The real cost of availability and operational risk
Or why stability without intentional design will always cost more
Performance and uptime directly affect the bottom line. When queries slow down or availability drops, the business impact is immediate, from customer experience to engineering distraction. Sources vary, and data depends on a range of variables (industry, seasonality, size, etc.), but most reports estimate the average cost of unplanned downtime to fall somewhere in the ballpark of $300,000 - $500,000 per hour. The challenge is balancing resilience with cost.
In self-managed environments, it’s tempting to overbuild: more replicas, faster storage, more zones. It makes sense, especially if there are concerns about whether onsite staff can effectively handle a potential outage. But resilience doesn’t have to mean redundancy at every layer. It has to mean deliberate design.
A more cost-effective and flexible setup can be built using open source tools like Patroni for high availability and failover orchestration, PgBouncer for lightweight connection pooling, and HAProxy for traffic routing. These components, when configured correctly, provide strong availability guarantees and operational resilience. For backups and recovery, tools like pgBackRest offer robust protection with support for incremental backups, parallel restores, and point-in-time recovery.
Unlike managed services, a self-managed PostgreSQL environment gives you full control over how availability is implemented and tuned. Whether deployed on VMs or in Kubernetes, this approach lets you align redundancy, failover timing, and recovery plans with your actual business requirements.
A Storage is a design decision title
Or why it’s more than just a line item
Availability isn't just about failovers — it's also about throughput, latency, and I/O. And nowhere is this more visible than in how PostgreSQL handles vacuuming.
PostgreSQL’s autovacuum process reclaims dead tuples and keeps statistics fresh, both essential for query performance. But when left untuned — especially in write-heavy workloads or environments with large tables — autovacuum can become a source of disk I/O spikes that push you into higher storage tiers like io1 or io2. That spike drives costs. And worse, the reactive fix is usually overprovisioning storage or upgrading to IOPS-optimized volumes.
A better approach is proactive tuning. That could mean lowering autovacuum thresholds, running vacuum manually on large tables during off-peak hours, or even temporarily disabling autovacuum in certain scenarios. Each strategy reduces the likelihood of performance slowdowns — and cuts down on the infrastructure needed to support routine operations.
Risk is technical debt, too
Or why it’s more than just downtime
Operational risk doesn't always show up as an outage. Sometimes it's the slow creep of degraded performance, missed maintenance windows, or assumptions made at a smaller scale that don’t hold up when traffic increases.
This is why we advocate for proactive operational review — not just during incidents, but as a regular part of PostgreSQL ownership. That includes:
• Quarterly failover and recovery testing
• Monthly index and bloat audits
• Scheduled autovacuum reviews for large tables
• Configuration validation across regions or environments
We’ve seen teams firefight replication lag, failover failures, and IO bottlenecks — not because their architecture was wrong, but because it lacked ongoing investment in visibility and optimization.
That’s where proactive operational design pays off. Testing failovers quarterly. Reviewing autovacuum metrics monthly. Validating replica performance before peak season. This isn’t overhead — it’s insurance against infrastructure waste and business impact.
