Causely vs IBM Cloud Pak for AIOps

Causely gives operations agents a live causal model of a system to help diagnose incidents and act proactively. Its causal inference identifies upstream causes behind cascading symptoms and is described as deterministic and explainable. Agents can traverse the causal graph to assess blast radius and compare behavior before and after deployments; they connect to the model through MCP. Causely ingests traces, metrics, logs, and alerts from existing observability tools, with OpenTelemetry, Datadog, and Prometheus named as examples. Its supported technologies include agents, telemetry sources, installation options, and workflow destinations such as Slack and Microsoft Teams. Deployment options include Kubernetes, Docker, hybrid SaaS, and bring-your-own-cloud. Causely says raw source data remains in the customer environment while distilled symptom state goes to its SaaS backend. BYOC can keep the entire application in the customer environment, including in air-gapped deployments. Professional Edition is $2,000 a month and covers up to 500 services, with 30 days of data retention and standard hybrid deployment. Enterprise pricing is custom.

IBM Cloud Pak for AIOps has no write-up on Specifiction yet; its figures are above.