Use Salesforce Flow when a business process can be expressed clearly with supported declarative elements; use Apex when the requirement needs code-level control or logic that does not fit those features well. Neither record count nor a vague label such as “complex” settles the choice. Evaluate the actual design against functionality, shared transaction limits, save order, security, maintainability, and the org’s edition and API version.
What Flow and Apex are best suited to
Flow: record processes and guided experiences
Salesforce describes Flow Builder as a point-and-click tool for automating business processes. Flows can handle record-centered work across multiple objects and can guide users through screens. That makes Flow a strong starting point when the requirement maps cleanly to its available elements and the resulting design will be understandable to the people who maintain it. See Salesforce’s comparison of Flows and Orchestrations.
Apex: code-level control
Apex is appropriate when the requirement calls for control or logic that the declarative approach cannot express well. Treat this as a requirement-led decision, not a general claim that Apex is inherently better for every complicated process. Salesforce’s Automation Best Practice Guide, published January 28, 2026, points to a feature comparison and an architect decision guide for evaluating use cases.
How to decide for a specific automation
- Describe the needed behavior. Write down the records and fields involved, decisions, user interaction, expected outcomes, and failure handling.
- Check the declarative fit. If supported Flow elements express the process plainly, and the team can maintain the Flow, start with Flow. For a guided user process, consider whether screens meet the need.
- Identify unmet requirements. If a necessary behavior or degree of control does not fit the supported declarative features, evaluate Apex for that part of the design.
- Review the whole transaction. Include every Flow, Apex component, and other automation that may execute as part of the same transaction; do not assess the new component in isolation.
- Map save order and data changes. List automation that can affect the same records and establish which steps actually run. Do not rely on an assumed sequence.
- Validate the target org. Confirm security, maintainability, deployment and operational needs, plus the relevant Salesforce edition, Flow type, and API version.
Salesforce’s automation guidance recommends choosing a tool in light of the use case and its complexity. The practical test is whether the implementation meets the requirement reliably and remains supportable in the target org.
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Flow and Apex share transaction limits
Flow is not outside Apex governor limits. Salesforce states that Apex per-transaction limits govern Flow; autolaunched flows share the transaction in which they run. If a governor limit is exceeded, Salesforce rolls back the transaction even when a Flow element has a fault connector. A fault path can help handle an element error, but it cannot make a transaction-limit breach commit successfully. Consult Salesforce’s Per-Transaction Flow Limits documentation.
Salesforce documents these selected per-transaction limits. They are platform limits, not recommended performance targets:
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| Documented limit | Salesforce per-transaction value |
|---|---|
| SOQL queries | 100 |
| Records retrieved by SOQL queries | 50,000 |
| DML statements | 150 |
| Records processed as a result of DML | 10,000 |
| Salesforce-server CPU time | 10,000 milliseconds |
These are the values in Salesforce’s current Per-Transaction Flow Limits reference, accessed in 2026. A design that uses Flow and Apex in the same transaction must account for their combined work against the applicable limits.
Check org-wide Flow limits for the actual org
Transaction limits are only part of the picture. Salesforce’s org-wide Flow limits vary by edition and Flow type, and some details depend on API version. For example, Salesforce documents a total heap-size limit of 215 MB per Flow interview for API version 61.0 and later, compared with 750 MB in API version 60.0 and earlier. That is a Flow interview-size limit; it is not the Apex governor heap figure reported in debug logs.
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Before relying on a limit quoted in an older article or for another Salesforce org, check the current General Flow Limits reference for the org’s edition, Flow type, and API version. Salesforce also summarizes permissions, design constraints, runtime, debugging, and deployment considerations in Flow Limits and Considerations.
Mixed automation makes execution order important
A record update can involve record-triggered Flows, Apex triggers, and legacy automation. If several components affect the same data, trace the actual save sequence instead of assuming that one tool always runs before another. Salesforce says it does not guarantee the order of multiple Apex triggers within the same trigger group. Its order-of-execution guidance also explains that a workflow field update does not re-fire further rules, Flows, or Apex triggers.
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These interactions can create unexpected results, data-integrity problems, or loops when automation is designed around an assumed sequence. Inventory the automation that touches the record and verify behavior against Salesforce’s order-of-execution reminders, published June 10, 2026.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Maintainability, security, and operational fit
The best option is not just the one that can perform the operation. Choose an implementation the team can safely review, test, deploy, monitor, and change. A clear Flow can make a process accessible to declarative maintainers; Apex may be the more suitable choice when code is needed to meet the requirement. In either case, verify that the design observes the org’s security model and that the team has a plan for fault handling and production support.
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There is no universal record-count threshold at which Flow should become Apex. Volume matters because of transaction limits and the surrounding automation, but it must be evaluated alongside behavior, execution context, and the target org’s configuration.
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