See everything. Know before the problem happens.
Observability built on the three telemetry types, metrics, logs and traces, to shorten the gap between failure and diagnosis.
AWS defines observability as how well you can understand what is happening in a system, typically by instrumenting it to collect metrics, logs or traces. The practical distinction from monitoring is this: monitoring answers whether something is outside the norm; observability lets you find out why, including for failures nobody anticipated when the alert was created.
The three telemetry types answer different questions. Metrics show trends and trigger alerts. Logs tell in detail what happened. Traces show the path of a request across services, and that is usually the missing piece, precisely the one that answers which of the twelve services in the chain introduced the latency.
In distributed architectures, the cost of diagnosis grows faster than the number of services. When the data you need is spread across hundreds of components, investigating without proper correlation burns hours of senior engineering time on every incident. Well-implemented observability is, in practice, an investment in MTTR and in preserving the focus of the engineering team.
If two or more of these scenarios sound familiar, it is worth a conversation.
Initial assessment at no cost
The exact composition depends on your environment and on the requirements identified during the assessment. This is the baseline we work from for this solution.
Metrics, logs, alarms and dashboards, with context across related signals in a single view
Interactive querying and analysis of large log volumes
Distributed tracing of requests crossing multiple services
Instrumentation with an open standard, with no lock-in to an observability vendor
Metrics at scale with PromQL queries, common in Kubernetes environments
Unified dashboards over multiple data sources
Synthetic monitoring that tests critical journeys from the user point of view
Visibility of AWS events that affect your resources
Event routing for response and remediation automation
Recording and tracking of operational items arising from incidents
We identify which flows actually matter to the business. Instrumenting everything with the same weight drives cost up and creates noise, the opposite of the goal.
We set service level indicators and the matching targets. Without SLOs there is no objective criterion to decide whether waking someone at three in the morning is warranted.
We collect all three telemetry types using OpenTelemetry as the standard, keeping portability in case the observability stack changes later.
We build distinct views for business, operations and engineering. A single dashboard trying to serve all three audiences usually serves none.
Every alert gets an owner, a severity and a runbook. An alert with no defined action is noise, and noise trains the team to ignore the real signal.
We start measuring deployment frequency, lead time, change failure rate and time to restore service, the four DORA metrics AWS documents for tracking delivery maturity.
Mean time to recovery, the indicator observability moves most directly
Mean time to detection, measured from the start of the problem until the alert fires
Share of the period in which the service operated within the agreed target
Proportion of alerts that led to real action
Market references documented by AWS itself in equivalent scenarios. Each card links to the original story published on the AWS site.
Rebuild of the failover mechanism of a corporate identification solution using Amazon CloudWatch, replacing a manual process that depended on human intervention.
Published result
AWS published the drop in failover time from 30 minutes to 3 minutes.
Transformation of service desk operations on AWS, focused on automating support and operational routines.
Published result
Operations and service desk case published by AWS.
The cases above are customer stories published by Amazon Web Services. The results cited belong to the companies mentioned and serve as a reference for what is technically achievable on AWS. They do not represent results delivered by Núvoli, nor a guarantee of equivalent performance, since every environment has its own context.
Our approach is not opinion. It follows guidance documented by AWS, and you can check the source yourself.
The first step is an assessment of your current setup, at no cost and with no commitment. You come out of it with risks mapped and priorities defined, even if you decide not to move forward with Núvoli.
