Question 1

What does Aurora PostgreSQL consulting include?

Accepted Answer

Aurora-specific architecture: cluster topology (writer + readers, Multi-AZ vs Multi-Region), Aurora Serverless v2 sizing, pgvector integration for RAG and ML workloads, Babelfish for SQL Server compatibility, Global Database for cross-region DR, parameter group tuning (different surface than RDS Postgres), and the Aurora-Postgres-vs-RDS-Postgres-vs-self-managed economics. Written advisory deliverables.

Question 2

Aurora Postgres vs RDS Postgres — which fits?

Accepted Answer

Aurora Postgres wins for: read-heavy workloads (up to 15 read replicas on shared cluster storage), pgvector + ML pipelines (Aurora-specific optimisations for ML inference), Global Database for cross-region active-active reads, faster failover (typically <30 seconds), or workloads benefiting from Aurora's cluster-storage performance. RDS Postgres wins for: cost-sensitive workloads, exact Postgres community-edition compatibility (Aurora has minor divergences), or workloads where Aurora's premium isn't justified. See Aurora vs RDS.

Question 3

What's Babelfish and when does it help?

Accepted Answer

Babelfish lets Aurora Postgres accept SQL Server TDS protocol traffic — applications connect as if to SQL Server, the database compiles a subset of T-SQL. For SQL Server → Postgres migrations, Babelfish is a phased-migration accelerator — your application connects without code changes while T-SQL stored procedures get rewritten incrementally. Coverage is ~80% of T-SQL; complex CLR functions, advanced stored procs, SQL Server-specific features still need rewriting. Useful tool, not a magic bullet.

Question 4

pgvector on Aurora — is it production-ready?

Accepted Answer

Yes. Aurora Postgres ships pgvector as a managed extension with HNSW and IVFFlat index types. Aurora has additional optimisations: integration with SageMaker for embedding generation, Aurora ML for in-database inference, and Aurora I/O-Optimized billing helps vector-search workloads where read amplification is high. For RAG architectures wanting vector search co-located with primary application data, Aurora Postgres is well-positioned.

Question 5

Aurora Global Database for Postgres — when is multi-region worth it?

Accepted Answer

Global Database replicates Aurora Postgres across regions with typical lag <1 second. Use cases: regulatory data-residency with cross-region DR, low-latency reads for global users (each region has read replicas), rapid recovery from full-region failure. The cost is real — N regions means N× compute + storage + cross-region replication. For most workloads, single-region Multi-AZ is sufficient; Global Database earns its premium when latency-sensitive global reads or strict cross-region RPO/RTO are real requirements.

Question 6

Aurora Serverless v2 for Postgres — fit profile?

Accepted Answer

Serverless v2 scales compute in 0.5 ACU steps with sub-second response. Best for: variable workloads (dev/test, multi-tenant SaaS, spiky traffic), unpredictable scaling, or cost-conscious environments where steady-state is small but bursts are common. Avoid for: predictable steady-state production where provisioned reserved-instance pricing is meaningfully cheaper. Aurora Serverless v2 on Postgres is more mature than v1 (no more cold-starts beyond seconds).

Question 7

RDS Postgres → Aurora Postgres migration — what's the path?

Accepted Answer

Cleanest path: RDS' in-place 'migrate to Aurora' (snapshot-based, simpler, has downtime) or AWS DMS for near-zero-downtime via logical replication. SQL surface is identical — Aurora Postgres is binary-compatible with Postgres community edition at the wire-protocol level. Application changes are usually limited to endpoint updates. We design the cutover with replication-lag gates and rollback path.

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