Cleric
AI SRE that learns from production issues across your stack.
Cleric is a strong choice for SRE teams drowning in alert noise who already have mature observability. Its stack mapping and compounding memory genuinely reduce toil. But the per-issue pricing demands volume—light-traffic teams won't see ROI—and setup assumes a solid Kubernetes foundation.
Verified 18d ago · liveness 95/100 · cite: rightaichoice.com/tools/cleric
- SRE teams handling high alert volume across Kubernetes and microservices
- Engineering leaders wanting to reduce on-call fatigue and preserve institutional knowledge
- Platform teams needing automated root cause for deployment failures
- Organizations with mature observability stacks looking to accelerate incident response
- Small teams with low alert volume (ROI may not justify setup)
- Startups without established observability instrumentation
- Teams needing real-time alerting without AI investigation
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Skip Cleric if you have low alert volume, no Kubernetes or microservices, or lack mature observability instrumentation—ROI will be slim and setup friction high.
Going past your monthly credit pool requires manual top-ups with written sign-off; there is no automatic overage, but running out of credits can halt investigations mid-week.
Cleric's per-Issue pricing ($20/Issue) fits teams with consistent alert loads—50+ issues/month—who can justify a $2K/month annual contract. For lower volumes, lighter tools like PagerDuty SRE Agent (tiered per alert) or Bits AI (per-seat pricing) may be cheaper. At high volume, Cleric's flat-rate Team plan becomes more cost-effective than per-seat alternatives.
In short
Cleric — AI SRE that learns from production issues across your stack. Best for SRE teams handling high alert volume across Kubernetes and microservices, Engineering leaders wanting to reduce on-call fatigue and preserve institutional knowledge, Platform teams needing automated root cause for deployment failures. Plans from $200024000/mo.
What's new in Cleric
Checked 17 days agoAcross the latest 5 updates: 1 feature update and 4 news mentions.
White Paper: The State of AI SRE
Cleric published a white paper analyzing the landscape of AI in site reliability engineering, featuring industry trends and best practices.
How Cleric uses Tailscale to securely automate software operations
Cleric announced deep integration with Tailscale to securely access private resources for AI SRE automation, enhancing security and network flexibility.
Stop Reviewing Agent Output. Start Reviewing Agent Decisions.
Cleric argues that AI agent governance should focus on reviewing decision-making processes rather than just outputs, influencing safer automation practices.
Why Your AI SRE Needs Memory
Cleric explains how operational memory differentiates AI SREs by capturing engineering judgment across incidents, reinforcing its compounding memory feature.
Hooks Won't Secure Your AI Agent
Cleric discusses the need for strict network controls over hooks for AI agent security, supporting its read-only and VPC architecture.
Viability Score
How likely is Cleric to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Automated alert investigation and root cause analysis
- Fix recommendation and verification against live environment
- Stack mapping (services, dependencies, ownership) auto-discovered
- Compounding operational memory across incidents
- Custom Agents for specific workloads (e.g., release-readiness, flaky-test triage)
- Scheduled Monitors for recurring anomaly detection
- Slash Commands for on-demand investigation via Slack
- Read-only by default; SOC 2 Type II compliant
- Auditable investigation logs with confidence scoring
- Integrates with Slack, Datadog, Cloudflare, Tailscale, PagerDuty, GitHub, Prometheus, Linear
- Supports Kubernetes, AWS, Azure, GCP
- Credit-based pricing for Issues and Custom Work (1 credit/min)
- 14-day free trial on your own production environment
- SSO via Google Workspace (Team) and OIDC/SAML (Enterprise)
- Tailscale integration for secure private resource access
About Cleric
Cleric is an AI Site Reliability Engineer for engineering teams managing Kubernetes, microservices, and cloud infrastructure. It automates alert investigation, root cause analysis, on-call triage, and fix recommendations, cutting time to root cause to five minutes with 92% actionable findings across 200,000+ production-grade investigations. Cleric automatically maps your stack—services, dependencies, and ownership—verifies fixes against live environments, and compounds institutional knowledge over time so that context persists even when engineers leave. It is read-only by default, SOC 2 Type II compliant, and integrates with Slack, Datadog, Cloudflare, and Tailscale. Three purpose-built systems—stack knowledge, verification, and compounding memory—drive investigations. Cleric uses Tailscale to securely access private resources for automation. Pricing is issue-based: $2,000/month for 1,000 credits (up to 100 Issues) with custom enterprise pricing. The product recently published a white paper on the state of AI SRE and emphasized decision-review governance for agent safety.
