Get full observability. Only pay for what’s important to your organization. True real-time alerts, anomalies, and dashboards without indexing or storage.
Debug logs and Error logs shouldn't cost the same. Get full coverage with low ingestion costs. Monitor your data with advanced alerts, anomalies, dashboards, LiveTail and more, without the cost of indexing and storing everything.
Dynamically implement a cap on the amount of data sent to Coralogix from a specific App/Service to prevent an account from reaching its daily quota. Your cost-effective bill remains predictable even when log volume spikes
Selectively archive any log data to your Amazon S3 bucket and keep the data forever.
The only solution that allows you to directly query your archived logs using the familiar Elasticsearch syntax via UI or CLI, with no affect on your daily quota.
Don't mount GBs of data to analyze a specific log record. The only the product that allows direct reindexing from archive using the familiar Elasticsearch syntax.
The only the product that allows direct reindexing from archive using the familiar Elasticsearch syntax.
Coralogix provides a real-time, pre-index live-tail so that you can view all your logs from all your servers in one place and with zero latency.
Create long term metrics from logs without storing them - for maximum business value and ad-hoc historical metrics.
ML-powered Anomalies & Alerts
The Coralogix data pipeline was built to run ML models, so we can provide anomalies and automatic data clustering without storing or indexing a single raw log
“We saved 50% on our monitoring costs with Coralogix”
Refael - SRE DevOps Team Leader
What’s the true cost of ‘homegrown’ ELK?
Get all of the benefits of an ML-powered logging solution at only a third of the cost and with more real-time analysis and alerting capabilities than beforaSee the Total Cost of Ownership of the ELK stack broken down into visible costs and hidden costs. A tool may have a respectable price tag but have significant costs hiding beneath the surface.e by defining the data pipeline based on its business value.
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