ERR_TIMEOUT_EXCEEDED on Make.com: Scenario execution timeout — Make.com reports "The scenario execution has exceeded the time limit" or the scenario run shows status "Incomplete". Root cause: Make.com enforces a maximum scenario execution time of 40 minutes on all standard plans. Scenarios that process large datasets, make many sequential API calls, or wait for slow external services will hit this limit. The scenario stops mid-execution and marks the run as Incomplete, which means some bundles were processed and some were not — creating a partial execution state that can cause data inconsistencies. Step 1: Identify which module is causing the timeout. Open the scenario run history (Scenarios → your scenario → History tab). Click the incomplete run. Make.com shows a visual timeline of module executions with durations. The module that was running when the timeout occurred will be highlighted. Note the module name and the bundle number it was processing — you will need this to resume from the correct point. Step 2: Reduce the number of bundles processed per run. The most effective fix for timeout issues is to process fewer records per execution. In the trigger module (e.g., Google Sheets → Watch Rows, or HTTP → Watch), set the "Maximum number of returned bundles" to a lower value (try 50–100 instead of 1000). Make.com will process this batch in one run, then automatically start a new run for the next batch. This keeps each run well under the 40-minute limit. Step 3: Replace sequential API calls with batch operations. If your scenario makes one API call per bundle (e.g., creating 500 HubSpot contacts one at a time), it will time out. Check whether the target app supports batch operations. HubSpot, Salesforce, and Google Sheets all support bulk create/update endpoints that process hundreds of records in a single API call. Replace the per-bundle module with a single Aggregator → Batch API call pattern. Step 4: Split the scenario into a trigger scenario and a processing scenario. For complex workflows, separate the data collection from the data processing. Scenario 1: collects records and sends them to a Make.com data store or a webhook. Scenario 2: triggered by the webhook or scheduled to read from the data store, processes records in small batches. This pattern prevents any single scenario from accumulating enough work to time out. Step 5: Handle the partial execution to avoid data inconsistencies. When a scenario times out mid-run, some bundles were processed and some were not. Go to the incomplete run → click "Resume" if Make.com offers it, or manually identify the last successfully processed bundle from the run log. If your trigger is a database or spreadsheet, add a "Processed" flag column and filter to only process unprocessed records. This makes your scenario idempotent — safe to re-run without duplicating already-processed data.