Better Stack AI SRE vs Cleric

Cleric is one of the more thoughtfully designed AI SREs in the space. Its core idea is simple but powerful: the system should learn from every incident, so investigations that take 40 minutes today take minutes in the future. That focus on compounding intelligence makes it a strong specialist for teams with established workflows.

Better Stack takes a broader approach. It combines AI SRE with eBPF-based observability, on-call scheduling, incident management, and status pages in one platform.

Both are real products solving similar problems, but for different team setups.

The real question is whether you want to enhance your existing stack or replace parts of it.

Cleric is the stronger fit if you already have a mature observability stack and want a focused AI SRE that improves over time.

Better Stack is the more complete and accessible option for most teams, offering AI SRE, observability, and the full incident workflow in one product, with predictable pricing and no need for additional tools.

This comparison breaks down where each approach fits best.

Quick comparison at a glance

Two ways to build an AI SRE

The structural difference between these products is the easier half of this decision. Knowing what each company is actually building tells you which one fits your team.

Better Stack AI SRE

Better Stack AI SRE is a Slack-native AI agent built into Better Stack's full observability and incident management platform. The agent investigates incidents using an eBPF service map, OpenTelemetry traces, logs, metrics, errors, and web events ingested into Better Stack. It also plugs into Datadog, Grafana, Sentry, Linear, and Notion when data lives elsewhere.

The bet: bundle the AI SRE with the data and the full incident workflow. One vendor, one bill, one UI for everything between "alert fired" and "post-mortem published."

Cleric

Cleric is a focused AI SRE overlay that sits on top of your existing observability, CI/CD, and incident tooling. Founded by Willem Pienaar (CTO) and Shahram Anver (CEO), both former platform engineers at Gojek who supported hundreds of engineers across dozens of services. The product was introduced in March 2024 and formally launched in December 2025 with the headline framing "the first self-learning AI SRE."

The architecture: three purpose-built systems. Maps your world, services, dependencies, and ownership mapped automatically so the AI knows where to look when something breaks. Compounds over time, every fix teaches Cleric something new about your environment, and patterns accumulate. Tests every theory, correlates signals, runs hypothesis tests in parallel, tracks confidence scores. The pitch is systematic elimination, not guesswork, with read-only access by default and write access opt-in.

Cleric has run in production at BlaBlaCar since early 2025. Public homepage stats: 5 minute time to root cause, 92% actionable findings, 200,000+ production-grade investigations. Gartner named Cleric a Cool Vendor in AI for SRE and Observability 2025.

The short version: Better Stack bundles the AI agent with the data and the incident workflow. Cleric is a focused specialist agent that overlays your existing stack and gets sharper over time. Which fits depends on whether you're building a stack or operating a complex existing one.

The self-learning angle

This is the feature Cleric is built around, and it's worth giving full credit before moving on.

Cleric: institutional knowledge that compounds

Cleric's positioning is that an AI SRE should learn from every incident, alert, and human decision, not just react to individual alerts. The product team came from Gojek where they watched the same pattern play out daily: an engineer deep in a complex refactor gets interrupted by an alert, spends 40 minutes investigating, only to discover it's the same leaked connection issue from three weeks ago.

Cleric's self-learning architecture turns those resolutions into institutional knowledge. The CTO's framing: investigations that took 40 minutes today take 3 minutes after Cleric has learned the patterns. Engineers leave, but context doesn't. When a new engineer joins, the AI already knows how this specific cluster fails, which signals matter for this particular service, and which past incidents look similar.

For teams operating complex production environments where institutional knowledge is genuinely a constraint (high engineer turnover, many microservices, complex dependency graphs), this is meaningful. It's also why Gartner singled out Cleric in the 2025 Cool Vendors report. Adaptive learning is what Gartner specifically called out as the next step in AI for SRE. Does your team lose meaningful productivity to "we've solved this exact issue before but the person who fixed it last time isn't here anymore"?

