4 Ways to Prevent Cloud App Performance Issues

TL;DR

Cloud application performance depends on more than server capacity.
Establish a baseline using application response times, user demand and workload requirements.
Monitor the application, database, infrastructure and network as one environment.
Account for user location, integrations and application configuration when diagnosing problems.
Assign clear responsibility for investigating and resolving issues across technology providers.

When a cloud application slows down, adding more infrastructure may seem like the obvious solution. But application performance depends on much more than processor speed, memory or storage.

The application, database, operating system, network, integrations and user location all affect how quickly work gets done. A change in any one layer can lead to slow logins, delayed reports, long refresh times or poor response during periods of high demand.

This is especially important for project-critical applications. When teams depend on systems such as Primavera P6, EcoSys, Autodesk products or Microsoft Project, slow performance can disrupt scheduling, reporting and day-to-day project work.

Improving cloud application performance begins with understanding the complete environment. These four actions help organizations identify problems earlier, address their actual cause and maintain a more reliable experience for users.

1. Establish an Application-Performance Baseline

You cannot identify a meaningful performance problem without knowing what normal performance looks like.

Start by measuring the functions users rely on most. For a project-controls application, that might include:

Login time
Schedule loading and refresh time
Report generation
Data imports and exports
Response during peak usage
Performance for users in different locations

Record the number of active users, typical data volumes, application versions and infrastructure capacity during the same period. This helps distinguish a temporary slowdown from a recurring capacity or configuration problem.

Latency and throughput provide useful technical measures. Latency shows how long data takes to travel between points, while throughput measures how much data moves within a given period. Both can affect application response, particularly for users working far from the hosted environment.

A useful baseline should reflect actual application tasks, not only server statistics. An environment may show healthy processor and memory usage while users still experience slow schedules, reports or database queries.

The AWS Well-Architected Performance Efficiency guidance recommends using data-driven monitoring and regular review as workloads and technologies evolve.

Review the baseline whenever users, data volumes, applications or integrations change. Performance targets established during deployment may no longer reflect the environment your teams operate today.

2. Monitor the Complete Application Environment

Cloud application performance depends on several connected technology layers. Monitoring infrastructure alone may confirm that a server is available without revealing why the application is slow.

Effective monitoring should cover:

Application response times and errors
Database queries and resource use
Processor, memory and storage capacity
Network latency and connectivity
Integration performance
User activity and demand
Availability and failed components

These measures need to be viewed together. A slow report could result from an inefficient database query, limited infrastructure capacity, network delay or an integration waiting for another system. Looking at only one component can lead teams to treat the symptom rather than the cause.

Google’s guidance on monitoring distributed systems identifies latency, traffic, errors and saturation as four essential signals. Applied to enterprise applications, these measures help teams understand whether performance problems come from user demand, failed requests, constrained resources or slower response across the environment.

Monitoring should also establish thresholds and alerts for meaningful changes. The objective is to identify declining performance before it disrupts project work—not simply confirm an outage after users report it.

3. Address Application, Database and Network Factors

Adding infrastructure will not resolve every cloud application performance problem. The source may be the application configuration, database, integrations, network or distance between users and the hosted environment.

For applications such as Primavera P6 and Sequence Enterprise (EcoSys), investigate:

Database configuration and query performance
Application settings and supported versions
Data volume and scheduled processes
Integrations that exchange or transform information
Network routes and latency
User location and connection quality
Recent patches, upgrades or configuration changes

The timing and location of the problem provide useful clues. If every user experiences a slowdown, the cause may sit within the application, database or infrastructure. If only one region or office is affected, the network path or local environment may be responsible. If performance declines during a scheduled import or reporting cycle, the workload itself may be creating contention.

Test changes against the performance baseline rather than relying on assumptions. Increasing capacity may help when resources are constrained, but configuration, database or network problems require different solutions.

The goal is to identify the layer causing the delay before changing the environment.

4. Establish Clear Responsibility for Performance

Cloud application problems often cross technology boundaries. The infrastructure provider may report that its servers are healthy while the software vendor sees no application error. Internal IT can then be left determining whether the problem sits within the application, database, network, integration or user environment.

Before an issue occurs, define who is responsible for:

Monitoring each technology layer
Investigating performance alerts
Coordinating with software and infrastructure vendors
Testing configuration changes
Communicating with affected users
Escalating unresolved problems
Confirming that performance has returned to its baseline

Service-level agreements should define availability, response and escalation commitments. But uptime alone is not enough. An application can technically remain available while performing too slowly for users to work effectively.

A provider responsible for the complete application environment can investigate across infrastructure, databases, applications and networks rather than stopping at one layer. This reduces the coordination burden on internal IT and creates clearer accountability for resolution.

Improve Performance Across the Complete Environment

LoadSpring Managed Hosting provides a fully managed environment for more than 200 project-critical applications. LoadSpring monitors and manages the infrastructure, applications, databases, security, access, updates, backups and support as part of the environment.

That complete view matters when a performance problem crosses technology layers. LoadSpring can investigate the application and its supporting environment, coordinate with software vendors when needed and help resolve the issue without leaving internal IT to manage multiple support paths.

With 25 years of experience, a 99.9% uptime guarantee and global live support available 24/7/365, LoadSpring helps organizations maintain reliable access to the applications their project teams depend on.

Talk to a LoadSpring expert about your cloud application performance and hosting environment.

FAQ

What causes poor cloud application performance?

Poor performance can result from undersized infrastructure, inefficient database queries, application configuration, network latency, integrations, scheduled jobs or high user demand. The cause may sit in one layer or span several parts of the environment.

How do you measure cloud application performance?

Start with the tasks users complete most often, such as logging in, opening schedules, running reports and importing data. Measure response times during normal and peak periods, then compare those results with application, database, infrastructure and network metrics.

What is the difference between latency and throughput?

Latency measures how long data takes to travel between locations. Throughput measures how much data can move during a given period. An application can have sufficient throughput and still feel slow when latency is high.

Why can a cloud application be slow when the server is healthy?

Healthy server metrics do not rule out problems elsewhere. Slow database queries, application settings, integrations, network routes or the distance between users and the hosted environment can all affect performance even when CPU and memory usage appear normal.

Who is responsible for cloud application performance?

Responsibility depends on the hosting model. It may be divided among internal IT, the infrastructure provider, the software vendor and other service providers. A managed hosting provider can take responsibility for monitoring and coordinating performance across the complete application environment.