AI SRE Buyer's Guide: 17 Must Have Features (2025)
How to evaluate AI SRE platforms from POC to production
Last quarter, I watched three different companies evaluate AI SRE platforms. Two chose poorly and ended up with expensive shelfware. One nailed it and reduced MTTR by 85% in 90 days.
The difference? The successful team had a clear evaluation framework. They knew which features were "nice-to-have" versus "make-or-break." They asked vendors the hard questions about autonomy, trust, and production readiness.
This guide is that framework, distilled. Whether you're a CTO signing off on budget or an SRE lead running the POC, these 17 features separate real AI SRE platforms from glorified dashboards with ChatGPT plugins.
Why Most AI SRE Evaluations Fail
- 1.Demo-Driven Decisions: Vendor shows perfect demo with fake data → you buy → it fails on real production chaos
- 2.Feature Checklist Fallacy: "Does it have anomaly detection? ✓" (but doesn't check if it's accurate or fast)
- 3.No Production Test: POC runs for 2 weeks in staging → misses edge cases that break in prod
- 4.Ignoring Team Readiness: Platform is great, but your team doesn't trust AI → adoption fails
The 17 Must Have Features (Grouped by Category)
Category 1: Context & Intelligence
These features determine if the AI "gets" your infrastructure or just pattern-matches error messages.
1. Topology Aware RCA
The AI must understand your service dependency graph & not just read logs in isolation.
How to Test:
- Break a downstream service (e.g., Redis)
- Does the AI trace failures upstream to affected services?
- Does it understand cascading failures ("payment-service failed because auth-service failed because Redis timed out")?
2. Deployment Context Integration
When an incident happens, the AI should know: "What changed in the last 30 minutes?" (Git commits, K8s deploys, feature flags, config changes)
How to Test:
- Deploy a bad canary
- Does the AI flag the deployment as suspicious?
- Does it link to the Git commit / JIRA ticket?
3. Historical Incident Memory
The AI should learn from past incidents: "We've seen this OOM pattern before last time we fixed it by increasing memory limits."
How to Test:
- Trigger a repeat incident (e.g., disk full on same service)
- Does the AI say "This happened on [DATE], fixed by [SOLUTION]"?
- Does it auto-suggest the previous fix?
4. Multi-Signal Correlation
The AI must correlate logs + metrics + traces + events (not just read logs).
How to Test:
- Ask: "Why is payment-service slow?"
- Does it check: (1) app logs for errors, (2) CPU/memory metrics, (3) trace spans for slow downstream calls?
- Or does it just grep logs?
Category 2: Autonomy & Remediation
Can the AI actually fix problems, or just tell you what's broken?
5. Verified Runbook Execution
The AI should auto execute verified runbooks (restart pod, clear cache, scale replicas) for known good patterns.
How to Test:
- Trigger a known issue (e.g., OOM kill)
- Does the AI auto-restart the pod without human approval?
- Does it post a full audit log of actions taken?
6. Auto-Rollback with Safety Checks
When a deployment causes errors, the AI should rollback automatically (with guardrails: check DB migrations, API contracts, etc.)
How to Test:
- Deploy a version that increases error rate by 10x
- Does the AI detect + rollback within 5 minutes?
- Does it check for DB migrations first (safety)?
7. Confidence Based Escalation
The AI should only act autonomously if confidence >90%. Otherwise, escalate to humans with "here's what I'd do, approve?"
How to Test:
- Trigger a novel incident (not seen before)
- Does the AI say "Confidence: 60% escalating to human"?
- Or does it blindly execute an unproven fix?
8. Dry Run Mode (Test Before Execute)
Before auto-remediation goes live, you need a "shadow mode" where the AI shows what it *would* do, but doesn't execute.
How to Test:
- Enable dry run mode
- Trigger incident → does AI log "Would have restarted pod" without actually restarting?
- Can you review dry run logs to build trust?
🔒 Category 3: Trust & Transparency
If your team doesn't trust the AI, they won't use it. These features build trust.
9. Explainability (Why Did You Do That?)
For every action, the AI must explain: "I restarted the pod because: (1) OOM kill, (2) CPU at 100% for 5 min, (3) This worked in 3 past incidents."
How to Test:
- After an auto-remediation, ask "Why did you do that?"
- Does it provide evidence (logs, metrics, historical data)?
- Or just "LLM said so"?
10. Full Audit Trail (Git-Like History)
Every AI action must be logged with: Who/What/When/Why, revertible (like git revert), and searchable.
How to Test:
- Search audit log for "all restarts in last 7 days"
- Can you see: command executed, reason, confidence, outcome?
- Can you revert an action if it made things worse?
11. Human Override (Break-Glass)
At any moment, a human must be able to say "Stop. I'll take over." No delays, no "are you sure?" instant override.
