational Postgres data directly — no ETL to a warehouse or vector DB.
Test that insights and AI are on live data — no ETL lag between the data and the analysis.
Confirm data stays in one place, in your control — not scattered across systems and clouds (DPDP-relevant).
Test pgvector and a RAG/agentic AI workload on your real data — grounded AI, where the data lives.
Confirm analytics scale to your data volumes on Postgres — no separate warehouse needed.
Identify the separate warehouse/vector DB this could replace — model the simplification and saving.
For the largest specialized analytics/vector needs, weigh Snowflake/Pinecone — EDB's edge is data-where-it-lives + sovereignty.
Model TCO vs a warehouse + vector DB + ETL sprawl — TechBag quotes it in INR/GST.
Questions buyers ask
Ready to evaluate EDB Postgres AI Analytics?
Scope an AI-on-data PoC (analytics + RAG on your Postgres data), or let a TechBag advisor plan sovereign analytics and AI — in INR/GST.
Stats, ratings, review counts and pricing are illustrative and sourced from public materials; verify before purchase.