
AI SRE that investigates, fixes, and learns from every production issue.
By Tanmay Verma, Founder · Last verified 02 Jun 2026
In short
Cleric — AI SRE that investigates, fixes, and learns from every production issue. Best for SREs automating alert investigation and root cause analysis, Engineering teams using microservices or Kubernetes, Organizations seeking to preserve institutional knowledge from incidents. Contact Sales pricing.
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Cleric is a compelling choice for engineering teams drowning in on-call noise, especially those with microservices or Kubernetes environments. Its self-learning capability sets it apart, but evaluate its compatibility with your existing observability stack before committing.
Compare with: Cleric vs Resolve AI, Cleric vs Cotality, Cleric vs ColdReach
Last verified: June 2026
When to pick Cleric: If your team spends too much time investigating low-severity alerts and you want to preserve knowledge despite engineer turnover, Cleric's automated investigation and learning engine are a strong fit. It's ideal for teams using Kubernetes, microservices, or cloud infrastructure who already have observability tools like Datadog. When to pass: If your stack relies on niche or legacy monitoring tools that aren't supported, Cleric may not integrate easily. Also, if you prefer full human control over investigations or have very small on-call rotations, the ROI may not justify the cost. Comparison: Compared to alternative like PagerDuty's SRE agent or Grafana's assistant, Cleric's unique advantage is its operational memory—fixes become institutional knowledge. However, it is read-only by default, so for teams needing automated remediation without human validation, Cleric may require a workflow change. Real-world usage caveats: While Cleric claims 5-minute time to root cause, actual performance depends on the complexity of your stack and quality of your observability data. Also, the 'self-learning' aspect requires manual review of fixes initially to build accuracy. Security-conscious teams will appreciate the SOC 2 compliance and encrypted data, but note that Cleric's write access is optional and gated.
Skip Cleric if Skip Cleric if you don't have a mature observability stack (Datadog, Prometheus, Kubernetes) or cannot grant read-only access to production systems.
Across the latest 4 updates: 4 feature updates.
Cleric details integration with Tailscale to access private resources for AI SRE operations.
Blog advocates shifting focus from reviewing AI agent outputs to reviewing their decision-making process.
Explains the importance of memory in AI SRE agents for context retention and improved incident response.
Argues that hooks alone are insufficient for AI agent security; deeper measures are needed.
How likely is Cleric to still be operational in 12 months? Based on 6 signals including funding, development activity, and platform risk.
Cleric is a self-learning AI SRE agent that automates alert investigation, root cause analysis, and fix recommendation across your engineering stack. Designed for SREs, engineers, and engineering leaders, Cleric integrates with observability and incident management tools to reduce on-call burden and accelerate incident resolution. Key features include automated alert investigation (bypassing dashboards like Datadog and Cloudflare), root cause analysis delivered before the alert lands, and a learning system that preserves institutional knowledge from every fix. Cleric maps your services, dependencies, and ownership automatically, and tests hypotheses in parallel to systematically eliminate guesses. The platform works inside your existing tools and learns from failure: every resolution compounds knowledge, making future investigations faster. Engineering teams report 5-minute time to root cause and 92% actionable findings, with over 200,000 production-grade investigations completed. Cleric is SOC 2 Type II compliant, read-only by default, and paranoid about security. Unlike generic AI assistants, Cleric is purpose-built for SRE workflows and operational memory."
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Concrete scenarios for the personas Cleric actually fits — and what changes day-one when you adopt it.
A PagerDuty alert fires for high latency in a microservice. Cleric automatically begins investigation, querying Datadog metrics, Kubernetes pod logs, and recent GitHub deployments.
Outcome: Within 5 minutes, Cleric surfaces a root cause (a recent code change increased database connection pool exhaustion) with a confidence score of 92%. The SRE reviews the transparent reasoning and approves the rollback.
You want to reduce on-call burnout and preserve incident knowledge. Cleric is set up to learn from every investigation.
Outcome: After 3 months, Cleric has resolved 200+ alerts autonomously, reducing MTTR by 50%. Junior engineers gain confidence by reviewing Cleric's step-by-step analyses and the institutional memory grows.
Cleric requires read access to your observability stack and Kubernetes; without these integrations, its investigative capabilities are severely limited. It is not a fully autonomous fix executor—human review is expected before actions are taken. Pricing is not publicly listed, meaning organizations must engage sales to understand cost.
The company stage and team size where Cleric's pricing actually pencils out — and where peers do it cheaper.
Cleric's contact-only pricing is best suited for mid-to-large engineering teams that already invest in observability tooling. It's more expensive than simpler AI alert summarizers but offers autonomous investigation and memory. For smaller teams, Grafana Assistant (free with Grafana) or PagerDuty SRE Agent (per-seat) may be more accessible.
How long it actually takes to get something useful out of Cleric — broken out by persona, not the marketing-page minute.
For teams with existing Datadog, Prometheus, and Kubernetes, setup takes about an afternoon (2-4 hours) to connect integrations and configure Slack routing. SSO and automation setup may require an additional day. Cleric begins adding value immediately upon connecting your first data source.
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Pricing, brand, ownership, or deprecation changes worth knowing before you commit. Most-recent first.
Common stack mates teams adopt alongside Cleric, with the specific reason each pairing earns its keep.
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