How do you architect an OmniStudio solution for performance?

How do you architect an OmniStudio solution for performance?

• A. Increase the number of API calls to fetch data more often.
• B. Run all processing on the client side; no server involvement.
• C. Minimize API calls, reuse DataRaptor and Integration Procedure results, batch processing, and avoid heavy runs on the client.
• D. Avoid caching any data to ensure fresh results.

Answer: (C)

Performance in OmniStudio is best achieved by reducing how often you reach out to data sources and by moving the heavy work to the server where it can be optimized. DataRaptor and Integration Procedures run on the server, so reusing their results and batching multiple operations into a single flow minimizes round-trips and total processing time. When you minimize API calls, you cut network latency and load, which scales better as more users come on line. Reusing results avoids repeating the same computations or fetches, which also speeds up response times. Putting the heavy processing on the server keeps the client lightweight, more responsive, and less burdened by data handling and security checks. Caching plays a big role in performance, too, because it avoids repeating costly fetches for data that hasn’t changed; however, caching needs sensible invalidation to ensure data stays fresh. Conversely, increasing API calls elevates latency and server load, running everything on the client sacrifices security, data consistency, and performance for complex operations, and avoiding caching eliminates a powerful optimization. Sticking to a server-centric, batched, and reused-data approach aligns with best practices for fast, scalable OmniStudio solutions.

Performance in OmniStudio is best achieved by reducing how often you reach out to data sources and by moving the heavy work to the server where it can be optimized. DataRaptor and Integration Procedures run on the server, so reusing their results and batching multiple operations into a single flow minimizes round-trips and total processing time. When you minimize API calls, you cut network latency and load, which scales better as more users come on line. Reusing results avoids repeating the same computations or fetches, which also speeds up response times. Putting the heavy processing on the server keeps the client lightweight, more responsive, and less burdened by data handling and security checks.

Caching plays a big role in performance, too, because it avoids repeating costly fetches for data that hasn’t changed; however, caching needs sensible invalidation to ensure data stays fresh. Conversely, increasing API calls elevates latency and server load, running everything on the client sacrifices security, data consistency, and performance for complex operations, and avoiding caching eliminates a powerful optimization. Sticking to a server-centric, batched, and reused-data approach aligns with best practices for fast, scalable OmniStudio solutions.