· HIGH AVAILABILITY & PERFORMANCE
High availability that's designed, not promised.
FAILOVER PLANNING
SCALING PATHS
RECOVERY TESTED
OBSERVABILITY
VM / K8s / HYBRID
What "high availability" actually means.
HA is not just "two servers". It's clear targets, clear failure modes, and proven recovery. We design around what matters most: downtime tolerance, data safety, and performance under load.
Define the targets (RTO/RPO)
Before you build redundancy, define what "acceptable" looks like for your business.
RTO: how long you can be offline
RPO: how much data loss is acceptable
Peak impact: what happens on sales days / payroll runs / month-end
Design for failure (not hope)
We plan the specific failure modes and build the simplest reliable path through them.
Single points of failure identified and removed
Clear runbooks: "if X fails, do Y"
Recovery tested so it's real, not theoretical
Performance is part of reliability
Slow systems fail too - they cause timeouts, queue buildup, and cascading incidents.
Storage latency and DB consistency
Cache effectiveness and edge strategy
Capacity planning and saturation alerts
Safe change reduces outages
Many outages are self-inflicted during upgrades. We design change workflows that are reversible.
Staging-first releases and rollout safety
Rollback paths and config consistency
Change visibility and ownership
Common HA architecture options.
The 'right' pattern depends on your RTO/RPO, complexity tolerance, and budget. We'll recommend the simplest design that meets your risk profile.
Active / passive
One primary environment + a standby. Simpler, cost-effective, and common for many businesses.
Failover playbook
Warm standby (faster RTO) vs cold standby (lower cost)
Restore and cutover practice
Active / active (multi-node)
Multiple nodes serving traffic with load balancing and redundancy. Higher complexity, higher resilience.
Load balancer + health checks
Rolling upgrades and no-downtime patterns
Capacity headroom for failures
Multi-environment governance
Production + staging + dev with clean separation and change control to reduce risk.
Staging parity with production
Release visibility and rollback readiness
Clear responsibility model
Database HA patterns
Databases are usually the real HA constraint. We design around consistency and recovery.
Replication strategies (where appropriate)
Backups + tested restores
Latency/IOPS-first storage design
Kubernetes HA
Cluster redundancy, node pools, and safe rollouts - plus the stateful storage patterns that matter.
Control-plane/worker resilience
Pod disruption and rollout strategy
Stateful workloads done properly
Hybrid by design
Sometimes the right answer is mixed: VM databases + container apps, or split workloads by risk.
Keep critical state stable
Modernise safely over time
Reduce complexity where it doesn't pay off
Want HA + recovery hardening? See Security, Backups & Monitoring.
Performance work that actually improves outcomes.
Performance isn't 'add more CPU'. We tune the bottlenecks that matter: storage latency, cache hit rates, database pressure, PHP/worker sizing, search sizing, and edge strategy.
Storage & database latency
Most "slow platform" incidents are storage/DB latency masquerading as an app problem.
Page cache strategy that respects cart, checkout and account sessions
Object cache patterns for product/category performance (where suitable)
PHP-FPM worker sizing for real concurrency (not "defaults")
Rate limiting and bot mitigation patterns to protect checkout
Caching & edge strategy
Cache hit rate is the cheapest scaling lever - when implemented properly.
DB health monitoring (slow queries, contention, storage latency)
Maintenance windows planned around business impact
Backup/restore designed for real recovery - not assumptions
Performance review for high-order stores and peak readiness
Workers, queues, and background load
Platforms fail under load when workers are mis-sized and background tasks silently pile up.
Capacity planning and load expectations (sessions, checkout concurrency)
CDN + asset strategy to reduce origin load
Scaling triggers and "what happens when it hits" planning
Operational visibility for promotion windows (alerts that matter)
Observability for performance
Performance stays fixed when you can see the bottleneck clearly and early.
Staging-first updates for payment/shipping plugins
Monitoring for checkout failures and payment gateway errors
Safer release workflow (rollback path and change visibility)
Performance tuning around integrations and background tasks
How we improve HA and performance.
We baseline, remove the highest-risk failure modes first, then harden change processes so upgrades don't become outages.
STEP 1
Baseline + risk map
We map failure points, current bottlenecks, and the operational gaps that cause incidents.
STEP 2
Architecture plan
We propose the simplest design that meets your RTO/RPO and performance needs, with clear trade-offs.
STEP 3
Build + harden
We implement improvements safely: redundancy, monitoring, backups, and tested recovery paths.
STEP 4
Validate recovery
We test restores/failover so the plan works under pressure - not just on paper.
STEP 5
Operate + optimise
Ongoing tuning, patching, upgrades, and continuous improvement as your platform grows.
STEP 6
Scale predictably
When spikes hit, scaling is planned and observable - not rushed and reactive.
Where HA/performance work usually lands.
These are the areas that most often drive outages or slowdowns - and the areas we prioritise first.
Reverse proxy / edge layer
Load balancing, health checks, caching rules, and safe rollout paths.
Traffic distribution and failure isolation
Cache control + invalidation strategy
Rate limiting and abuse protection
Stateful systems
Databases, search, file storage, and backups - designed for consistency and recoverability.
Storage latency and reliability
Backups + restore testing
Failover and maintenance planning
Application runtime
Workers, timeouts, caching layers, background jobs, and deployment safety.
Worker sizing and concurrency planning
Queue health and backlog prevention
Release workflow with rollback capability
Observability
Monitoring that detects issues early and points to the cause.
Latency/error rate alerts (not noise)
Saturation signals (CPU/RAM/IO)
Runbooks + incident response process
Common questions.
Short answers - we can go deeper once we understand your store, traffic, and current environment.
Do we need Kubernetes for high availability?
Not always. Many businesses get excellent HA from simpler VM-based patterns. We'll recommend the simplest design that meets your goals.
Is HA expensive?
It can be - but the cost should match your downtime risk. We prioritise designs that reduce outage likelihood without unnecessary complexity.
Can you design for "near zero downtime" upgrades?
Often yes, depending on the stack and release model. The key is safe deployment patterns, redundancy, and a clean rollback path.
How do you validate recovery?
By testing restores and failover steps as part of the operating model - not as a one-off exercise.
Related pages.
Explore options based on what you're running and what level of resilience you need.
Hosting Overview
How we run production hosting - performance, uptime, monitoring, backups, and change control.
Security, Backups & Monitoring
Protection, verified backups and monitoring that catches issues early.
Magento Hosting
Hosting built for conversion, cache strategy, and safe deployments for revenue-critical stores.
Odoo Hosting
ERP hosting designed for stability, safe upgrades, and reliable background processing.
WordPress Hosting
Production-grade WordPress and WooCommerce hosting with monitoring, security, and update safety.
DevOps
Automation, deployment safety, and operational discipline that reduces outages.
Want WordPress hosting that stays fast and safe?
We'll review your current environment (hosting, database, caching, plugins, update workflow, monitoring, and backups), then recommend a hosting model that improves speed, reduces risk, and keeps updates predictable.