Deliver superior experiences for millions of customers using your SaaS applications while also reducing your infrastructure expenses up to 50%.

Deploy Azul on hardware, and deliver consistent, secure muscle across all IoT-connected workflows, on smart edge devices, on secure gateways, and for cloud intelligence.

Provide your customers with secure, glitch-free e-Commerce experiences that keep them loyal and happy, while also reducing your costs.

Migrate Java applications to the cloud, develop cloud-native Java microservices deployments, and reduce cloud infrastructure costs.

Reduce infrastructure overhead by as much as 50%, bolster security, and improve performance of databases, data lakes, data analytics tools, and search platforms.

Accelerate your digital transformation, drive cloud migration, automate workflows, and provide secure and better user experiences at up to 90% less cost.

Minimize cybersecurity risks in Java applications that manage personal data and identity, secure online and physical transactions, and detect fraud.

Bolster the security of point-of-sale devices, detect fraudulent transactions at speed, and assure compliance with PCI-DSS standards.

Optimize low-latency trading and risk management systems while offering the fastest, most predictable response times with the lowest infrastructure costs.

Reduce infrastructure overhead and improve performance when using these technologies.

Azul reduces the infrastructure costs of Cassandra and big data technologies.

Handle thousands of messages per second with Azul.

Improve search while cutting costs across your stack.

Faster searches. Better results. Less infrastructure.

Handle more HDFS metadata and larger clusters while reducing infrastructure costs and complexity.

Consistently improve Spark and Scala response times.

Increase Ignite capacity, reduce pause times & remove Java-caused limits to scalability.

Sub-millisecond response times with Azul, every time.

Increase heap size, reduce pauses, save costs.

Eliminate application jitter, response time inconsistency, and in-memory data limitations.

More flexible memory management with reduced pauses and improved cluster efficiency.