Prelert is a machine-learning-driven anomaly detection and behavioral analytics product that automatically learns baseline patterns in large streaming operational and security datasets and flags anomalous behavior in near real time, with integrations for Elasticsearch/ELK and Splunk and an API to embed analytics.
Published Oct 9, 2026
Comparisons focus on current anomaly-detection capabilities in modern platforms: Elastic ML (successor), Splunk ecosystem ML/anomaly tooling, and Datadog anomaly detection.
| Aspect | Prelert (standalone, legacy) | Elastic ML (successor home) | Splunk (platform + ML/anomaly tooling) |
|---|---|---|---|
| Starting Price | Not publicly available (legacy) | Varies; serverless pricing shows ML billed by machine-learning capacity units (plus storage/ingest) | Varies by model; Splunk pricing is multi-model and often quote-based |
| Appropriate for | Legacy deployments only | Teams standardizing on Elastic for security/observability and wanting integrated anomaly detection | Teams standardizing on Splunk; broad enterprise log/SIEM use cases |
| Key Strength | Early ML anomaly detection focus; connectors/API | Integrated with Elastic Stack and Kibana workflows; scalable ML nodes | Broad ecosystem; many enterprise deployments; extensive ingestion/search/analytics capabilities |
| Key Weakness | Standalone product viability and support risk | Costs can grow with ingest and ML compute; requires Elastic-specific expertise | Cost and complexity at scale; licensing and feature packaging can be complex |