Cloud infrastructure for machine learning at scale

Deploy, manage, and scale machine learning models in production.

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Serverless workloads

Realtime

Respond to requests in real-time and autoscale based on in-flight request volumes.

Async

Process requests asynchronously and autoscale based on request queue length.

Batch

Run distributed and fault-tolerant batch processing jobs on-demand.

Automated cluster management

Cluster autoscaling

Elastically scale clusters with CPU and GPU instances.

Spot instances

Run workloads on spot instances with automated on-demand backups.

Environments

Create multiple clusters with different configurations.

CI/CD and observability integrations

Provisioning

Provision clusters with declarative configuration or a Terraform provider.

Metrics

Send metrics to any monitoring tool or use pre-built Grafana dashboards.

Logs

Stream logs to any log management tool or use the pre-built CloudWatch integration.

Built for AWS

EKS

Cortex runs on top of EKS to scale workloads reliably and cost-effectively.

VPC

Deploy clusters into a VPC on your AWS account to keep your data private.

IAM

Integrate with IAM for authentication and authorization workflows.

Scale machine learning applications

Model serving

Deploy machine learning models as realtime workloads and scale inference across CPU or GPU instances.

Machine learning operations (MLOps)

Create services that continuously retrain and evaluate models to maintain their accuracy over time.

Microservices

Scale compute-intensive microservices without dealing with timeouts or resource limits.

Image, video, and audio processing

Scale data processing pipelines to handle large structured or unstructured data sets.

Discovering Cortex has been a lifesaver, it is servicing half a billion API calls each month for us. The ease with which we’ve been able to deploy Cortex has facilitated rapid development across our team, enabling us to meet the needs of our highly demanding customers.

Madison Bahmer - Two Six Technologies