Traditional DBs

Integrated AI capabilities within traditional RDBMS, analytical MPP, and lakehouse platforms.

Developer Capability Comparison Matrix

Capability	PostgreSQL	MariaDB	Oracle AI DB	Apache Doris	Microsoft Fabric
Vector Storage & Query	Yes (pgvector)	Yes (native)	Yes (native)	Yes (native)	Yes (vector indexing in OneLake)
RAG Pipeline Support	No native, via app orchestration	Vector-native retrieval; embeddings typically produced externally	Yes with AI Vector + Model Context Protocol (MCP)	Via SQL + external LLM	Yes (platform workflows + Copilot)
AI Agents / Copilots	External tooling only	External tooling / app-level agents	Yes (platform tooling)	External tooling	Yes (built-in Copilot experience)
In-DB Inference Calls	Possible via procedural languages calling external services	No native; external services or UDF-style integrations	Yes (AI functions)	Yes (SQL functions calling external models)	Via platform services
Managed Cloud / Multicloud	Yes via Azure/GCP/others	Yes (MariaDB Cloud)	Yes OCI + @AWS/@Azure/@GCP	Often self-hosted or cloud distros	Yes (Azure-native)

Open Source RDBMS with extensive AI extension ecosystem.

pgvector: Adds a vector type for embeddings, distance operators (L2, inner product, cosine), and ANN indexing (HNSW / IVFFlat).
pgai (Timescale): Suite integrating RAG / semantic search patterns directly into SQL workflows.
Hybrid Search: Combine vector similarity with traditional structured SQL filters in a single query.

Embeddings and model calls are typically done in the application layer; Postgres remains the grounding store.
Excels as grounding store for embeddings when paired with an AI model provider.
Requires developer orchestration to manage ANN indexing and inference pipelines.

Managed Cloud: Supported by AWS RDS, Google Cloud SQL, Azure Database for PostgreSQL.

RDBMS with integrated vector search (VECTOR type + vector indexes) tuned for similarity search workloads.

Native Vector Search: VECTOR data type and VECTOR INDEX (modified HNSW) integrated into the server engine.
Distance Functions: VEC_DISTANCE_EUCLIDEAN / VEC_DISTANCE_COSINE / VEC_DISTANCE for similarity queries over embeddings.
SIMD Acceleration: Vector index operations can use SIMD instructions (x86_64 AVX2/AVX512, aarch64 neon, PowerPC AltiVec).

High-throughput vector retrieval inside the database reduces data movement for RAG retrieval steps.
Embedding generation is typically external; DB stores and searches vectors efficiently.

Enterprise-grade AI-native database with deep agentic integration.

AI Vector Search: Native support for vector data types and indexes stored inside transaction tables.
MCP / Agentic AI: Secure integration with MCP allowing agents to safely execute SQL.
NVIDIA NIM Acceleration: Integrated NVIDIA NIM containers for embedding generation and RAG pipelines.
DataFrame Integration: Tight Python (Pandas/NumPy) support for analytics-centric AI workflows.

Open-source, MPP analytical database for real-time AI analytics.

Analytical Vector Search: Built-in vector indexes supporting ANN queries for high-scale semantic similarity.
In-SQL Inference: Direct AI function calls (extraction, summarization) that interact with external LLMs.
MPP Performance: Designed for sub-100ms query latencies and 10k+ QPS on analytical workloads.
Lakehouse support: Native read/write for Iceberg/Paimon open formats for AI data workflows.

Unified AI-powered data estate for the enterprise.

OneLake: Single source of truth for all data, from SQL to unstructured files.
Integrated Copilot: AI-powered assistants for data engineering, science, and BI.
Vector Lakehouse: Advanced RAG pipelines with integrated vector indexing in OneLake.