Elastic Observability is a powerful search and observability data platform, logs, metrics and APM on the Elastic Stack. If you want autonomous, governed resolution rather than a data platform to query, here is an honest comparison with Ops Singularity, and a note on cost at scale.
Elastic Observability is built on the Elastic Stack's search engine, which makes it excellent for log-heavy observability: fast search across huge volumes, flexible querying, and a unified store for logs, metrics and traces. Teams that want to own their observability data platform value it. Teams look for an alternative for two reasons: the operational cost of running Elasticsearch at scale (storage, cardinality, cluster management), and the fact that Elastic surfaces problems for a human rather than resolving them. If the goal is fewer incidents reaching an engineer at all, a search platform, however powerful, is a different tool from an autonomous resolution layer.
| Dimension | Elastic | Ops Singularity |
|---|---|---|
| Primary focus | Search-centric observability data platform (logs, metrics, traces) | Autonomous, governed resolution across ten operational domains |
| Detection vs resolution | Powerful search and dashboards; an engineer investigates and fixes | Closes the loop: ProcBot executes the fix, Sherlock validates it |
| Operating cost at scale | Elasticsearch storage, cardinality and cluster management to run | Commercial platform focused on resolution, not data-store operations |
| Governance of actions | Alerting and search; remediation is manual | Every action is a reversible, audited Action Ticket with approval gates |
| Operational breadth | Observability data (logs, metrics, traces) | Ten pillars including security, cost, process and the managed estate |
| Deployment | Self-managed or Elastic Cloud | SaaS, on-premises or fully air-gapped |
Elastic Observability is the better choice if you want a unified search, logging and observability data platform you can self-manage, with powerful log search at its core and full control of the data store.
Sentinel AI runs the Observe, Investigate, Act, Optimize loop and executes the fix through ProcBot, so many incidents never need a human at all.
Actions run as reversible, audited Action Tickets with approval gates, so autonomy is something an auditor or a change board can accept.
One intelligence layer across telemetry, service, infrastructure, security, data, cost, process, DevSecOps and the managed estate, deployable on-premises or fully air-gapped.
Not necessarily. Many teams keep Elastic for what it does well and add Ops Singularity to resolve incidents autonomously, feeding context and updates back through Integration Connectors. One tells you what is wrong; the other fixes it under governance.
Autonomous execution of the fix through governed, reversible Action Tickets, validation via Sherlock, and breadth across ten operational domains, so fewer incidents reach a human in the first place.
Yes. Teams keep Elastic as their observability data platform and add Ops Singularity to resolve incidents autonomously, connected through Integration Connectors and OpenTelemetry.
Bring a real incident. We will show you Sentinel investigate, act and verify end to end, with every action reversible and audited.