Deploy stage
Best for
Moving a tuned model from a notebook to a monitored endpoint with an SLA.
How it runs
Pick the serving stack and hardware that meet your latency targets at the lowest cost.
Serving planRuntime, GPU type, and cost per 1K requests, all benchmarked.
Quantise, batch, and tune caches. Nothing ships unless it passes the evaluation gate.
Inference endpointAn OpenAI-compatible API with autoscaling, authentication, and rate limits.
Start in shadow mode, move to a canary release, then full traffic, with instant rollback.
Tracing dashboardsPer-request traces, token spend, latency percentiles, and quality sampling.
On-call runbooks, drift alerts, and monthly model health reviews.
Retraining pipelineScheduled jobs that collect failures and refresh the training data.
In practice
Numbers from the base model versus the model we shipped.
A frontier-API prompt pipeline was accurate enough to demo but too slow and too expensive at production volume.
Routing accuracy
Before: 71%After: 93%
Cost per 1K tickets
Before: $4.80After: $0.60
FAQ
A quantised 7–8B model typically runs on a single mid-range GPU and handles thousands of requests an hour. At volume, that is often far below per-token API pricing.
Yes. We deploy with your infrastructure-as-code, CI, and observability stack rather than bringing our own.
Often paired with
Share a sample of your data and a target metric. We'll come back with a plan and a timeline.
We reply within one business day (IST).
