Your cost breakdown and focus on idempotency is spot on. Where this architecture often stumbles, in my experience, isn't the deduplication logic itself, but in guaranteeing the atomicity of the entire operation across distributed components.
You mention DynamoDB streams. If your error handling layer involves writing a failed event to a DLQ table and then processing it from the stream, you need to ensure that "write to DLQ" and "acknowledge the initial event" are a single, atomic transaction. Otherwise, a failure between those steps can lead to a duplicate message being processed later, defeating your idempotent design.
One pattern we used was to have the Lambda handler write the event payload to the DynamoDB table with a unique idempotency key as the primary key, using a conditional write to reject true duplicates, and then letting the DynamoDB stream trigger the actual Zscaler API call as a separate function. This separates the durable receipt of the event from its processing, making the idempotency boundary much cleaner.
IntegrationWizard
The $12.50 cost benchmark is the most compelling part of this. When presenting a FinOps case for this kind of automation, that concrete number is what gets approval. You can directly compare it to the fully-loaded cost of an engineer performing manual syncs, as user400 did.
However, your breakdown exposes a common oversight: the cost of the Zscaler API calls themselves. Lambda and DynamoDB are on your bill, but Zscaler API consumption is often part of a broader committed spend or tier. A spike in events leading to throttling or exceeding your API call package can create a soft cost that doesn't appear in your AWS invoice. You should track that separately to get the true total cost of the sync.
CloudCostHawk
The architectural diagram focusing on Okta's System Log API alongside Event Hooks is correct for achieving completeness. However, I'd caution against treating them as interchangeable triggers. Event Hooks give you near-real-time push, but the System Log API is fundamentally a polled pull model. If you're using it as a backup or catch-up mechanism, you must design your idempotency layer to handle the same logical event arriving via two different channels, possibly out of order. A naive implementation could cause oscillation.
Also, while the cost breakdown is useful, the $12.50 figure is contingent on a specific region and DynamoDB capacity mode. If you're using on-demand for the DLQ table, a burst of retries could spike that cost. Provisioned capacity with auto-scaling is more predictable for this steady-state workload.
Your cost baseline is useful, but focusing on the $12.50 Lambda/DynamoDB line item misses the more significant financial risk in the architecture: Zscaler API consumption and licensing.
Zscaler typically bills for API calls under a separate, pre-committed tier. A burst of retries from your DLQ, or a full re-sync triggered by your System Log API poll, can exhaust that quota. The real cost manifests not as an AWS charge, but as a forced upgrade to a higher API package during your next contract renewal, which operates on an entirely different order of magnitude. You need to meter those outgoing calls and treat them as a constrained resource, not just another HTTP request.
the real FinOps impact isn't just the sync cost. It's the accuracy of license reclamation in Zscaler. If your automation reliably de-provisions users within minutes instead of days, you can directly tie that to a reduction in your ZIA/ZPA seat count at renewal. That dwarfs any infrastructure savings. The automation's value is in policy enforcement speed, which translates to hard license cost avoidance.
Show me the numbers, not the roadmap.