Operations | Sarvam API Docs

Everything you need to run a Sarvam endpoint in production.

Instance sizing & pricing

All three models run best on the ml.g6e (L40S) family. Start with the recommended instance and scale up only if a single instance can’t meet your throughput.

Model	Real-time / streaming	Batch
Saaras v3 (STT)	`ml.g6e.xlarge`	not supported
Bulbul v3 (TTS)	`ml.g6e.xlarge`	`ml.g6e.xlarge`
Sarvam Vision (OCR)	`ml.g6e.xlarge`	`ml.g6.xlarge`

Larger and multi-GPU ml.g6e instances scale up accordingly. See Get started for the per-instance concurrency table.

Pricing is the software fee shown on the AWS Marketplace listing, billed per host-hour, plus the underlying AWS infrastructure cost. Rates vary by region and package version — always confirm the current rate on the Speech-to-Text or Sarvam Vision listing. Usage-based, no end date, cancel anytime.

Autoscaling

Attach an Application Auto Scaling policy to the endpoint variant to add and remove instances with load — typically targeting SageMakerVariantInvocationsPerInstance or GPU utilisation.

Async endpoints can scale to zero. Set the minimum instance count to 0 so you pay nothing between jobs — the endpoint spins an instance up when a request lands. This is the cheapest option for spiky or occasional workloads.

Monitoring

CloudWatch metrics

Track Invocations, ModelLatency, OverheadLatency, Invocation4XXErrors/5XXErrors, and GPU/host utilisation per variant. Alarm on latency and error rate.

Container logs

The model container streams logs to CloudWatch Logs under the endpoint’s log group — your first stop for diagnosing failed invocations.

Security & data residency

Network isolation — deploy with EnableNetworkIsolation=True; the container has no outbound internet.
Stays in your cloud — audio and documents are sent only to your endpoint and never leave your VPC.
VPC & encryption — run the endpoint in your VPC, use your KMS keys for S3 (batch/async) and volume encryption, and scope the execution role to only the buckets it needs.

Updating model versions

Create a new model and endpoint config

Create a fresh model from the new ARN and a new endpoint configuration pointing at it.

Update the endpoint in place

Call update_endpoint with the new config. SageMaker performs a rolling (blue/green) update with no downtime, then retires the old variant.

Remember the split between the package version (what you subscribe to, e.g. saaras:v3.1) and the API model id (what you send in requests, e.g. saaras:v3). A version bump changes the package ARN — it does not necessarily change the request-time model id.