How to Test:
- While AI is remediating, hit the override button
- Does it stop immediately?
- Does it log "Human override at [TIMESTAMP] by [USER]"?
12. Role Based Access Control (RBAC)
Not all SREs should have "approve auto-remediation for prod DB" permissions. Granular RBAC is critical.
How to Test:
- Can you configure: Junior SRE = read-only, Senior SRE = approve low-risk, Staff SRE = approve high-risk?
- Does it integrate with your SSO (Okta, AD)?
Category 4: Production Readiness
Can this platform handle your production scale and complexity?
13. Performance at Scale
Can the AI handle your log volume? (e.g., 10M logs/minute, 100K metrics/second, 500-service topology)
How to Test:
- Share your actual log/metric volumes with vendor
- Ask: "What's your RCA latency at our scale?" (should be <30 seconds)
- Do they have customers at similar scale? (get references)
14. Multi-Cloud & Hybrid Support
If you're running AWS + GCP + on-prem, the AI needs unified visibility (not separate dashboards per cloud).
How to Test:
- Connect one service in AWS, one in GCP
- Does the AI see cross-cloud dependencies?
- Can it trace a request from AWS → GCP → on-prem?
15. API-First Architecture
You should be able to trigger RCA, get incident status, or approve remediations via API (for custom workflows, Slack bots, etc.)
How to Test:
- Ask for API docs
- Try: POST /incidents/analyze, GET /incidents/:id, POST /incidents/:id/approve
- Is the API rate-limited? (should be high limit for internal use)
16. SLA & Reliability Guarantees
What happens if the AI SRE platform itself goes down? Do you have a fallback?
What to Ask:
- What's your uptime SLA? (should be 99.9%+)
- What's your RCA latency SLA? (should be <1 minute p95)
- If your platform is down, can we still get alerts? (failover mode)
Category 5: Team & Workflow Fit
The best platform in the world fails if it doesn't fit your team's workflow.
17. Slack / Teams / PagerDuty Integration
Your SREs live in Slack/Teams. The AI must meet them there (not force them into a new dashboard).
How to Test:
- Connect Slack
- Trigger incident → Does AI post RCA to Slack?
- Can you approve/reject remediations directly in Slack? (no context-switching)
Red Flags: When to Walk Away
These are deal-breakers that indicate a vendor isn't ready for production.
🚩 "We're LLM-first"
Translation: "We wrapped ChatGPT in a dashboard." No ML models, no anomaly detection, just a chatbot.
🚩 "Just send us your logs"
No topology, no deployment context, no metrics. They're just grepping logs with GPT-4.
🚩 "100% autonomous" (no human override)
Dangerous. You should always be able to stop the AI. If they won't add override, walk away.
🚩 No customer references at your scale
"You'd be our first customer with 100+ services." That's not a POC, that's beta testing.
🚩 "Custom implementation required"
If it takes 6 months of professional services to get basic RCA working, it's not a platform it's a consulting gig.
🚩 "We don't share our accuracy metrics"
Legitimate vendors publish RCA accuracy, false positive rates, and MTTR improvements. No metrics = no confidence.
Pricing Models Explained (And Which to Choose)
| Model | How It Works | Best For | Watch Out For |
|---|---|---|---|
| Per-Service | $X per monitored service/month | Microservices (predictable cost) | Cost explosion if you have 500+ services |
| Usage-Based | $X per GB logs, $Y per incident analyzed | Variable traffic (pay for what you use) | Surprise bills during incident storms |
| Flat Rate | $X/month for unlimited services | Large orgs (budget predictability) | May be expensive if you have few services |
| Seat-Based | $X per SRE user/month | Small SRE teams (simple) | Penalizes collaboration (limits user invites) |
Pro tip: Negotiate a "fair use" cap on usage-based models. Example: "$2k/month for up to 10TB logs, then $X per TB overage." This protects you from runaway costs during major incidents (when you need the AI most).
Key Takeaways
- →17 must-have features separate real AI SRE platforms from chatbot wrappers
- →Topology context and deployment awareness are non-negotiable for accurate RCA
- →Trust features (explainability, audit trail, human override) determine adoption success
- →Run a 30-day POC with the checklist don't buy based on demos alone
- →Watch for red flags like "LLM-first" (no ML), no customer references at scale, or no human override
See How AutonomOps Checks All 17 Boxes
AutonomOps was built with these exact features in mind: Topology-Aware RCA, verified runbook execution, full explainability, and more. Start a free 30-day trial and test against this checklist yourself.
About Shafi Khan
Shafi Khan is the founder of AutonomOps AI. He's evaluated dozens of AI SRE platforms (and built one from scratch). This buyer's guide is based on real POCs with fintech, SaaS, and e-commerce companies.
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