AWS DevOps Guru vs Nuberio Audit: What's the Difference?

Both products live in the CloudWatch ecosystem and both promise less time spent hunting for problems, which is exactly why they get confused with each other. They're not competitors — they answer different questions. AWS DevOps Guru asks "is something behaving abnormally right now?" Nuberio Audit asks "if something breaks, will an alarm actually fire?" This post covers what DevOps Guru actually does, what it costs, where Nuberio Audit fits instead, and when you'd genuinely want both.

• What is AWS DevOps Guru?
• How much does AWS DevOps Guru cost?
• What does Amazon DevOps Guru for RDS specifically do?
• What is Nuberio Audit, and how is it different?
• AWS DevOps Guru vs Nuberio Audit: side by side
• When DevOps Guru is the better fit
• When Nuberio Audit is the better fit
• Can you use both?

What is AWS DevOps Guru?

AWS DevOps Guru is a machine-learning service that automatically analyzes CloudWatch metrics, logs, and events for a given AWS account or resource group and flags abnormal behavior without you writing any detection rules. It builds a baseline of what "normal" looks like for your resources, then surfaces an insight — with context and a remediation recommendation — when something deviates from that baseline. No thresholds to set, no alarms to author.

That's a genuinely different mechanism from a CloudWatch alarm. An alarm fires when a metric crosses a threshold you defined ahead of time — CPUUtilization > 80%, say. DevOps Guru instead learns the normal range for a metric from its own history and flags deviations from that learned pattern, including patterns a static threshold would never catch, like a metric that's technically under 80% but behaving nothing like it usually does.

How much does AWS DevOps Guru cost?

DevOps Guru is consumption-based with no minimum fee, billed on two components: resource analysis and API calls. Resource analysis splits into two price groups — $0.0028 per resource-hour for Lambda functions and S3 buckets (Group A), and $0.0042 per resource-hour for most other resource types including RDS, DynamoDB, EC2 in an Auto Scaling group, and load balancers (Group B). API calls cost $0.000040 each ($0.40 per 10,000 calls). A resource only counts as "active" — and billable — for hours it actually produces metrics, events, or log entries.

AWS's free tier covers 7,200 resource-hours in each price group plus 10,000 API calls per month, for the first 3 months only — after that, every active resource accrues cost continuously, every hour, for as long as DevOps Guru is enabled on it. For a fleet of a few dozen resources this is a small, steady bill; the number to watch is how many resources you enable it on, since cost scales with resource-hours, not with how many insights it actually finds.

What does Amazon DevOps Guru for RDS specifically do?

DevOps Guru for RDS is a database-specific capability that applies the same ML approach to RDS Performance Insights telemetry — DB load, wait events, SQL-level query patterns, and OS metrics for the underlying instance. It supports Aurora and RDS for PostgreSQL (not Aurora Serverless), and it detects things a generic threshold alarm structurally can't: an increasing number of on-disk temporary tables, or Aurora receiving a larger volume of SQL queries that are reading more data than usual — findings that come from a pattern across several signals, not one metric crossing one number.

It also ships Proactive Insights — findings issued before a problem becomes customer-impacting, not just after. That's a real capability gap against a standard CloudWatch alarm, which by definition only fires once a threshold has already been crossed. Billing for DevOps Guru for RDS follows the same Group B resource-hour rate as the rest of RDS ($0.0042/hour per DB instance) — there's no separate RDS-specific price tier.

What is Nuberio Audit, and how is it different?

Nuberio Audit doesn't do anomaly detection at all — it's a free, read-only scan of your existing CloudWatch alarm configuration. It connects via a scoped, read-only IAM role, inventories the resources actually running in your account (RDS instances, ECS services, Lambda functions, EC2 instances, load balancers, and more), and cross-references them against a namespace-specific rule set of the alarms a production resource of that type should have. Then it classifies what it finds: alarms that are missing entirely, alarms that exist but are misconfigured in a way that suppresses real failures (`SUPPRESSED-BAD` — for example an alarm stuck in `INSUFFICIENT_DATA` with no real state transition in over 2 days, usually a renamed or redeployed resource the alarm never followed), and alarms that are just noisy.

For every missing alarm, Nuberio Audit generates a ready-to-run `aws cloudwatch put-metric-alarm` command with the correct namespace, metric, threshold, and dimension already filled in for that specific resource — copy, paste, run. No account is required to run the scan, no credit card, and it takes 3-5 minutes end to end.

# Example of what Nuberio Audit generates for a missing RDS CPU alarm
aws cloudwatch put-metric-alarm --alarm-name "prod-db-cpu-high" \
  --metric-name CPUUtilization --namespace AWS/RDS --statistic Average \
  --period 300 --threshold 80 --comparison-operator GreaterThanThreshold \
  --evaluation-periods 3 --datapoints-to-alarm 2 --treat-missing-data breaching \
  --dimensions Name=DBInstanceIdentifier,Value=prod-db

The scope is deliberately narrower than DevOps Guru's: Nuberio Audit doesn't learn behavioral baselines or flag anomalies within a threshold. It answers one question precisely — does this resource have the alarm coverage a production resource of its type needs — and does it as a one-time or repeatable free check rather than a continuously billed service.

AWS DevOps Guru vs Nuberio Audit: side by side

When DevOps Guru is the better fit

If your team has resources with complex, evolving behavior — an Aurora database under variable query load, a fleet of Lambda functions with traffic patterns that shift by the hour — and you want a system that learns what normal looks like and tells you when it stops, DevOps Guru does that well and there's no honest substitute for it in a rule-based tool. It's also the right call if you don't want to author or maintain alarm thresholds at all and are fine with an ongoing per-resource-hour bill in exchange for that.

When Nuberio Audit is the better fit

If the real risk isn't subtle behavioral drift but the far more common failure mode — an alarm nobody ever created, or one that quietly stopped working after a redeploy and nobody noticed — Nuberio Audit is built for exactly that gap, and it costs nothing to find out. It's also the right starting point if you haven't set up DevOps Guru yet: there's little value in ML anomaly detection layered on top of resources that don't have baseline alarm coverage to begin with.

Can you use both?

Yes, and it's arguably the correct order of operations rather than an either/or choice. Run Nuberio Audit first to confirm every production resource has the alarms it needs — the foundational layer that catches known, well-understood failure modes cheaply. Then layer DevOps Guru on top for the anomalies a fixed threshold structurally can't express. One makes sure the floor exists; the other watches for the things that fall through it in ways nobody predicted.