AlloyDB consulting from MinervaDB is PostgreSQL consulting applied to Google’s managed PostgreSQL-compatible service: migration assessment and execution from self-managed PostgreSQL, Cloud SQL, Oracle and SQL Server; instance, read-pool and columnar-engine design; performance engineering with the same evidence discipline we use on community PostgreSQL; AlloyDB Omni for on-premises and multi-cloud deployments; and 24×7 AlloyDB support with named engineers. We have run PostgreSQL in production for more than two decades, and that is the expertise AlloyDB customers actually need, because most of what makes AlloyDB fast or slow is still PostgreSQL.
What AlloyDB Changes, and What It Does Not
AlloyDB is PostgreSQL-compatible across the current major versions, with a disaggregated storage layer, a columnar engine that keeps a copy of hot table data in memory in columnar format for analytical queries, read-pool instances that load-balance read traffic, continuous backup with point-in-time recovery, cross-region secondary clusters, and the AlloyDB AI features: pgvector storage, ScaNN-based approximate nearest-neighbour indexing, and in-database model invocation. AlloyDB Omni is the downloadable edition for Linux, Kubernetes and other clouds.
What AlloyDB does not change is the part most teams underestimate. Query planning is PostgreSQL query planning: statistics, join strategies, index selection and the cost model behave the way they do on community PostgreSQL. Bloat, vacuum, transaction ID wraparound, connection management, lock contention and long-running transactions are still yours to manage. Schema design still decides throughput. A workload that is slow on Cloud SQL because of a missing index or a chatty ORM is slow on AlloyDB for the same reason, at a higher price. Our AlloyDB consulting starts from that premise.
Feature details are drawn from the AlloyDB documentation, which we re-verify at the start of every engagement because the service changes quickly.
AlloyDB Consulting Services
Migration assessment: is AlloyDB the right target?
Before any migration we answer the question honestly. AlloyDB earns its cost on write-heavy transactional workloads that also carry a reporting or analytical load, on estates that need read pools without replica lag management, and on applications adding vector search next to relational data. It does not earn its cost for small databases, for pure analytics (BigQuery or ClickHouse are the right engines), or for workloads whose bottleneck is application design.
The assessment produces a sizing model, a feature-compatibility inventory (extensions, procedural languages, foreign data wrappers, logical replication needs), a cost comparison against Cloud SQL for PostgreSQL and self-managed PostgreSQL on Compute Engine, and a recommendation we are willing to argue for.
Migration execution
From self-managed PostgreSQL 12 through 17, Cloud SQL for PostgreSQL, Amazon RDS and Aurora PostgreSQL, Azure Database for PostgreSQL, and from Oracle and SQL Server through schema and PL/SQL or T-SQL conversion. We use Database Migration Service where it fits and logical replication with pglogical or native publication and subscription where it gives tighter control over the cutover, run both systems in parallel with row-count and checksum reconciliation, and cut over with a rehearsed rollback. Sequences, large objects, extension versions and replication slots are checked explicitly, because they are where migrations fail.
Architecture and instance design
Primary instance sizing from measured CPU, memory and I/O profiles; read-pool node counts derived from read traffic patterns rather than from a rule of thumb; columnar-engine memory allocation and column selection driven by the actual analytical queries, with the auto-columnarisation recommendations reviewed rather than accepted blindly; connection pooling through PgBouncer or the AlloyDB managed connection pooling; cross-region secondary clusters with a documented promotion procedure; and Private Service Connect or private IP networking that satisfies your security review.
Performance engineering
We diagnose AlloyDB performance the way we diagnose PostgreSQL: pg_stat_statements for the workload profile, EXPLAIN (ANALYZE, BUFFERS) for the plans that matter, pg_stat_user_tables and pg_stat_user_indexes for bloat and dead-index findings, wait events from pg_stat_activity, and the AlloyDB-specific views for columnar-engine hit rates and read-pool balance. Fixes are staged with a rollback path and verified against the same metrics afterwards. Where the columnar engine is not engaging on a query it should accelerate, we find out why (unsupported operators, missing columns, memory pressure) rather than buying a larger instance.
AlloyDB Omni
For customers who need AlloyDB’s engine on their own hardware, on Kubernetes with the AlloyDB Omni operator, or on another cloud, we design and operate the deployment: storage layout, high availability, backup and restore with pgBackRest-style discipline, monitoring, and the upgrade calendar. We also say when community PostgreSQL with Patroni is the better fit, because for many workloads it is.
Vector search and AlloyDB AI
Schema and index design for pgvector and ScaNN indexes, embedding pipeline integration, hybrid search combining vector similarity with relational filters, and the capacity planning that vector workloads need. We help teams decide between AlloyDB, PostgreSQL with pgvector, and a dedicated vector database such as Milvus based on recall, latency and scale requirements measured on their own data.
Security and compliance
IAM database authentication, customer-managed encryption keys, VPC Service Controls, pgAudit-based audit logging, row-level security, and the evidence needed for SOC 2, HIPAA, PCI DSS, GDPR and India’s DPDP Act.
