Arango vs. Postgres
Ship AI Agents.
Not a Frankenstack.
Arango replaces everything a Postgres-based AI stack has to bolt on: vector search, graph traversal, and retrieval logic, all in one platform.
Postgres for AI means bolting on a vector store, graph library, search index, and custom retrieval code that’s four systems to maintain. Arango does it natively, in one engine.
One platform, not five bolted-on extensions.
How Arango compares to a Postgres-based AI stack
It comes down to what’s bolted on vs. what’s built in.
| Capability | Arango Contextual Data Platform | Postgres + Extensions | Advantage |
|---|---|---|---|
| Horizontal scaling How read and writes scale as load grows | Arango shards natively across the cluster, so both data and query load scale out as the workload grows. | Postgres writes through a single primary with no native sharding. Scaling further typically means read replicas (which don’t help writes) or moving shardable workloads onto separate systems — the path OpenAI documented taking to reach production scale. | Arango |
| Vector search How similarity search performs at scale | AQL-native vector search runs inside the same engine as everything else, not a database plus a separate vector store to keep alive. | pgvector shares CPU, memory, and I/O (input/output) with your live transactional traffic, and its indexes are capped at 2,000 dimensions (4,000 with halfvec): real, documented limits, not a rounder number about vector count. | Arango |
| Graph traversal How multi-hop queries actually run | Native multi-hop traversal is built into the engine and shards horizontally with the cluster, so performance holds up as both the graph and the traffic grow — no re-platforming down the line. | Postgres has had native SEARCH and CYCLE clauses since version 14 for cycle detection, but recursive CTEs still lack index-free adjacency: a graph engine follows a direct pointer per hop, while Postgres re-joins and re-indexes at every level, so performance degrades sharply as traversal depth and graph size grow. | Arango |
| Search Full-text and hybrid retrieval | ArangoSearch is built into the same engine, so full-text and hybrid search come standard, with nothing extra to provision, secure, or keep in sync. | Only basic built-in text search. Production-grade relevance typically means standing up Elasticsearch or OpenSearch as a second system to run alongside Postgres. | Arango |
| Knowledge graph construction Work required before AI can use the graph | AutoGraph automatically discovers entities and relationships and builds a governed knowledge graph from raw data, turning a job that normally takes months into days. | No automatic graph-building tooling. Relationships get modeled by hand, usually through a custom pipeline built with something like LangChain or LlamaIndex, ongoing engineering work, not a one-time cost. | Arango |
| Retrieval strategy How the right method gets chosen per query | AutoRAG automatically picks the best retrieval approach for each query, so no one on the team has to write or maintain that decision logic themselves. | No built-in retrieval-strategy layer. Teams write and maintain that orchestration code themselves, and it has to be kept in sync as usage patterns change. | Arango |
| Transactional maturity Track record as a system of record | Strong support for ACID (Atomicity, Consistency, Isolation, Durability) transactions, but a materially shorter track record specifically as an OLTP (Online Transaction Processing) system of record, a real trade-off, worth naming plainly here. | Decades of production hardening as a relational OLTP engine, with an enormous ecosystem: a genuine, earned advantage where this is the primary workload. | Postgres |
| Consolidation How many moving parts a team operates | One engine and one query language cover graph, vector, document, and search, so there’s a single system to operate, secure, and scale, not four. | Each AI capability, vector, graph, search, retrieval, is its own bolted-on piece, each with its own tuning, scaling limits, and failure modes to manage separately. | Arango |
What teams use Arango for:
Consolidating the stack
01
One engine instead of five.
Graph, vector, document, and search all live in one system, not stitched together from pgvector, a graph library, Elasticsearch, and a RAG (Retrieval-Augmented Generation) framework, each with its own operational surface.
02
No manual ontology design.
AutoGraph discovers entities and relationships automatically. There’s no Postgres equivalent: knowledge-graph structure otherwise has to be modeled and maintained by hand as the data changes.
03
Retrieval that adapts itself.
AutoRAG picks the right strategy per query. On a Postgres-based stack, that decision logic is orchestration code someone on the team owns and maintains.
What teams use Arango for:
Building AI on enterprise data
The graph builds itself
Hand-modeling a schema and relationships takes weeks before anything ships. Arango AutoGraph builds a governed knowledge graph from your data automatically.
Retrieval without the pipeline
Postgres-based RAG means assembling and maintaining your own pipeline. AutoRAG handles relationship-aware retrieval across graph, document, vector, and search out of the box.
Ready when AI is
AutoGraph, AutoRAG, GraphML, and MCP tools ship with the platform. If AI isn’t your workload yet, it’s the same database when it is.
See Arango AutoGraph turn your existing data into a knowledge graph.
What teams are building with Arango
Digital twins, operational systems, real-time state, and production AI, all running where connected data, performance, and scale decide whether the system holds up.
Orange Telecom
51M
Digital twins using graph, key-value, and geospatial data with ACID transactions at carrier scale.
HPE (Aruba)
5→1
Consolidated 6 separate databases down to one with Arango.
PSI CRO
6 weeks → minutess
Cut clinical trial site identification from up to six weeks to minutes using a system built on Arango.
Cloud Imperium Games
Scale
Horizontal scale for real-time game state in a massive persistent universe.
Scale beyond what a bolted-on stack can hold together.
Stop stitching your Frankenstack together.
Tell us what you’re building. We’ll show you how much engineering comes off the table.