10 Best Cleric Alternatives in 2026

Cleric is a self-learning AI SRE agent that investigates production issues through hypothesis-driven reasoning. Named a Gartner Cool Vendor in AI for SRE and Observability 2025, it maps your architecture in real-time, tests multiple hypotheses against logs, metrics, and infrastructure state, and delivers diagnoses with confidence scores directly in Slack. It has completed over 200,000 production-grade investigations with a 92% actionable findings rate and averages 5 minutes to root cause. Cleric raised $9.8M in seed funding from Vertex Ventures US and Zetta Venture Partners.

But Cleric has specific limitations. It operates in read-only mode and does not execute fixes, generate pull requests, or run remediation scripts. It depends entirely on external observability tools for data (Datadog, Prometheus, Grafana, CloudWatch) and does not provide its own log management, metrics, or tracing. It has no incident management, on-call, or status pages. And its pricing is not publicly listed.

This guide compares the 10 best Cleric alternatives for teams that need remediation beyond diagnosis, built-in observability, incident lifecycle management, or more transparent pricing.

Why do teams look for Cleric alternatives?

Cleric's hypothesis-driven investigation and self-learning capability are genuinely innovative. But teams look elsewhere for practical reasons:

Diagnosis only, no remediation. Cleric investigates and explains what went wrong, but it does not generate pull requests, create fix scripts, execute kubectl commands, or take any write action. Teams that want an AI agent to go from diagnosis to fix need a tool that does more.

No built-in observability. Cleric reads from Datadog, Prometheus, CloudWatch, Grafana, Sentry, and OpenSearch through integrations. It does not collect, store, or query telemetry independently. If your integration coverage has gaps, Cleric's investigations inherit those blind spots.

No incident management. Cleric delivers diagnoses in Slack but does not provide on-call scheduling, escalation workflows, incident timelines, status pages, or post-mortem generation. You need separate tools for the full incident lifecycle.

Integration-dependent setup. Cleric requires connecting your observability stack, Kubernetes APIs, cloud provider APIs, and knowledge sources (Confluence, Notion, Drive). Teams with simpler stacks or those wanting faster time-to-value may prefer tools that need less wiring.

Pricing is not public. Cleric offers a free start option, but scaling plans require talking to their team. There is no transparent self-serve pricing page.

Seed-stage company. At $9.8M in total funding, Cleric is well-positioned for a startup but smaller than competitors with $150M+ (Resolve AI) or public company backing (Datadog, LogicMonitor).

How do Cleric alternatives compare?

1. Better Stack

Better Stack fills every gap Cleric leaves open. Cleric diagnoses issues using data from your existing tools but cannot fix them, does not store telemetry, and has no incident management. Better Stack collects the telemetry, investigates with AI, generates fixes, and manages the incident lifecycle in a single product that requires no external dependencies.

What makes Better Stack the strongest Cleric alternative?

Cleric reads from Datadog, Prometheus, and Grafana through integrations. Better Stack replaces those tools entirely. It collects logs, metrics, and traces through eBPF-based auto-instrumentation and OpenTelemetry, then uses that native data to power its AI SRE. There is no integration gap between where your data lives and where the AI investigates.

Where Cleric stops at diagnosis, Better Stack keeps going. When the AI SRE identifies a root cause, it can open a pull request in GitHub to fix the error, draft a post-mortem from the incident timeline, create a Linear ticket for follow-up work, or answer your follow-up questions with embedded charts. Cleric delivers a Slack message explaining what happened. Better Stack delivers that explanation plus the tools to actually resolve and document the incident.

Better Stack also includes on-call scheduling, escalation routing, and status pages that Cleric depends on PagerDuty or other tools for. The entire workflow from alert to investigation to resolution to retrospective lives in one place.

The AI shows every query it runs during investigation so you can verify the reasoning, similar to Cleric's hypothesis tree transparency. It operates across Slack, Microsoft Teams, and Claude Code via MCP, and never takes action without your approval.

🌟 Key features

• AI investigation using natively collected eBPF service maps, OpenTelemetry traces, logs, metrics, and error data
• Service map visualization showing how errors propagate between services during active incidents
• Transparent investigation with every query visible and verifiable
• Root cause documents with evidence chains, log citations, resolution steps, and long-term recommendations
• Pull request generation for new errors detected in GitHub
• Plain-English querying with inline chart responses
• Linear ticket creation, AI-drafted post-mortems, and automated log/trace analysis
• MCP server for Claude Desktop and Claude Code workflows
• On-call rotation, incident timelines, escalation routing, and hosted status pages
• eBPF auto-instrumentation that requires no code changes or agent configuration

