Systems Monitoring & Observability Consulting

See Everything. Control Your Costs. Never Miss a Production Issue.

SaaS monitoring tools charge per host, per metric, per gigabyte ingested. As your infrastructure scales, the bill skyrockets. You start deleting metrics to stay under budget. You stop logging because storage costs too much. You lose visibility exactly when you need it most.

Self-hosted observability flips the economics. Pay once for infrastructure, get unlimited visibility. Sharper Cloud builds production-grade Prometheus, Grafana, and Loki stacks that cost a fraction of SaaS alternatives while giving you complete control and data ownership.

The Problem: SaaS Observability Becomes Prohibitively Expensive

Your observability stack costs too much:

  • SaaS monitoring charges per host. Add 10 servers, costs triple.
  • Per-metric pricing incentivizes you to delete important metrics to save money.
  • Per-GB ingestion costs for logging mean you sample logs instead of seeing everything.
  • Vendor lock-in means switching is expensive and disruptive.
  • You don’t own your data. Provider outages mean you lose visibility.
  • The tool’s limitations become your infrastructure’s limitations.

Meanwhile, your best engineers waste time fighting the tool instead of using it to understand systems.

Our Solution: Self-Hosted Observability Stack

We deploy and maintain production Prometheus, Grafana, and Loki stacks that give your team unlimited visibility at 10% the cost:

Monitoring Architecture

  • Prometheus for metrics collection with HA setup
  • Grafana for visualization and dashboards
  • Loki for log aggregation
  • AlertManager for intelligent alerting
  • Integration with your monitoring targets (Kubernetes, databases, application metrics)

Custom Dashboards

  • Purpose-built dashboards for your specific services
  • Service-level dashboards, infrastructure dashboards, business metric dashboards
  • Dashboard templating so similar services share dashboard patterns
  • Dashboard backup and version control

Intelligent Alerting

  • AlertManager configuration for intelligent alert grouping and routing
  • Integration with PagerDuty, Slack, email, webhooks
  • On-call rotation management
  • Alert tuning to reduce false positives and alert fatigue

SLO & SLI Implementation

  • Service Level Objective definition and tracking
  • Service Level Indicator implementation
  • Error budgets calculated and visible
  • Alert on SLI violations before customers are affected

Scope of Work: What’s Included

Monitoring Infrastructure Design & Deployment

  • Prometheus cluster setup with HA and long-term storage
  • Loki log aggregation setup (or Filebeat/Elasticsearch alternative)
  • Grafana instance deployment with authentication
  • Scrape configuration for all your infrastructure
  • Data retention policies and storage planning
  • Backup and disaster recovery setup

Custom Dashboard Development

  • Application-specific dashboards (latency, errors, throughput)
  • Infrastructure dashboards (CPU, memory, disk, network)
  • Business metric dashboards (revenue, user growth, feature adoption)
  • Dashboard documentation and ownership assignment
  • Training for your team to create/modify dashboards

Alerting Strategy & Implementation

  • Alert rule definition for critical services and infrastructure
  • AlertManager configuration for routing and grouping
  • Integration with PagerDuty, Slack, and notification systems
  • On-call rotation setup (if using PagerDuty)
  • Alert runbook creation for quick response
  • Tuning to minimize alert fatigue

SLO & SLI Implementation

  • SLO definition for your key services
  • SLI calculation and monitoring
  • Error budget tracking
  • Availability and reliability dashboards

Documentation & Handoff

  • Architecture documentation and diagrams
  • Operational runbooks for common scenarios
  • Training for your team to maintain the stack
  • Queries and dashboard library documentation

Tools & Technologies

Metrics Collection: Prometheus, node_exporter, kube-state-metrics, custom exporters

Visualization: Grafana, Grafana Loki for logs

Log Aggregation: Loki (recommended), Filebeat, Elasticsearch, or CloudWatch/Stackdriver integration

Alerting: AlertManager, PagerDuty integration, Slack webhooks

Storage: Prometheus long-term storage (S3, GCS, or local), Loki backends

Deployment: Kubernetes (Helm charts), Docker Compose, or VMs (Systemd)

Infrastructure Monitoring: node_exporter, blackbox_exporter, SNMP exporter, custom exporters

Why Sharper Cloud for Observability

Justin Sharp has:

  • Built production observability stacks serving millions of metrics per second
  • Implemented monitoring at companies like Divvy that handled high-volume financial transactions
  • Designed SLO frameworks and error budgets for mission-critical systems
  • Optimized monitoring costs by 90%+ through self-hosted infrastructure
  • Trained teams to use observability effectively for debugging and capacity planning

He runs his own Prometheus + Grafana stack in production and knows exactly how to operate these systems at scale.

Typical Engagement Results

  • Observability cost reduced by 70-90% compared to SaaS alternatives
  • Unlimited metrics collection without cost penalties for scale
  • Complete data ownership — your logs and metrics stay on your infrastructure
  • Custom dashboards tailored to your actual operations
  • Intelligent alerting that catches real issues without overwhelming your team
  • SLO visibility so reliability is measurable and tracked
  • Team trained to operate and extend the monitoring stack

Real example: A SaaS company reduced monitoring costs from $18K/month (DataDog) to $3K/month (self-hosted Prometheus + Grafana on Kubernetes) while improving observability. They went from sampling 5% of logs to storing 100%, gaining visibility into rare edge cases that were causing production issues.

Frequently Asked Questions

Won't self-hosted monitoring require dedicated ops people?

No. A well-designed Prometheus/Grafana/Loki stack on Kubernetes is highly reliable and requires minimal maintenance. We deploy it with high availability, automate backups, and design for operational simplicity. Most teams spend less than 2 hours per month on maintenance after initial setup.

How long until we see ROI on self-hosted monitoring?

Usually within 2-3 months. If you're currently paying more than $10K/month for SaaS monitoring, self-hosted pays for itself almost immediately. Even smaller teams see value in data ownership and unlimited metrics collection.

Can we migrate from DataDog/New Relic to self-hosted?

Yes. We'll help you plan the migration, set up parallel monitoring, and cut over once you're confident in the new stack. You'll keep historical data in your old system while the new system collects going forward. Some dashboards may need reconstruction, but metrics collection starts immediately.

What about compliance and security for self-hosted monitoring?

Self-hosted gives you complete control. We implement proper network segmentation, authentication, encryption in transit, and access controls. For compliance (SOC2, PCI DSS, HIPAA), self-hosted often makes compliance simpler since you control where data lives and how it's encrypted.

Can we still use some SaaS tools alongside self-hosted monitoring?

Absolutely. Many teams use self-hosted monitoring for infrastructure visibility and maintain SaaS tools for specific capabilities (e.g., user experience monitoring, synthetics testing). We can integrate the best of both approaches.

Ready for Real Observability?

Unlimited visibility at a fraction of the cost. Let’s build an observability stack that gives your team real insight into production systems.

Book a Free 30-Minute Consultation to discuss your current monitoring setup, evaluate cost savings potential, and plan a migration strategy.

Related services: Self-hosted monitoring pairs well with Kubernetes Consulting for container orchestration, Cloud Infrastructure for underlying architecture, and CI/CD Automation for deployment insights.