API-led architecture that actually scales in practice: The System / Process / Experience pattern isnt just theoreticalweve used it to drive real reuse across Salesforce and backend systems. It reduces point-to-point integrations and lets teams move faster without re-solving the same problems. Flexible deployment model that works in real enterprise environments: CloudHub, Runtime Fabric, and hybrid options give you room to integrate legacy systems while still moving toward cloud-native patterns. That flexibility matters when youre operating at scale with mixed environments. Strong governance and lifecycle control out of the box: Anypoint gives you consistent API managementversioning, policy enforcement, and standardizationwhich is critical when multiple teams are building and consuming APIs across the same platform.
June 1, 2026
1. The learning curve is extremely high. While there are some tutorials and walkthroughs, a lot are not up-to-date or are not really translatable to real use-cases. 2. Since Salesforce has acquired Mulesoft, it was difficult to set up the overhead architecture since Mulesoft utilizes Salesforce credits for its AI functions. In addition, we didn't realize that we needed additional products from Salesforce in order to use the AI functions within Mulesoft (Datacloud being one). 3. When using the Design function of the platform, a lot of the errors stem from DataWeave errors. However, the logs are not really useful in letting the user know what is wrong. The Design function also utilizes demo data which doesn't help in assessing accuracy since there is no visibility in the underlying demo data.
December 26, 2025