SerOps vs Causely

Causely builds a causal model of your system from its telemetry and gives root causes, blast radius and remediation guidance to your agents and workflows. SerOps is an open-source application your team uses directly: it reads your logs, metrics, traces, deploys and code, and explains incidents with an AI model you choose.

Facts checked against each project's own documentation on 21 October 2026. Spotted something out of date? Tell us.

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The short answer

Pick Causely if you want deterministic causal reasoning feeding your own AI agents and automation: its documentation says it uses causal models rather than LLMs to compute root cause, blast radius and remediation guidance from your system's causal graph, exposed through an MCP server.

Pick SerOps if you want a self-hosted, open-source tool your on-call team opens during an incident, with the analysis matched to the deploy and commit that caused it, and nothing to buy: the AI analysis is in the free Community edition.

SerOps and Causely side by side
	SerOps	Causely
Approach	AI model given the evidence for each window	Causal models over a graph of your system
Main user	Your on-call team, in the SerOps app	Your agents and workflows, through MCP
Telemetry	Queries 14 log, metric and trace backends in place	OpenTelemetry, Prometheus, Datadog and others, plus eBPF
Code and deploys	Commits, diffs, CI/CD runs, Argo CD and Flux deploys	Not documented on the pages we checked
Deployment	Self-hosted: Docker Compose, Helm	Hybrid SaaS or BYOC, on Kubernetes
Pricing	Free software; your AI provider's tokens	Not published
License	AGPL-3.0	Proprietary

Causely facts from causely.ai and its documentation.

How each one reasons

Causely maps your services and their dependencies from telemetry (it lists OpenTelemetry, Prometheus, Datadog, Dynatrace and Elasticsearch among its sources, and eBPF auto-instrumentation to fill gaps) and infers causes from that causal graph. It works on demand when an alert fires and proactively on emerging degradation.

SerOps gives an AI model the evidence for one project, environment and time window: log excerpts, metrics, traces, recent deploys and the diffs of recent commits, after redacting secrets and personal data. The result is a root cause in plain language, the fix, and links to the evidence it used, which you can question further in AI chat.

SerOps AI chat explaining that a deploy shrank the Redis connection pool, with links to the evidence — Asking SerOps about an incident it analysed.

Deployment and pricing

Causely runs in Kubernetes and offers a hybrid SaaS (components in your environment, backend hosted by Causely) or BYOC (the whole application in your environment). Its site has no public price list; it offers a trial and a contact form.

SerOps is AGPL-3.0, self-hosted with Docker Compose or Helm on Kubernetes or plain VMs. You pay only your AI provider's tokens, or nothing with Ollama.

Frequently asked questions

Does SerOps work with AI agents?

SerOps has a REST API for analyses, issues and investigations. Its main interface is the app your team uses during an incident.

Do I need eBPF or new agents for SerOps?

No. SerOps reads the backends you already run. Your log shippers and exporters need a few labels (project, service, environment).

Can SerOps run fully on-premises?

Yes: self-hosted, with a local model through Ollama, nothing leaves your network.

Is SerOps free?

SerOps Community, the complete product with the AI analysis, is free and open source under AGPL-3.0 when you self-host. Enterprise adds governance features for large organisations; nothing moves out of Community.

See it on real data

The live demo runs on seeded incidents: no signup. Or run SerOps on your own infrastructure; it is free and open source (AGPL-3.0).

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