AlloyDB, Cloud SQL or Self-Managed PostgreSQL
This is the decision most AlloyDB consulting engagements actually turn on, so we make it explicit rather than assuming the answer.
| Consideration | AlloyDB | Cloud SQL for PostgreSQL | Self-managed PostgreSQL on GCE or GKE |
|---|---|---|---|
| Best fit | Write-heavy OLTP with a reporting load, read scaling without lag management, vector search next to relational data | Standard OLTP at moderate scale, teams that want the simplest managed option | Extension-heavy workloads, full configuration control, cost-sensitive large estates |
| Read scaling | Read pools on shared storage, low lag | Read replicas with streaming replication lag | Replicas you design, with Patroni or your own tooling |
| Analytics on live data | Columnar engine in memory | Row store only; offload to BigQuery | Columnar extensions or offload |
| Extensions and superuser | Curated list, no superuser | Curated list, no superuser | Anything, full control |
| Operational load on your team | Low for platform, unchanged for database | Low for platform, unchanged for database | High unless a remote DBA covers it |
| Cost profile | Highest per vCPU, justified by throughput and read pools | Middle | Lowest infrastructure cost, plus operations |
We model your workload against all three with real pricing and the measured resource profile from the assessment, and we operate all three, so the recommendation is not biased by what we can sell.
Operating AlloyDB Day to Day
For customers on the support retainer, the operational runbook for an AlloyDB estate covers the work that AlloyDB’s managed platform leaves with the database owner.
Vacuum, bloat and wraparound
Autovacuum still has to keep up with the write rate. We tune per-table autovacuum thresholds and cost limits from pg_stat_user_tables dead-tuple ratios, watch age(datfrozenxid) against the wraparound horizon, and schedule pg_repack-style rebuilds where bloat has already accumulated. A wraparound-forced shutdown is as possible on AlloyDB as on any PostgreSQL if nobody is watching.
Connections and pooling
PostgreSQL’s per-connection process model means a connection storm from a scaled-out application tier can take down the primary regardless of the storage layer. We size max_connections, put PgBouncer or the managed pooler in front, and set application pool limits so the total stays inside the instance’s capacity.
Statistics and plans
Plan regressions after data growth or a major version change are found through pg_stat_statements deltas and fixed with targeted ANALYZE, extended statistics, index changes or, rarely, planner settings, each with a before-and-after plan captured in the change record.
Read pools and the columnar engine
Read-pool node counts are adjusted from measured read throughput, and the columnar engine’s memory allocation and column set are reviewed monthly against the analytical queries that are supposed to benefit, using AlloyDB’s columnar-engine statistics views to confirm they actually do.
Backups, PITR and DR drills
Continuous backup retention set from your recovery objectives, on-demand backups before every risky change, cross-region secondary clusters for estates that need them, and a quarterly restore and promotion drill with the measured recovery time recorded.
Version and maintenance windows
We track AlloyDB maintenance schedules and PostgreSQL major-version availability, test upgrades on a cloned cluster with your application’s regression suite, and schedule the production change inside a window with a documented rollback.
Sizing and Cost Modelling
AlloyDB pricing is driven by vCPU and memory on the primary and each read-pool node, storage, backup storage and network egress. Oversizing is common because teams provision for the peak of an unoptimised workload. Our sizing model starts from the measured CPU, memory, I/O and connection profile of the current system, applies the query and schema fixes identified in the assessment, and only then sizes the AlloyDB instances, which routinely lands on a smaller machine type than the first estimate. The model is delivered as a spreadsheet you keep, with the assumptions labelled, and reviewed against actual utilisation after the first month in production.
24×7 AlloyDB Support
AlloyDB support is delivered by the same PostgreSQL engineers who cover our PostgreSQL support and PostgreSQL remote DBA customers, with the same severity matrix: S1 acknowledged within 15 minutes around the clock, S2 within 12 hours, S3 within 24 hours, S4 within 48 hours. The retainer includes an onboarding assessment, a runbook for your estate, monitoring integration with Cloud Monitoring, Prometheus or Datadog, monthly health reports, quarterly restore and failover drills, and advisory hours. Google Cloud remains responsible for the platform; we are responsible for making your database on it perform, stay available and stay within budget.
Pricing
Remote consulting is US $300 per hour and on-site consulting US $500 per hour plus travel. Support and remote DBA retainers start at US $4,500 per quarter. Migration and assessment projects are quoted on a fixed scope after a short call. As with every MinervaDB engagement: test every change in a non-production cluster first, keep verified backups before schema or version changes, and maintain a robust disaster-recovery posture with cross-region replication where the data warrants it.
Frequently Asked Questions
Is AlloyDB fully compatible with PostgreSQL?
For applications, effectively yes: the SQL dialect, drivers, most extensions and the tooling work unchanged. Operationally you give up superuser access and some configuration control, and a few extensions and features are unavailable. Our assessment checks your specific inventory before we recommend a move.
Should we move from Cloud SQL for PostgreSQL to AlloyDB?
Only if the workload benefits from AlloyDB’s storage layer, read pools or columnar engine enough to justify the higher price. We measure that on your workload during the assessment and give you a written recommendation either way.
Can you migrate us from Oracle or SQL Server to AlloyDB?
Yes. Schema, PL/SQL and T-SQL conversion, data migration with reconciliation, and application-layer changes are all in scope. Our Oracle and SQL Server practices work alongside the AlloyDB team on these projects.
Do you support AlloyDB Omni outside Google Cloud?
Yes, on Linux hosts, on Kubernetes through the operator, and on AWS or Azure infrastructure.
How does AlloyDB support differ from Google Cloud support?
Google supports the platform. We support your database: query performance, schema design, replication and failover procedures, capacity, cost and incident response with a root-cause analysis after every S1.
How do we get started?
Book a scoping call and we will come back with a written scope. Existing AlloyDB estates usually start with a health check.