➕ Pros

• Collects its own telemetry natively, eliminating Cleric's dependency on external observability tools
• Generates PRs and remediation artifacts that Cleric's read-only model cannot produce
• Includes on-call, status pages, and incident management that Cleric leaves to third-party tools
• Query transparency mirrors Cleric's hypothesis tree approach
• Available on Slack, Microsoft Teams, and web (not Slack-only)
• Transparent pricing at $29/responder/month with a free tier
• 60-day money-back guarantee to evaluate risk-free
• SOC 2 Type 2, GDPR, ISO 27001 certified
• Running in 5 minutes, no multi-tool integration required

➖ Cons

• Does not use Cleric's hypothesis-tree visualization model for displaying investigation reasoning

💲 Pricing

Transparent and predictable. Free tier covers 10 monitors, 3 GB logs (3-day retention), and 2B metrics (30-day retention). Paid plans start at $29/responder/month with the full platform included. Enterprise options available. 60-day money-back guarantee on every paid plan.

2. Resolve AI

Resolve AI is a multi-agent AI SRE founded by OpenTelemetry co-creators Spiros Xanthos and Mayank Agarwal. It raised $125M at a $1B valuation from Lightspeed Venture Partners in February 2026, with total funding past $150M. Customers include Coinbase, DoorDash, MongoDB, Salesforce, and Zscaler.

How does Resolve AI compare to Cleric?

Both investigate incidents autonomously. The fundamental difference is what happens after diagnosis. Cleric operates in read-only mode and delivers explanations. Resolve AI generates PRs, kubectl commands, code fixes, and scripts to actually remediate the issue. For teams that want the AI to close the loop from detection to fix, Resolve AI goes significantly further.

Resolve AI's multi-agent system also pursues multiple hypotheses in parallel, similar to Cleric's approach but at a larger scale with specialized agents for different investigation domains. Coinbase reports 72% faster critical incident investigation. DoorDash reports 87% faster investigations.

🌟 Key features

• Multi-agent parallel hypothesis testing across code, infrastructure, and telemetry
• 100% of alerts investigated in under 5 minutes
• Generates remediation PRs, kubectl commands, code fixes, and scripts
• Auto-generates post-mortems and updates ticketing systems
• Learns from historical patterns and runbook knowledge
• SOC 2 Type II, GDPR, HIPAA compliant

➕ Pros

• Generates and executes remediation that Cleric's read-only mode cannot
• Enterprise-proven with Coinbase, DoorDash, Salesforce, MongoDB, Zscaler
• $1B valuation and $150M+ funding versus Cleric's $9.8M seed
• Full compliance certifications already in place
• Multi-agent parallel investigation at enterprise scale

➖ Cons

• Pricing not public, reportedly $1M+/year for large deployments
• Standalone agent with no built-in observability or incident management
• Less transparent reasoning than Cleric's hypothesis tree model

💲 Pricing

Free trial available. Custom enterprise pricing through sales.

3. Datadog Bits AI SRE

Datadog Bits AI SRE is an autonomous AI SRE with native access to Datadog's full observability dataset. GA since December 2025, tested across 2,000+ customer environments.

Why would a team choose Bits AI SRE over Cleric?

Cleric reads Datadog data through integrations. Bits AI SRE lives inside Datadog with native, unfiltered access to every metric, log, trace, RUM session, database query, and network path. No API limitations, no sampling gaps. For Datadog customers, this means richer investigation context than Cleric can achieve from the outside.

Bits AI also goes beyond diagnosis by suggesting code fixes via the Bits AI Dev Agent, which Cleric's read-only design does not support. It learns from feedback loops and supports a bits.md configuration file for team-specific troubleshooting knowledge.

🌟 Key features

• Native access to Datadog's full telemetry without integration limits
• Parallel root cause exploration at machine scale
• Code fix suggestions via Bits AI Dev Agent
• Feedback loops improving accuracy from responder corrections
• bits.md for team-specific context
• RBAC, HIPAA compliance, enterprise security

➕ Pros

• Native data access is deeper than Cleric can achieve through Datadog integration
• Code fix generation that Cleric's read-only mode does not support
• Tested across 2,000+ environments with 90% faster resolution reported by iFood
• Mature, publicly traded company backing
• HIPAA, RBAC, enterprise controls

➖ Cons

• Per-investigation pricing ($500/20 per month annual) scales with alert volume
• Only valuable inside the Datadog ecosystem
• Increases vendor lock-in
• No hypothesis tree visualization like Cleric

💲 Pricing

$500 per 20 investigations/month (annual). $600 month-to-month. Inconclusive investigations free. 14-day free trial.

4. Deeptrace

Deeptrace is an AI-powered production debugging platform with a living knowledge graph of your system architecture.

How does Deeptrace's knowledge graph compare to Cleric's live system mapping?

