Google Cloud Financial Services North America

Airwallex

IT Operations Automation

Airwallex exists to simplify global finance, managing high-frequency transactions across markets like the US, EU, Australia, and Singapore. To support this scale, the company utilizes a globally distributed microservices architecture where each domain is assigned a dedicated database to ensure fault isolation and security.

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Managed 5,000+ databases with only four DBAs using Terraform automation Maintained a 99.99% uptime SLA for global payments while spinning new regions

Reported impact

Business outcome

Managed 5,000+ databases with only four DBAs using Terraform automation Maintained a 99.99% uptime SLA for global payments while spinning new regions; Integrated AI database agents as virtual DBAs to slash mean time to resolution (MTTR); Unified fleet health and security across thousands of database instances.

Solution

Airwallex manages 5,000+ databases with four DBAs using Cloud SQL to ensure 99.99% uptime for global payments. Airwallex exists to simplify global finance, managing high-frequency transactions across markets like the US, EU, Australia, and Singapore. To support this scale, the company utilizes a globally distributed microservices architecture where each domain is assigned a dedicated database to ensure fault isolation and security. While this model supports rapid growth, it resulted in a massive fleet of more than 5,000 databases. Managing such a vast fleet presented significant challenges in identifying performance bottlenecks like slow queries across thousands of instances. Airwallex needed an operating model that could keep pace with rapid global expansion. Google Cloud’s global footprint means we can spin up isolated, enterprise-grade databases in new regions in minutes.

Solution architecture

2 components · 2 layers

Storage
- Cloud SQL Enterprise Plus Stores transactional state and application data for the operational workflow at low latency.
Governance
- Database Center Provides fleet-wide visibility into database health, performance, and maintenance operations.

Architecture clues

Because of this, the company chose Cloud SQL Enterprise Plus edition for PostgreSQL because this operating model can keep up with global expansion. Google Cloud’s global footprint means Airwallex can spin up isolated, enterprise-grade datab
Database Center
Google Cloud’s global footprint means we can spin up isolated, enterprise-grade databases in new regions in minutes. Cloud SQL Enterprise Plus gives us near-zero downtime for planned maintenance and scaling and we can maintain a 99.99% avai
Instead of fragmented logs, Database Center offers a single pane of glass to monitor fleet health across thousands of databases. Query Insights and System Insights now help the team spot performance issues early, allowing them to optimize r
To manage their massive database life cycle at scale, Airwallex leverages infrastructure as code with Terraform, benefiting from Cloud SQL's robust Terraform support. Once created, every Cloud SQL instance automatically shows up in the Data

Evidence from the source

Airwallex exists to simplify global finance, managing high-frequency transactions across markets like the US, EU, Australia, and Singapore. To support this scale, the company utilizes a globally distributed microservices architecture where

Airwallex manages 5,000+ databases with four DBAs using Cloud SQL to ensure 99.99% uptime for global payments.

Google Cloud’s global footprint means we can spin up isolated, enterprise-grade databases in new regions in minutes. Cloud SQL Enterprise Plus gives us near-zero downtime for planned maintenance and scaling and we can maintain a 99.99% avai

Integrated AI database agents as virtual DBAs to slash mean time to resolution (MTTR)

Managed 5,000+ databases with only four DBAs using Terraform automation Maintained a 99.99% uptime SLA for global payments while spinning new regions

Unified fleet health and security across thousands of database instances

Transparency Evidence & methodology 75/100 reference value · 87% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value75/100

Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth15/25
Rewards named components, data flow, integrations, and architecture detail.
Outcome specificity20/20
Measures whether the reported benefit is concrete rather than a generic claim.
Quantified impact15/25
1 metric describe measurable impact.
Deployment scale5/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence87%

Source authority20/25
The main evidence is an official vendor-published customer story.
Claim traceability21/25
10 of 12 key fields link to a named source.
Direct evidence20/20
6 direct source quotes retained.
Date provenance10/10
The publication date comes from the source page or publisher.
Source transparency8/10
1 source reference document publisher, URL, authority, retrieval, and supported fields.
Implementation specificity8/10
Rewards source-backed components, data flow, quantified impact, and a specific outcome.

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

Google Cloud
Airwallex customer story

First-party
https://cloud.google.com/customers/airwallex-cloudsql

Source type
Vendor-published customer story

Published
Oct 9, 2026
Supports
- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes
- Publication date

Date evidence

Published on source page Oct 9, 2026 · Google Cloud Blog · First-party
Open date source

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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