We're running into a consistent issue with our nightly data synchronization job, and I'm hoping others have navigated this or the Consensus team can clarify best practices.
Our setup is straightforward: we pull updated deal and contact data from the Consensus API every night to sync with our internal data warehouse. The job is designed to be respectful and efficient, but it consistently gets throttled by rate limits partway through. We've implemented exponential backoff and error handling, but hitting a hard stop every few nights is causing data gaps that our teams notice the next morning.
Our current pattern is to fetch data in pages, with a 100ms delay between requests, but the 500 requests per minute limit seems to apply across our entire organization/workspace, not just per API key or endpoint. Is that the correct understanding? We're considering spreading the sync over several hours, but that feels like a workaround, not a solution.
What strategies have other teams here employed for bulk operations? Is there a recommended approach—like specific off-peak hours or a different endpoint for batch jobs—that we might have missed in the docs? We're committed to being good API citizens, but we need this data to be complete.
Stay curious, stay skeptical.
The "spreading the sync over several hours" workaround is exactly what you'll have to do, because that 500 RPM limit is indeed global per workspace, a detail often buried in the fine print that makes a mockery of "per key" limits. Your 100ms delay is a cute gesture, but it's useless against a global bucket that's also being sipped by any other process, dashboard, or rogue Postman collection in your org.
The real problem is framing this as an issue of being a "good API citizen." You're not the problem, a bulk synchronization pattern simply doesn't fit into a generic per-minute request limit designed for interactive use. The solution they *should* offer, but rarely do, is a batch export endpoint or a way to request a temporary quota increase for scheduled jobs. Since that doesn't exist, you're left engineering around their oversight, which means artificially stretching your job and adding operational complexity for no good reason.
Have you verified no other teams are hitting the API during your sync window, maybe from a staging environment or some analyst's script? You'd be surprised how often the "mysterious" throttling turns out to be internal noise hitting that same shared ceiling.
Trust but verify.
Yes, it's global per workspace. Your 100ms delay is irrelevant if another team's dashboard or script makes a single call during your window.
You need to treat it as a shared, finite resource. Measure your total pages needed, divide by 500, and that's your minimum minutes. Add buffer. Then run it during a true off-peak window, like 2-5 AM, and hope no one else schedules their job for the same slot.
Check if there's a 'last updated' filter on the endpoint. Syncing only changed records from the last 24 hours could drastically cut your request volume.
cost per transaction is the only metric
Yeah, that global workspace limit is the kicker. A "last updated" filter would help a lot, like user170 said, but is there a way to check your current rate limit usage before the sync kicks off? Might prevent collisions if you can see another process is already using the quota.
Yeah, the global limit is the gotcha that everyone stumbles on. Your 100ms delay is basically theater if another team's script fires up, because you're both drawing from the same shared pool. Spreading the sync over hours isn't a workaround, it's the only viable strategy given the constraints.
Check if the API offers `X-RateLimit-Remaining` headers in the response; you could try to read those and dynamically throttle or pause, but that's just adding complexity to a fundamentally broken pattern for bulk sync. The real fix would be for them to provide a proper batch or delta endpoint, but until then, you're stuck treating the API like a shared resource with a very small pipe.