Both Cleric and Deeptrace build dynamic models of your infrastructure. Cleric calls it live system mapping. Deeptrace calls it a living knowledge graph. The core difference is what they do with it. Cleric uses its map to inform diagnosis. Deeptrace uses its graph to inform diagnosis and generate PRs, update runbooks, and create Linear tickets for remediation.

Deeptrace delivers evidence-backed root causes with citations in 2-3 minutes, ranks alerts by business impact, and groups related alerts. It integrates with Datadog, Grafana, New Relic, PagerDuty, AWS CloudWatch, Sentry, Snowflake, and PostHog. Gary Tan, president of Y Combinator, has endorsed the platform.

🌟 Key features

• Living knowledge graph updating in real-time
• Evidence-backed root cause analysis with citations in 2-3 minutes
• Alert intelligence with business impact ranking
• PR generation, runbook updates, and Linear ticket creation
• 20+ integrations including Datadog, Grafana, New Relic, PagerDuty, Sentry

➕ Pros

• Generates PRs and remediation artifacts that Cleric cannot
• Knowledge graph approach is conceptually similar to Cleric's mapping but with actionable output
• 70%+ root cause accuracy with evidence citations
• Complements existing tools without platform replacement
• Under 1 hour setup

➖ Cons

• Startup plan caps at 1,000 alerts/month
• Early-stage ($5M seed), similar maturity to Cleric ($9.8M seed)
• No incident management or on-call
• 20+ integrations is modest

💲 Pricing

Startup: 2-week trial, 1,000 alerts/month. Enterprise: 4-week trial, custom capacity, flexible deployment.

5. incident.io AI SRE

incident.io AI SRE is an AI investigation agent embedded in a mature incident management platform.

What does incident.io provide that Cleric does not?

Cleric diagnoses issues. incident.io diagnoses, coordinates, and manages the entire incident. It provides on-call scheduling, escalation routing, status pages, and AI-native post-mortems that Cleric leaves to separate tools. It also drafts code fixes and opens PRs from Slack, going beyond Cleric's read-only diagnosis.

incident.io's AI SRE leverages years of historical incident data for pattern-matching, identifying the exact PR behind a failure within seconds.

🌟 Key features

• Correlates telemetry, code changes, and historical incident patterns
• Pinpoints the specific PR behind failures within seconds
• Drafts code fixes and opens PRs from Slack
• AI-native post-mortems with timeline and contributing factors
• Full on-call, status pages, and escalation workflows

➕ Pros

• Full incident lifecycle that Cleric does not provide
• Generates code fixes and PRs beyond Cleric's read-only diagnosis
• 5x faster resolution and 80% automation rates reported
• Established company with mature platform

➖ Cons

• Depends on external tools for observability data, same limitation as Cleric
• AI SRE pricing requires sales engagement
• Slack-focused workflow
• No self-learning or hypothesis tree like Cleric

💲 Pricing

Platform approximately $31-45/user/month. AI SRE pricing requires demo.

6. Rootly AI SRE

Rootly AI SRE is an AI investigation layer on a mature incident platform used by NVIDIA, LinkedIn, Figma, Canva, and Replit since 2021.

How does Rootly compare to Cleric?

Both emphasize transparent reasoning. Cleric shows hypothesis trees. Rootly shows full chain-of-thought reasoning at every investigation step. The key difference is that Rootly also provides incident management, on-call, retrospectives, and status pages that Cleric lacks.

Rootly offers bring-your-own AI API key, MCP server for IDE investigation (Cursor, Windsurf, Claude), and AI Labs for open reliability research. However, like Cleric, Rootly does not generate PRs or execute remediation.

🌟 Key features

• Transparent chain of thought for every investigation
• MCP server for IDE integration
• AI-powered post-mortems and retrospective diagrams
• Full on-call, incident response, and status pages
• Bring-your-own AI API key, PII scrubbing

➕ Pros

• Incident lifecycle management Cleric does not have
• Chain-of-thought transparency comparable to Cleric's hypothesis trees
• Enterprise customers: NVIDIA, LinkedIn, Figma, Canva
• MCP for IDE-based investigation
• 14-day free trial, starts at $20/user/month

➖ Cons

• Does not generate PRs or execute fixes, same limitation as Cleric
• Depends on external observability tools
• No self-learning architecture like Cleric's continuous improvement model

💲 Pricing

14-day free trial. Starts at $20/user/month.

7. IncidentFox

IncidentFox is a YC W26-backed AI incident investigator with 300+ built-in tools and zero-setup onboarding.

What does IncidentFox offer that Cleric does not?

IncidentFox delivers executable fix scripts with one-click approval, directly addressing Cleric's read-only limitation. It also auto-builds integrations by analyzing your codebase and Slack history, requiring less manual setup than Cleric's integration configuration. The open core Apache 2.0 license provides self-hosting flexibility.

