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Kubernetes-native platform for building and running serverless applications, handling autoscaling (including scale-to-zero) and traffic management.

• DevOps

Kubernetes event-driven autoscaler that scales workloads based on the number of events needing processing, including scaling to zero.

• DevOps

Kubernetes performance and scale testing tool that generates configurable cluster load to measure how a cluster behaves under stress.

• Testing

Horizontally scalable, distributed time-series database and metrics platform built at Uber for very large-scale monitoring.

• Monitoring

Kubernetes-native Java framework designed for fast startup times and low memory footprint, tailored for containers and serverless.

• Backend

Distributed wide-column NoSQL database designed for massive scale and zero single point of failure across multiple data centers.

• Database

Distributed filesystem and object storage system built for cloud-native, large-scale data-intensive applications.

• Storage

Real-time analytics database built for sub-second queries over continuously streaming and historical event data at scale.

• Analytics

Distributed POSIX filesystem that stores data on object storage like S3, combining familiar filesystem semantics with cloud-scale storage.

• Storage

Kubernetes-native platform for serving machine learning models at scale, standardizing model deployment across frameworks.

• AI & Machine Learning

Real-time, per-second monitoring dashboard that auto-discovers metrics across your infrastructure with zero configuration.

• Monitoring

Cloud-native search engine purpose-built for observability data, indexing logs and traces directly on object storage for cost-efficient search at scale.

• Search

Distributed data integration platform for moving and transforming data between databases, data lakes, and messaging systems at scale.

• Backend

Unified analytics engine for large-scale batch and stream data processing, one of the most widely adopted big data frameworks.

• Analytics

Extends Prometheus with long-term storage, global querying across clusters, and high availability for large-scale metrics deployments.

• Monitoring

MySQL-compatible distributed SQL database that scales horizontally while supporting both transactional and analytical workloads.

• Database

High-throughput, memory-efficient inference and serving engine for large language models, built for running LLMs in production at scale rather than on a single local machine.

• AI & Machine Learning