What are the best practices for customizing SharePoint without affecting performance and scalability?

The best practices for customizing SharePoint without affecting performance and scalability include designing list and library structures with indexed columns and sensible thresholds in mind, avoiding overly complex client-side rendering, minimising unnecessary API calls, testing customisations under realistic load, and being deliberate about how permissions are structured across large volumes of content. Getting these details right early is what separates SharePoint customization that scales gracefully from customisation that quietly degrades performance as adoption grows.

Design list and library structures with scale in mind

SharePoint enforces list view thresholds that can cause performance problems if a list grows large without appropriate indexing on the columns used for filtering and sorting. Customisations that rely on views filtering unindexed columns, or that use complex nested lookups across large lists, tend to degrade in performance considerably as content volume grows, even if they performed acceptably during initial testing with a small dataset. Planning indexed columns and simpler list structures from the outset avoids this becoming a problem only once real content volume accumulates.

Be deliberate about permission structures

Applying unique permissions to individual items rather than managing permissions at the list or library level creates a permission structure that becomes increasingly expensive for SharePoint to process as the number of uniquely permissioned items grows. Customisations that programmatically create many unique permission assignments, without a clear justification for why item-level permissions are genuinely necessary, are a common and often overlooked source of performance degradation in larger SharePoint environments.

Minimise unnecessary API calls in custom code

Custom SPFx web parts and extensions that make excessive or inefficient calls to the SharePoint REST API, particularly calls made in a loop rather than batched appropriately, create performance problems that become more visible as the number of users interacting with the customisation grows. Batching API requests where possible, caching data that does not change frequently rather than re-fetching it on every interaction, and being deliberate about which data is genuinely needed rather than over-fetching, all contribute meaningfully to a customisation that scales well.

Avoid overly complex client-side rendering

Customisations that perform heavy processing or rendering directly in the browser, particularly on pages with large amounts of data, can create a sluggish experience for users, especially on lower-powered devices or slower network connections. Offloading heavier processing to a backend service, such as an Azure Function, rather than performing it entirely client-side within an SPFx web part, generally produces a more consistently responsive experience across a wider range of user devices and conditions.

Test customisations under realistic data volume and user load

Customisations that perform well during development, typically tested against a small sample dataset with a handful of test users, can behave very differently once deployed against production-scale content volume and genuine concurrent usage. Testing against a dataset that approximates real production volume, and where possible simulating realistic concurrent usage, catches performance problems before they affect the wider organisation during a full rollout.

Monitor performance after deployment, not just before

Performance considerations do not end at deployment. Using Application Insights or similar monitoring tools to track how customisations perform in production over time, as content volume and user count naturally grow, allows performance issues to be caught and addressed proactively rather than only after users begin reporting a sluggish experience.

Plan for growth from the outset

Ultimately, the customisations that hold up well over time are the ones designed with realistic growth in mind from the start, rather than optimised purely for the scale that existed at the point of initial deployment. Building in this forward-looking consideration during the design phase, rather than treating scalability as something to address later if it becomes a problem, avoids the more costly process of retrofitting performance fixes into a customisation that is already deeply embedded in daily business operations.

Documenting performance-related design decisions

Recording why specific performance-related decisions were made, such as why a particular list was indexed a certain way or why processing was deliberately pushed to a backend service, helps future developers maintain the customisation without inadvertently undoing decisions that were made specifically to protect performance at scale. This documentation is easy to skip under project deadline pressure but pays for itself the first time a future change threatens to reintroduce a performance problem that was already solved once.

Working with experienced developers for performance-critical customisations

For customisations expected to handle significant scale or sit on a business-critical process, involving developers with specific experience building performant SharePoint solutions, rather than relying solely on general SharePoint familiarity, meaningfully reduces the risk of performance problems surfacing only after the customisation has already been rolled out organisation-wide. This investment upfront is consistently cheaper than diagnosing and fixing performance problems after a customisation is already relied upon daily across the organisation.

Revisiting performance assumptions as the organisation grows

Performance characteristics that were acceptable at a given organisational size do not necessarily hold as the business grows significantly, which is why periodically revisiting key customisations against current, rather than original, scale assumptions helps catch degrading performance before it becomes a significant user-facing problem.