🌟 Key features

• 300+ built-in tools with auto-generated custom integrations
• Root cause analysis with executable fix scripts
• One-click remediation with human-in-the-loop approval
• Sandboxed execution with credential proxy
• Open core (Apache 2.0) with self-host option

➕ Pros

• Executable fix scripts go beyond Cleric's read-only diagnosis
• Zero-setup versus Cleric's multi-tool integration requirements
• Open core license for self-hosting and transparency
• 300+ built-in tools for broad connectivity

➖ Cons

• Very early-stage (YC W26, two-person team)
• SOC 2 Type 2 in progress
• Slack-only, no web UI
• No self-learning architecture like Cleric

💲 Pricing

Free to start. Enterprise pricing requires demo. Self-hosting under Apache 2.0.

8. Dash0 Agent0

Dash0 Agent0 is six specialized AI agents inside an OpenTelemetry-native observability platform. Dash0 acquired Lumigo to expand serverless coverage.

When does Dash0 make sense over Cleric?

Dash0 provides a full observability platform that Cleric depends on external tools for. The six agents cover incident triage (The Seeker), PromQL queries (The Oracle), OTel onboarding (The Pathfinder), trace analysis (The Threadweaver), dashboards (The Artist), and frontend performance (The Lookout). Cleric focuses solely on investigation.

Dash0 is OpenTelemetry-native with portable instrumentation.

🌟 Key features

• Six specialized agents covering investigation, queries, onboarding, traces, dashboards, and frontend
• OpenTelemetry-native, zero vendor lock-in
• Built-in observability platform

➕ Pros

• Built-in observability Cleric depends on external tools for
• Broader agent capabilities beyond investigation
• OTel-native portability

➖ Cons

• Still in Beta
• No fix generation or remediation
• No incident management
• No self-learning architecture

💲 Pricing

Free trial. Starts at approximately $50/month.

9. Sentry Seer

Sentry Seer is an AI debugging agent for application errors using stack traces, session replays, traces, and profiles.

When is Sentry Seer a better fit than Cleric?

Seer is the right choice when your issues are application code bugs rather than infrastructure failures. Seer analyzes stack traces and replays with deeper code-level context than Cleric's infrastructure-oriented investigation. It also generates PRs and reviews GitHub PRs proactively against production error patterns, catching bugs before they ship. Cleric does neither.

🌟 Key features

• Code-level root cause using stack traces, replays, traces, and profiles
• Proactive PR reviews against production error patterns
• MCP integration for IDE debugging
• PR and patch generation

➕ Pros

• Deeper application debugging than Cleric's infrastructure focus
• Proactive PR reviews catch bugs pre-production
• Generates PRs that Cleric cannot

➖ Cons

• Not designed for infrastructure incidents
• Requires paid Sentry plan ($40/active contributor)
• No self-learning or system mapping

💲 Pricing

$40 per active contributor per month on paid Sentry plans.

10. LogicMonitor Edwin AI

LogicMonitor Edwin AI is an enterprise AIOps platform with 3,000+ integrations and bi-directional ServiceNow sync.

When does Edwin AI make sense over Cleric?

Edwin AI targets enterprise IT managing hybrid infrastructure with legacy systems and multi-cloud. Cleric targets cloud-native engineering teams. If your environment includes on-premises infrastructure and ServiceNow-driven ITSM workflows, Edwin AI's 3,000+ integrations and self-healing playbook execution cover ground Cleric was not designed for. Edwin AI also takes remediation actions, which Cleric's read-only design does not allow.

🌟 Key features

• Full incident lifecycle with self-healing automation
• 3,000+ integrations, bi-directional ServiceNow sync
• Playbook generation and autonomous execution
• Predictive outage prevention

➕ Pros

• Self-healing automation that Cleric's read-only model cannot match
• 3,000+ integrations for enterprise hybrid environments
• Proven: 67% ITSM incident reduction, 88% noise reduction

➖ Cons

• Overkill for cloud-native teams
• Enterprise pricing through sales only
• Traditional ITOps focus, not SRE

💲 Pricing

Enterprise pricing. Requires demo.

Final thoughts

Cleric brings genuine innovation with its self-learning architecture, hypothesis-driven investigation, and live system mapping. But its read-only design, dependency on external observability, and lack of incident management mean teams still need multiple tools around it.

If you want one product that collects telemetry, investigates with AI, generates fixes, and manages the incident lifecycle, Better Stack covers the full picture. It replaces the observability tools Cleric reads from, adds the PR generation and remediation Cleric cannot do, and includes the on-call and status page workflows Cleric leaves to PagerDuty. Transparent pricing, 5-minute setup, no assembly required.

The core question: do you want a diagnosis agent that explains what broke, or a platform that explains it and helps you fix it? For most, Better Stack delivers both.

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