Running Bulk VIN Jobs the Right Way
When you need to enrich an entire inventory, a dealer catalog, an auction lot, a fleet snapshot, single lookups in a loop are the wrong tool. The VIN Doc bulk endpoint is built for volume, and using it well means thinking in jobs, not requests. The shift in mindset is the whole article: a job is a unit of work the platform owns until it is done, not a connection you hold open.
Submit a job, not a loop
A bulk submission accepts a list of VINs and returns a job identifier immediately. Processing happens asynchronously on our side, so you are never holding a connection open while thousands of records resolve. You poll the job status or, better, subscribe to a completion webhook and let us tell you when it is ready.
POST /v1/jobs
{ "vins": ["1HGCM82633A004352", "WBA3A5C5XEF...", ...] }
=> { "job_id": "job_4d1", "status": "queued" }- Batch VINs into jobs sized for your tolerance
- Prefer a completion webhook over tight polling
- Capture the job ID for traceability
Size your batches deliberately
There is no universal right batch size; there is the size that fits your reconciliation tolerance. Bigger jobs amortize overhead but take longer to come back and are coarser to retry. Smaller jobs return sooner and isolate failures but cost more orchestration. Pick a size, measure the turnaround, and adjust rather than guessing once and forgetting.
Design for partial success
A job of ten thousand VINs will almost never be ten thousand clean hits. Some VINs are malformed, some have no records, some come back with low-confidence matches. The result set reports a per-VIN status so you can route the clean ones forward and queue the rest for review, instead of treating the whole job as pass or fail.
{ "vin": "1HGCM82633A004352", "status": "ok", "confidence": 0.97 }
{ "vin": "BADVIN", "status": "invalid" }
{ "vin": "JH4KA...", "status": "no_records" }Respect concurrency limits
Your plan defines how many jobs run in parallel and how large each can be. Submitting beyond the limit returns a 429 with a retry hint. Treat that as the system protecting your own throughput, and let a queue smooth your submission rate rather than firing everything at once. A client that respects the limit finishes sooner than one that fights it.
- Read per-VIN status before trusting the aggregate
- Back off on 429 instead of retrying immediately
- Reconcile results against your source list
Track jobs to completion
Persist the job ID the moment you receive it, alongside the input list it covers. If your process restarts, you can resume from status rather than resubmitting and paying twice. The job ID is also your support handle: if a job behaves oddly, that single identifier lets us trace its entire lifecycle.
Plan for cost and scheduling
Volume work has economics single lookups never expose. A bulk job that enriches an entire catalog is cheaper per VIN than the same VINs resolved one at a time, but it is still real spend, so schedule it where it does the least damage to your rate budget. Run large backfills off-peak, stagger recurring jobs so they do not collide, and cache aggressively so you are not re-enriching VINs that have not changed since last week. The combination of bulk submission, caching, and sensible scheduling is what turns a six-figure VIN count from a budget problem into a line item you can forecast. Treat the bulk endpoint as a tool for steady, planned throughput rather than a firehose you open whenever a list lands on your desk.
Close the loop
A bulk job is not done when it returns, it is done when every VIN is either enriched, flagged for review, or explicitly marked unresolvable. Reconcile the output against the input every time, and your inventory enrichment becomes a process you can trust rather than a script you babysit. You can rehearse all of this against the sandbox first: the free trial runs two days for €3.99, then €49.99/month, auto-renews, and cancels anytime, which is more than enough to prove a bulk pipeline before you point it at a real catalog.
