Rover vs. Elastic Security

Investigate further back—without restoring archived logs.

With Rover, your security data and indexes stay in your own object storage. Search retained history without restoring archived logs or maintaining always-on search clusters.

Elastic Security also supports historical investigations. On Elastic Cloud, data moves through hot, warm, cold, and frozen tiers to keep long retention affordable, and cold and frozen data is searched from snapshots in object storage. Older data stays searchable in place, but frozen searches are slower than cold. The difference lies in how quickly history can be searched and how retention is priced.

Get started in hours. Simplify ongoing operations.

Connect Rover through HTTP ingestion or data already in your object storage. Get started in hours, with schema-on-read to reduce upfront data-modeling work and no persistent search clusters to provision. Timing depends on data readiness, access approvals, and integration scope. Initial setup does not represent a full enterprise migration.

Elastic Security collects data through Elastic Agent and Fleet integrations, Beats, or Logstash, and incoming data must map to the Elastic Common Schema (ECS). Sources without a prebuilt integration need custom mappings, which Elastic's AI-based Automatic Import can help generate. Elastic offers hosted, serverless, and self-managed deployments, and administration responsibilities vary by option.

Evaluate onboarding by the time it takes to establish usable security coverage—not simply ingest the first log.

Give every investigation more context.

Rover connects historical search, entity relationships, and AI-led investigation to build evidence-backed cases. Help your team trace related activity, reconstruct attack timelines, and understand what happened before an alert.

Elastic Security combines Timeline investigations and case management with entity risk scoring and AI features, including AI Assistant, Attack Discovery, and Agent Builder. Its managed LLM is billed at $4.50 per million input tokens and $21 per million output tokens, and on Elastic's serverless offering, workflows and agent runs are billed per execution beyond a free allowance.

Compare investigation quality, analyst effort, and the response workflows your SOC needs—not simply the presence of AI.

Detection rules: focus on coverage, not just rule count.

Your detection strategy should be driven by risk. The question is not simply how many rules you can enable, but how reliably they run as coverage grows.

Our Signal Mesh engine continuously updates detection state as telemetry arrives. A separate, on-demand query engine handles historical investigations, giving continuous detection and investigative search distinct execution paths.

Elastic Security offers a large library of prebuilt detection rules, which run on scheduled intervals with a look-back window. When runs start late because of queue backlog, cluster load, or resource limits, gaps in coverage can occur, and Elastic provides tools to find and fill them. Maintaining timely detection requires attention to rule scheduling, cluster capacity, and gap monitoring.

Understand the annual cost of visibility.

Our pricing is based on daily ingestion volume, includes multi-year retention, and adds $0.01 per query.

Elastic's own SIEM price estimate works out to $478 per GB/day each year with about 30 days of retention, and every extra day of retention adds to the bill. AI features are metered separately.

For a 1 TB/day deployment, compare annual costs using the same retention period and investigation workload. Include applicable storage, implementation, support, and administration costs—not just the subscription.