Behind the Verdict
Cleric delivers on the promise of an AI SRE that learns. The three-system architecture—stack knowledge, verification, and compounding memory—turns every incident into a reusable artifact. For teams running Kubernetes at scale, this is a genuine productivity unlock. We'd reach for this when on-call fatigue is high and incident response is a bottleneck. The integration with Tailscale for secure access to private resources is thoughtful and addresses a real security concern. However, the pricing model is a double-edged sword: you pay per resolved Issue (10 credits each), which aligns cost with value but penalizes teams with low alert volume. A smaller shop may burn credits on investigation overhead, while high-volume teams will see clear ROI. Alternatives like PagerDuty SRE Agent offer different pricing models but lack Cleric's institutional memory. Setup is straightforward but assumes decent Kubernetes and observability instrumentation. Overall, a sharp tool for the right environment, but not for everyone.
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Real-world workflow fit
Concrete scenarios for the personas Cleric actually fits — and what changes day-one when you adopt it.
A PagerDuty alert fires for high error rate in the payments service. Cleric automatically investigates, queries logs and metrics, correlates with a recent deploy, and posts a root cause analysis in Slack with a proposed fix.
Outcome: SRE acknowledges, reviews the fix proposal, and approves a rollback. MTTR drops from 45 minutes to 5 minutes.
A critical incident occurs in the middle of the night. Cleric investigates and resolves the issue by rolling back a bad change, logging all steps. The next morning, the leader reviews the audit log and adds a memory entry to prevent recurrence.
Outcome: Institutional knowledge is preserved; the leader saves 2 hours of manual incident reconstruction.
Configure a Scheduled Monitor to check for slow database queries every hour. Cleric scans recent query performance and posts a summary to Slack. It flags a new slow query pattern and opens a Linear ticket.
Outcome: Proactive detection of performance regressions before they cause user-facing incidents.
Use Cases
- Investigate a PagerDuty alert autonomously and deliver a root cause analysis within minutes
- On-demand troubleshoot a production slowdown by querying logs and metrics via Slack
- Reduce MTTR by automatically correlating deployment changes with new errors
- Coach junior engineers by letting them review Cleric's step-by-step reasoning and evidence
- Build an institutional knowledge base of past incidents and resolutions
- Verify that a fix has resolved the issue without introducing regressions
- Automate release-readiness checks with custom agents
- Run scheduled monitors for slow queries or anomaly detection
Models Under the Hood
as of 2026-07-14
Limitations
- Cleric requires read access to your observability stack and Kubernetes; without these integrations, its investigative capabilities are severely limited.
- The Team plan requires an annual contract, and unused credits expire after one month (rolling over up to 2x monthly allocation).
- Custom Work billing per minute can add up if not monitored.
as of 2026-07-02
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Cleric tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Team
$2,000/month billed annually ($24,000/year)
Ideal for
SRE teams of 1-2 with heavy on-call responsibilities and 50-100 Issues per month across Kubernetes and microservices.
What this tier adds
Entry point with 1,000 credits/month, includes full product suite but requires annual contract, Google Workspace SSO only.
Enterprise
Custom (billed annually)
Ideal for
Organizations rolling out Cleric across many teams with custom credit needs and VPC connectivity requirements.
What this tier adds
Adds VPC private resource access, audit logs, OIDC/SAML, premium support, and custom negotiating terms.
Where the pricing makes sense
The company stage and team size where Cleric's pricing actually pencils out — and where peers do it cheaper.
Cleric's per-Issue pricing ($20/Issue) fits teams with consistent alert loads—50+ issues/month—who can justify a $2K/month annual contract. For lower volumes, lighter tools like PagerDuty SRE Agent (tiered per alert) or Bits AI (per-seat pricing) may be cheaper. At high volume, Cleric's flat-rate Team plan becomes more cost-effective than per-seat alternatives.
Setup time & first value
How long it actually takes to get something useful out of Cleric — broken out by persona, not the marketing-page minute.
SRE with admin access: 1-2 hours to connect Datadog, PagerDuty, GitHub, and Slack. The 14-day free trial on your own production lets you evaluate within a week. Platform engineer adding custom agents: a few hours more. Full rollout with SSO: 1-2 days for enterprise.
Switching to or from Cleric
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From PagerDuty SRE Agent: replicate alert routing rules and migrate custom agent definitions; Cleric provides Slack-native slash commands and Scheduled Monitors.
- →From Bits AI: set up Cleric's knowledge graph and memory by importing past incident post-mortems via the documentation hub.
- ↗To PagerDuty SRE Agent: export Cleric's investigation logs and memory entries for post-mortem continuity.
- ↗To Grafana Assistant: adjust to Grafana's different observability scope and lack of compounding memory.
Integrations
Resources & Guides
Official links
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