Better Stack: feedback-driven without explicit memory framing

Better Stack's AI SRE doesn't market continuous learning the way Cleric does. The agent improves through feedback (you can correct it during investigations), and the underlying platform's eBPF service map updates as your services change. But there's no explicit "operational memory" framing of "investigations get faster the longer we run" the way Cleric pitches it.

Is that a meaningful gap? Depends on your environment. If you have a stable production setup where the same five or ten classes of incident keep recurring, Cleric's compounding intelligence has real ROI. If your incident patterns are highly varied or your infrastructure changes constantly, the marginal gain from a learned pattern library is smaller. How repetitive are the incidents your team actually fights?

Investigation depth and remediation

Both AIs do real autonomous investigation. The product surfaces and remediation flows differ.

Cleric

When an incident occurs, Cleric autonomously investigates and delivers findings directly in Slack with links to relevant evidence. For complex cases, engineers can guide its reasoning through conversation or examine detailed diagnostics through a dedicated web interface. The dual surface, Slack for fast cases, web for deep cases, is a thoughtful product decision. Slack is right for "here's the answer, do you want to act," but a web app is better for "show me the full trace of every query you ran."

Confidence scores accompany every conclusion, which lets engineers triage AI output the same way they'd weigh a junior engineer's hypothesis. The reasoning is transparent: every investigation is auditable, every action logged, with read-only access by default. Write access (executing remediations) is explicitly opt-in.

The remediation flow is collaborative rather than autonomous. Cleric's framing on the homepage: "Work with Cleric to ship fixes by asking questions, giving instructions, and collaborating until the issue is resolved." That's deliberately less aggressive than tools that auto-generate PRs and merge them. Cleric is the diagnostician; the engineer drives the fix.

For Kubernetes environments, microservices architectures, and cloud infrastructure (the three "by system context" surfaces on their site), Cleric is purpose-built. It's not trying to cover serverless or hybrid edge cases. The opinionation lets it go deeper.

Better Stack

Better Stack's AI SRE activates during an incident and correlates recent deployments, errors, trace slowdowns, metric trend changes, and logs to build hypotheses. The eBPF service map gives it impact analysis across service boundaries.

Output: root cause analysis document with an evidence timeline, log citations, root cause chain, immediate resolution steps, and long-term recommendations. You can drill into any query the agent ran. The agent sits firmly in "suggest, don't act" territory: forms hypotheses, surfaces evidence, proposes fixes, but you approve every write action. PR generation happens for code-related root causes through GitHub.

Better Stack works across mixed infrastructure (Kubernetes, VMs, hybrid, serverless). Cleric's stronger Kubernetes focus is real, but Better Stack's broader scope removes the "we use K8s but also have legacy VMs" awkwardness. Where Cleric pulls ahead: explicit confidence scores, the dedicated web app for deep investigation, and the self-learning compounding effect over time. Which combination matters more depends on your environment shape. Do most of your incidents resolve faster when an AI shows its certainty alongside its hypothesis, or do your engineers prefer to evaluate the evidence themselves?

Platform scope: AI SRE plus what?

The clearest difference between these products isn't the AI itself. It's what's around the AI.

Cleric: focused AI SRE, no platform

Cleric is squarely in the "AI SRE overlay" category. The product investigates alerts, correlates data, and surfaces root causes in Slack and a dedicated web app. What it doesn't do: own the observability data (you keep using Datadog/Grafana/whatever), manage on-call rotations, publish status pages, or own the incident management workflow.

This is by design. Cleric's "no rip and replace" stance means it slots into existing tooling rather than trying to replace it. Customers don't need to rebuild or adapt their environments to take advantage of Cleric. For BlaBlaCar, that means the existing observability and incident management stack stays intact, and Cleric layers on top.

For teams that already have a mature stack and don't want to disrupt it, this is the right shape of product. For teams trying to consolidate vendors, it's not.

