Use case
Classification & summarization
High-volume classification and summarization jobs on region-pinned inference, with fair-use plan limits per API key.
The problem
Classification and summarization are steady background workloads — tickets, messages, feeds — where the volume adds up and every item touches the provider on the other end of the API.
Why data control matters
Background jobs amplify what a default provider setting does: more items, more data, more destinations. Pinning the workload to one market with dedicated infrastructure — prompts and outputs not stored by default, no sharing, no training on shared or third-party models — removes the drift before it starts.
Architecture summary
Your job queue calls chat completions per item (or per batch the context fits in). The per-key, fair-use plan limits keep the workload bounded on both sides.
Model strategy
Start with Qwen 3.8 for jobs that need long context; the same chat-completions shape works for single-item classification and multi-document summarization.
Regional considerations
Keep each data source in its own market when sources are regulated differently; otherwise one endpoint per team in the market they operate in (EU/USA/LATAM).
Governance & data boundary
Inference — prompts and outputs — not stored by default, never shared and never trained on; processing in the market's operating location. Rate limits are per key, under a fair-use plan.
Integration example
for item in queue:
r = client.chat.completions.create(
model="qwen-3-8",
messages=[{"role": "user",
"content": f"Classify (legal/finance/other):\n{item}"}],
)Frequently asked questions
Is there a hard token limit?
Plans are fair-use with per-key rate limits — the limit is about abuse, not about capping your workload mid-job.
Which region should the job run in?
Where the source data lives. The EU, USA and LATAM markets are operated from Germany, the USA and Colombia respectively.
Start with control
Join the waitlist for an account in your region.