Managed Postgres Databases Across AWS, Azure, GCP — Weekly Benchmarks for Aug 13 – Aug 19, 2026

🗄️ Managed Postgres Databases — Weekly Provisioning Benchmarks

Window: Aug 13 – Aug 19, 2026 | Total runs: 189

AWS us-west-2 led managed postgres databases provisioning this week at p50 5m 09s.

At a glance

Cloud	p50	p95	Reliability	Runs
AWS	5m 15s	5m 34s	100.0%	63
Azure	5m 34s	5m 41s	98.4%	63
GCP	8m 35s	9m 48s	96.8%	63

Regional detail

AWS

Region	p50	p95	Reliability
eu-north-1	5m 13s	9m 11s	100.0%
us-east-1	5m 18s	5m 28s	100.0%
us-west-2	5m 09s	5m 31s	100.0%

Azure

Region	p50	p95	Reliability
eastus2	5m 33s	6m 38s	100.0%
northeurope	5m 37s	5m 39s	95.2%
westus2	5m 34s	5m 37s	100.0%

GCP

Region	p50	p95	Reliability
europe-north1	8m 25s	9m 26s	100.0%
us-east1	8m 54s	10m 03s	90.5%
us-west2	7m 44s	8m 56s	100.0%

Methodology

Managed Postgres instance provisioning, measured from create-DB API call to the database accepting a connection. Includes teardown timing for completeness.

All data is collected via ProvisioningIQ — a continuous benchmarking platform that runs synthetic provisioning tests and publishes the results. No estimates, no vendor data; every number above came from a real provisioning attempt.

Comments

[Mads Hansen]

Useful weekly signal. The next thing I’d want is a comparability manifest per run: engine/version, instance tier and vCPU/RAM, storage type/size/IOPS, HA setting, networking mode, encryption/private endpoint choices, API region, and whether each account was warm with existing quotas. Provisioning defaults differ enough that “managed Postgres” can otherwise compare different products.

“Accepting a connection” is also a good first milestone but not necessarily ready-for-work. A stronger terminal check would authenticate with the intended app role, create/use a temporary schema if permitted, run a transaction, verify parameter settings, and record DNS/TLS readiness separately. That exposes partial-ready states.

With roughly 21 samples per provider-region for the week, regional p95 is effectively one of the slowest observations and will be noisy. Publishing raw timestamps, confidence intervals, sample count per cell, and failure taxonomy (quota, API timeout, control plane, DNS, auth, DB readiness) would make the trend more actionable.

I’d also separate create latency from retry-to-success latency and verify teardown completion/leak rate. For automation teams, a fast first attempt matters; a bounded, correctly classified recovery path matters more.