Many modern monitoring environments provide the foundational tools we’ve come to rely on: time-series metrics, logs, dashboards, and fixed thresholds paired with alerting capabilities. These components are essential for keeping systems running and spotting issues as they arise. However, while they form a solid base, they often fall short of delivering true observability—the ability to deeply understand a system’s behaviour and performance in real time. More importantly, they lack the intelligence and context needed to shift organizations toward SLO-driven decision-making, which is increasingly critical in today’s fast-paced, customer-centric world.
Service Level Objectives (SLOs) represent a modern and powerful approach to bridging this gap. Unlike traditional monitoring that focuses solely on raw data or static thresholds, SLOs provide a structured way to measure service health against meaningful, business-aligned targets. They define the acceptable level of performance and reliability for a service—think uptime, latency, or error rates—and tie directly into the expectations set by Service Level Agreements (SLAs) with both internal stakeholders and external customers. By tracking SLOs, businesses can proactively assess whether they’re meeting their commitments or at risk of breaching them, shifting from reactive firefighting to strategic oversight.
Incorporating SLOs into monitoring practices isn’t just a technical upgrade—it’s a mindset shift. It empowers teams to prioritize what matters most to the business and its users, offering a clear lens through which to evaluate success or failure. Are services healthy? Are customers getting the experience they’ve been promised? Are contractual obligations being met? SLOs answer these questions with precision, making them an indispensable tool for organizations aiming to align technical performance with business outcomes in an increasingly complex digital landscape.
Where Monitoring Falls Short
Most environments have the fundamental indicators and teams often spend more time troubleshooting symptoms rather than root cause. They are typically based on:
Time-series metrics
Dashboards that visualise data
Alerts based on static/fixed thresholds
Basic insights into system performance
They lack key capabilities such as:
Anomaly detection: Many paid observability vendors offer sophisticated anomaly detection, but open-source solutions and homegrown systems often fall behind.
Correlation of golden signals: The ability to combine throughput, failure rates, and latency into meaningful SLOs (Service Level Objectives) is missing.
Maturity and automation: Without these capabilities, organisations struggle to automate decision-making and improve reliability proactively.
Continuous Assessment loops: A moving time window where multiple indicators(metrics, logs, traces) are assessed and root cause analysis provided.
Predictability or forecasting: The ability to know when a service will break or cause disruption.
Change analysis: Knowing what has changed in code or configuration that may have introduced deviation.
Finding a Better Approach
Through our partnership with Autoptic—stemming from over a decade of collaboration at a leading observability vendor—we’ve tackled these key gaps that are very visible in the field. Building on mutual trust and deep customer insight, we’ve now teamed up to address these gaps using open-source solutions while delivering capabilities that rival top vendors, all without any major overhauls.
We took a real-world use case and tasked Autoptic to produce SLO's through a continuous assessment loop.
We combined multiple metrics from AWS ALB's, volume of requests, latency, failures, http500's and even measured health of targets.
Produced an SLO assessment in real time that truly reflected current state.
We then sent the SLO metrics back to Prometheus, making them fully available in our existing stack (Grafana) for exec level insight.
Sent alerts to slack to inform the wider team, not only about a deviation, but we provided a full assessment of why the SLO was impacted.
Real-Time Anomaly Detection in Action
Autoptic includes an anomaly detection engine, enabling us to identify deviations across all metrics in our data sources. To define an SLO, we first identify the indicators. But, in addition to those indicators we select for our SLO, Autoptic is also scanning and analysing other metrics in the same assessment loop from multiple data sources.
A practical example: AWS ALB via AWS Cloudwatch
In AWS, ALBs are the last infrastructure piece before user traffic disperses into an environment. So an excellent bellwether for performance and throughput.
Autoptic was able to detect anomalies and provide reports on real-time deviations that included possible root cause.
Traditional static thresholds, like high/low watermarks, often react too late.
Autoptic provided proactive alerts by identifying deviation before they became critical. These could quite easily be sent to automation solutions like aiops to repair.
Autoptic’s capabilities went beyond alerting. By analysing metrics and indicators across multiple data sources, it delivered a potential root cause summary directly to our Slack channel. Initially aiming to create an SLO, we instead gained a comprehensive breakdown of the "why" behind issues, complete with root cause insights—far surpassing simple metric combinations or anomaly detection.
The Future: Predictive & Proactive Observability
With SLO-driven observability, organisations can shift from reactive alerting to proactive reliability management. The combination of:
Continuous SLO assessment
Anomaly detection across all data sources
Root Cause Analysis
PUSH Integration with Prometheus and other tools (even your customers)
Many teams find themselves hitting a wall with free-to-use tools, unable to move forward. While these tools excel at gathering and displaying data, they often fall short when it comes to identifying patterns and trends that help teams advance their observability beyond basic dashboards and alerting.
Sharing the intelligence: Pumping the SLO right back into Prometheus
In our last step, we aimed to share SLO metrics without adding a new Grafana data source integration. This enabled us to build executive dashboards that emphasize business context—such as site or transaction performance—while streamlining operator workflows with one fewer UI. We accomplished this by pushing the metrics directly into Prometheus, making SLOs readily available for Grafana+Prometheus users.
Visualising in Grafana is then up to you:
Autoptic brings intelligence to your data, leveraging its distinctive ability to craft SLOs tied to business context and possible root cause analysis. This empowers you to better prepare and avoid major outages, all without committing to sprawling vendor observability ecosystems. Ultimately, it’s the same data —how you use it is what sets it apart, and that can be a game-changer.
We demonstrated that there is a solution that fills a critical monitoring gap, with Autoptic offering far more than just addressing our urgent need. It includes a co-pilot feature that leverages AI, allowing you to interact directly with your data and much much more. Its designed by engineers for engineers.
If you would like a demo of our SLO use case and many others, reach out to me via greg@visibilityplatforms.com.
Website: www.autoptic.io
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