Get Started

Score terabytes overnight — 10x better throughput per dollar than real-time endpoints.

Batch Inference Engine

Runs large-scale batch prediction jobs on terabytes of data with distributed execution, output partitioning, and cost-optimized compute scheduling for overnight or periodic scoring workloads. Supports banking risk scoring, telecom churn batch analytics, and industrial predictive maintenance overnight runs.

Why It Matters

BatchInferenceEngine recognizes a truth real-time-obsessed vendors ignore — most enterprise scoring doesn’t need millisecond latency, it needs terabyte throughput at the lowest possible cost.
Distributed batch execution scores entire portfolios, customer bases, and equipment fleets overnight at 10x better throughput per dollar than keeping real-time endpoints running.
1.png

10x throughput per dollar

Paying for always-on real-time endpoints to run nightly scoring wastes money — cost-optimized batch scheduling delivers the same predictions at a fraction of the spend.

2.png

Built for enterprise-scale workloads

Banking risk scoring across full loan books, telecom churn analysis over entire subscriber bases, predictive maintenance across whole plants — terabyte jobs finish overnight, reliably.

3.png

Right tool for the right workload

Distributed execution, output partitioning, and smart scheduling handle the operational realities of massive periodic jobs that ad-hoc Spark scripts routinely fumble.

The Cloudly Advantage

BatchInferenceEngine covers the workhorse half of production ML that InferenceGateway’s real-time serving doesn’t — together they let Cloudly match the right serving economics to every client workload.
1.png

Serving portfolio completeness

Real-time (InferenceGateway), edge (EdgeDeploy), and batch — Cloudly right-sizes serving costs per workload, a consultative story point vendors with one serving mode can't tell.

2.png

Use cases mirror our verticals

Risk scoring (banking), churn analytics (telecom), predictive maintenance (manufacturing) — BD walks in with the exact workload the account already runs.

3.png

Spark and Ray cluster revenue

Distributed batch infrastructure design, tuning, and operations are core Cluster Services engagements with recurring managed-operations income.

4.png

Cost-audit sales motion

Many enterprises run batch workloads on real-time endpoints today — a simple serving-cost audit surfaces immediate savings and opens the door to the wider platform.

5.png

Pairs with ComputeOrchestrator and ModelCompressor

Spot-instance scheduling plus compressed models multiply the batch savings — the full cost-efficiency trilogy applied to the biggest jobs.

The Final Takeaway

Dataflow and SageMaker pull-through — Batch Transform and GCP Dataflow integrations open AWS and GCP migration and monitoring conversations across both hyperscaler footprints.

Powered By

Apache Spark MLlib

Core to the BatchInferenceEngine technology stack.

AWS SageMaker Batch Transform

Core to the BatchInferenceEngine technology stack.

Dask

Core to the BatchInferenceEngine technology stack.

Ray Data

Core to the BatchInferenceEngine technology stack.

Let's start a quick, free consultation

Get Started