Better Stack: full incident response stack

Better Stack covers significantly more surface area. Logs, metrics, traces, error tracking, RUM, uptime monitoring, AI SRE, on-call scheduling with multi-tier escalation, unlimited phone and SMS alerts, Slack-native incident channels, public and private status pages, AI-generated post-mortems. All native, all in one bill.

For teams that want vendor consolidation, this matters. The full math of "AI SRE + observability + on-call + incident channels + status page + post-mortems" can easily be 4-5 separate vendors. Better Stack collapses that into one product. How many incident response tools does your team currently pay for, and how often do you wish they were the same vendor?

Pricing and access

The two products take very different approaches to publishing pricing.

Better Stack

Flat per responder, all-in-one platform pricing, fully published.

• Free tier: 10 monitors, 3 GB logs for 3 days, 2B metrics for 30 days.
• Paid plans with on-call: Start at $29 per responder per month (annual).
• Enterprise: Custom pricing with a 60-day money-back guarantee.

The unit is responders, the people on-call. You get the AI SRE, MCP server, on-call scheduling, incident management, status pages, post-mortems, logs, metrics, traces, RUM, error tracking, and uptime monitoring for that flat rate. Volume-based observability ingestion is bundled into the same bill.

Cleric

Cleric does not publish pricing on its website. The site has a "Book a demo" CTA and a "Calculate ROI" calculator, but no pay-as-you-go tier or self-service starter plan visible. Pricing requires a sales conversation.

This is consistent with their enterprise positioning, BlaBlaCar is the public reference customer, and Cleric is targeting teams operating at production scale where a sales-led motion makes sense. Early adopters report freeing 20-30% of engineering capacity, which the ROI calculator presumably translates into a per-team price.

The trade-off: for teams evaluating multiple AI SRE vendors, having to book a demo to even get a price quote is friction. For Cleric specifically, it suggests they're optimizing for high-touch customer wins rather than self-service adoption. Is that a problem for your team's procurement process, or are you fine with a sales call?

Compliance, security, and recognition

Both products are enterprise-ready. The security posture is broadly similar; the recognition each has earned is different.

Cleric

SOC 2 Type II compliant with regular manual penetration testing. Read-only access by default. Every action logged. Every investigation auditable. Data encrypted everywhere and explicitly never used for training. Trust Center publicly available.

Public recognition: Gartner named Cleric a Cool Vendor in AI for SRE and Observability 2025 (the report was published October 2, 2025). For Gartner-driven enterprise procurement processes, this is a meaningful signal. Total funding ~$9.8M from Vertex Ventures US and Zetta Venture Partners.

Better Stack

SOC 2 Type 2 attested (NDA), GDPR-compliant, hosted in ISO 27001-certified data centers. SSO via Okta, Azure, Google. RBAC, audit logs, and tool-level allowlist/blocklist controls for the AI agent. Better Stack does not currently have HIPAA certification.

Better Stack hasn't picked up Gartner Cool Vendor recognition for AI SRE, but the platform has 7,000+ teams in production across thousands of organizations. The proof points are different in shape, breadth of adoption versus analyst recognition.

Final thoughts

If your observability stack is already in place and not going anywhere, Cleric fits naturally as a focused upgrade. Its strength is in learning from past incidents and getting better over time, which can make a meaningful difference for teams dealing with recurring issues. For environments that are heavily Kubernetes-based and microservices-driven, that compounding knowledge becomes a real advantage.

Better Stack takes a different path by reducing the need for that stack in the first place. It brings AI SRE, observability, on-call, incident management, status pages, and post-mortems into one platform, with clear pricing and no dependency on multiple vendors. Instead of layering AI on top, it integrates it directly with the data and workflows it operates on.

If your main goal is to get more value out of your current tools, Cleric is a strong option. If your goal is to reduce complexity and manage everything in one place, Better Stack is the more practical choice.

You can explore it here: https://betterstack.com/ai-sre

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