Prometheus Consulting

Prometheus is an open-source monitoring and alerting system originally developed at SoundCloud and now maintained by the Cloud Native Computing Foundation (CNCF), designed to collect, store, and query time-series metrics for reliable observability. It pulls metrics over HTTP on a regular schedule (scraping), stores them efficiently, and provides the PromQL query language for building dashboards, troubleshooting performance issues, and defining alert conditions. Prometheus is commonly used to monitor applications and infrastructure in cloud-native environments—especially on Kubernetes—and supports service discovery, metric relabeling, and a broad exporter ecosystem for systems such as Linux hosts, databases, and web servers.

• Provides real-time visibility into system performance and health, enabling proactive issue resolution.
• Alerts to potential problems before they escalate, reducing downtime and improving service reliability.
• Tracks and analyzes key performance indicators (KPIs), aiding in informed decision-making.
• Enhances security by detecting unusual activities or breaches, allowing for immediate response.
• Facilitates resource optimization by identifying underutilized or overburdened assets.
• Supports compliance efforts by maintaining logs and records of system activities.
• Enables a data-driven approach to IT management, improving overall operational efficiency.
  

Prometheus is an open-source monitoring and alerting system designed for collecting and querying time-series metrics. It is commonly used to improve reliability and performance by making system behavior measurable and actionable.

• Pull-based metrics collection via HTTP scraping simplifies instrumentation and reduces reliance on per-host agents in many environments.
• PromQL enables flexible ad hoc queries, aggregations, and rate calculations for troubleshooting and capacity analysis.
• Strong integration with cloud-native platforms, especially Kubernetes, through service discovery and label-based targeting.
• Label-based dimensional metrics support high-cardinality segmentation across services, instances, regions, and deployments.
• Built-in alerting rules allow symptoms to be detected from metrics and routed to incident workflows with predictable logic.
• Efficient local time-series storage is well-suited for fast queries over recent data and operational dashboards.
• Exporter ecosystem covers common infrastructure components such as Linux hosts, databases, message queues, and proxies without custom code.
• Federation and remote storage options support scaling across multiple clusters and longer retention requirements.
• Designed for reliability during partial outages, with local scraping and storage continuing even when downstream systems are degraded.

Prometheus is a strong fit for metrics-based monitoring and alerting, particularly in dynamic environments. For very long retention, global query across many clusters, or strict multi-tenant isolation, remote write to long-term storage or a managed backend is often required.

Common alternatives include Grafana Mimir, VictoriaMetrics, InfluxDB, and Datadog.

Our experience with Prometheus helped us build repeatable patterns, runbooks, and automation that we use to deliver reliable monitoring setups for clients across Kubernetes and VM-based environments.

Some of the things we did include:

• Deployed Prometheus in Kubernetes using Helm, including resource sizing, retention policies, and production-ready scrape configurations.
• Implemented alerting with Alertmanager, including routing rules, silences, inhibition logic, and on-call friendly notifications.
• Built dashboards and operational views by integrating Prometheus with Grafana, aligning panels and alerts to SLOs and incident response workflows.
• Instrumented applications and services with consistent metric naming, labels, and recording rules to reduce query cost and improve signal quality.
• Integrated Prometheus with OpenTelemetry pipelines to standardize telemetry collection and reduce per-team instrumentation drift.
• Configured exporters (node, blackbox, and service-specific exporters) and standardized scrape targets for common infrastructure components.
• Hardened Prometheus deployments with network policies, RBAC, and secret management, and reviewed label hygiene to prevent sensitive data leakage.
• Improved query performance by optimizing PromQL, introducing recording rules, and tuning scrape intervals based on cardinality and business needs.
• Designed high-availability patterns (replication, sharding where appropriate, and safe upgrade procedures) and validated recovery steps during planned maintenance.
• Created documentation and training sessions for engineers and SREs on PromQL, alert tuning, and troubleshooting common ingestion and alerting issues.

This experience helped us accumulate significant knowledge across multiple Prometheus use-cases, and it enables us to deliver high-quality Prometheus setups that are maintainable, observable, and aligned with how teams actually operate systems.

Some of the things we can help you do with Prometheus include:

• Run a Prometheus maturity assessment and deliver a prioritized report covering scrape strategy, alert quality, label hygiene, and operational risks.
• Create an adoption roadmap that standardizes metrics, SLOs, and alerting practices across teams to improve reliability and on-call outcomes.
• Design and implement production-grade Prometheus deployments on Kubernetes with Helm, high availability patterns, and backup/retention policies.
• Instrument services and platforms with best-practice metrics and dashboards, and define actionable alert rules aligned to incident response workflows.
• Establish security and governance guardrails (RBAC, network policies, multi-tenancy boundaries, and secret handling) to meet compliance requirements.
• Optimize performance and cost by tuning scrape intervals, cardinality, retention, recording rules, and remote write/long-term storage strategies.
• Automate configuration and lifecycle management with Infrastructure as Code and GitOps-friendly workflows to reduce drift and speed up changes.
• Troubleshoot production issues such as missing metrics, query latency, high cardinality, and noisy alerts, and harden the platform for scale.
• Enable your team with hands-on training for PromQL, alerting best practices, and operational runbooks so teams can self-serve confidently.