Centralize all app, infrastructure, and microservices logs to improve the resiliency of increasingly complex architectures.
Focus on optimizing your production infrastructure instead of maintaining your monitoring and logging infrastructure.
Keep more data for as long as you need and manage data growth with unparalleled cost-performance at scale.
ChaosSearch transforms your cloud object storage into a hot analytical data lake. Collect, aggregate, and analyze all your cloud infrastructure and cloud services logs – including edge (Amazon CloudFront/Cloudflare), network (Amazon VPC Flow), load balancers (Amazon ALB/ELB), core (Amazon CloudTrail), application, and microservices (sidecar/Kubernetes) – for a complete view of all the data that cloud architects and engineers need to troubleshoot and optimize cloud services.
With the growth in cloud-native apps and adoption of microservices architectures, you can’t trim the number of logs analyzed to reduce costs. ChaosSearch powers up your log analytics. You’ll be able to keep more log data from multiple systems in a single repository. All your data is in your cloud object storage for as long as you need — at a fraction of the cost of existing solutions. Full visibility makes for better data analysis across all your services.
Leverage your system’s data across your full analytical workflow, from monitoring to troubleshooting. Set up dashboards in Kibana and alerts into your tools of choice to monitor system health. Hunt over all your logs to seamlessly identify root causes and visualize in Kibana to reduce MTTR.
Focus on managing your production infrastructure, not your logging system. Our architecture delivers eleven 9s durability and four 9s availability and eliminates the need to plan and manage the underlying cloud infrastructure. And it’s easy to get started. Just land your logs in Amazon S3, connect ChaosSearch, and leverage our published ElasticSearch API / Kibana UI. You eliminate management overhead and reduce costs without making your users change their behavior or the tools they use.
Indexing and querying JSON logs with complex data structures (e.g. multiple nesting levels, nested arrays, and sibling arrays) can explode database size, causing sudden cost increases. CloudOps teams and data engineers are forced to create complex pipelines and limit the volume of data available for analysis. Our JSON Flex capability allows customers to store raw JSON and analyze it as if it were structured at different nested levels — with no data explosion, no complex and unwieldy queries, and no lost insights.