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Observability

Prometheus & Grafana — Open-Source Observability Stack

Prometheus and Grafana are the industry standard for cloud-native observability — battle-tested by the largest Kubernetes deployments in the world. Opsio implements production-grade Prometheus stacks with Thanos or Cortex for long-term storage, Grafana dashboards for every team, and Alertmanager configurations that actually wake the right person.

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CNCF

Graduated

0

License Cost

PromQL

Query Language

Customization

CNCF Graduated
Kubernetes Native
Thanos/Cortex
Alertmanager
Open Source
Multi-Source

What is Prometheus & Grafana?

Prometheus is a CNCF open-source time-series monitoring system that collects metrics via a pull model with powerful PromQL query language. Grafana is a multi-source visualization platform for creating dashboards, alerts, and data exploration workflows.

Monitor Everything without Vendor Lock-In

Vendor-locked monitoring solutions create budget pressure that forces teams to make impossible trade-offs — monitor fewer services, retain less data, or sacrifice alert granularity. As your infrastructure grows, per-host pricing models can turn observability into one of your largest cloud expenses. A company monitoring 500 hosts with a commercial SaaS platform typically spends $120,000-$200,000 per year on licensing alone — before adding APM, logs, or additional features. At 2,000 hosts, that figure can exceed $500,000 annually. Opsio implements the Prometheus + Grafana stack to give you unlimited metrics, unlimited dashboards, and unlimited users — with zero per-host licensing. We add enterprise-grade features through Thanos for global view and long-term storage, Alertmanager for sophisticated routing, and Grafana for cross-team visibility. The only costs are compute and storage for running the stack itself, which typically amounts to 10-20% of equivalent commercial platform pricing at scale.

Prometheus works on a pull model — it scrapes metrics from instrumented targets at configurable intervals (typically 15-30 seconds). For Kubernetes environments, Prometheus uses ServiceMonitor CRDs to auto-discover pods and services, while node-exporter and kube-state-metrics provide host and cluster-level metrics out of the box. Applications expose metrics via /metrics endpoints using client libraries for Go, Java, Python, Node.js, and every major language. The data is stored as time-series in Prometheus's custom TSDB, optimized for write-heavy workloads and fast range queries. PromQL provides a powerful query language for aggregation, rate calculation, histogram analysis, and prediction.

For production environments that need long-term retention, multi-cluster visibility, and high availability, we deploy Thanos or Cortex on top of Prometheus. Thanos uses a sidecar model that uploads Prometheus blocks to object storage (S3, GCS, Azure Blob) and provides a global query endpoint across multiple Prometheus instances. Cortex provides a horizontally-scalable, multi-tenant Prometheus backend. Both solutions enable months or years of metrics retention with automatic downsampling (5-minute and 1-hour resolution for older data) that keeps storage costs manageable. Clients retaining 13 months of metrics for capacity planning and YoY comparison typically spend $200-$500/month on object storage.

The Prometheus + Grafana stack is the ideal choice for Kubernetes-native organizations, teams with strong engineering cultures that value customization, environments where per-host licensing is prohibitively expensive, and organizations that require full data sovereignty with all telemetry remaining within their own infrastructure. It integrates natively with the entire CNCF ecosystem — OpenTelemetry, Jaeger, Loki, Tempo, and every Kubernetes component exposes Prometheus-format metrics. Grafana supports over 100 data sources, so it can also visualize CloudWatch, Datadog, Elasticsearch, and InfluxDB data alongside Prometheus metrics.

However, Prometheus is not the right choice for every organization. It requires operational effort to deploy, scale, upgrade, and maintain — unlike SaaS platforms that are fully managed. Teams without Kubernetes experience or strong infrastructure engineering capabilities may find the learning curve steep. Prometheus does not provide built-in APM distributed tracing (you need Jaeger or Tempo separately), log management (you need Loki separately), or synthetic monitoring — so achieving full-stack observability requires assembling multiple tools. For organizations that prioritize a single-vendor, all-in-one experience with zero operational overhead, Datadog or Dynatrace is a better fit. Opsio helps you evaluate the total cost of ownership including both licensing and operational costs before recommending a platform.

Prometheus DeploymentObservability
Thanos / Cortex Long-Term StorageObservability
Grafana Dashboards & VisualizationObservability
Alertmanager & EscalationObservability
Custom Exporters & InstrumentationObservability
Loki & Tempo IntegrationObservability
CNCF GraduatedObservability
Kubernetes NativeObservability
Thanos/CortexObservability
Prometheus DeploymentObservability
Thanos / Cortex Long-Term StorageObservability
Grafana Dashboards & VisualizationObservability
Alertmanager & EscalationObservability
Custom Exporters & InstrumentationObservability
Loki & Tempo IntegrationObservability
CNCF GraduatedObservability
Kubernetes NativeObservability
Thanos/CortexObservability
Prometheus DeploymentObservability
Thanos / Cortex Long-Term StorageObservability
Grafana Dashboards & VisualizationObservability
Alertmanager & EscalationObservability
Custom Exporters & InstrumentationObservability
Loki & Tempo IntegrationObservability
CNCF GraduatedObservability
Kubernetes NativeObservability
Thanos/CortexObservability

How We Compare

CapabilityPrometheus + GrafanaDatadogNew RelicAmazon CloudWatch
Licensing costFree (open source)$15-23/host/month + extrasPer-user + data ingestPay-per-metric
Cost at 500 hosts (annual)$30-60K (infra + ops)$120-200K$100-180K$40-80K (basic)
CustomizationUnlimited (open source)Limited to platform featuresLimited to platform featuresLimited to AWS services
Kubernetes supportNative (Operator, CRDs)Good (Cluster Agent)GoodBasic (Container Insights)
Long-term retentionUnlimited (Thanos/Cortex + object storage)15 months max13 months max15 months max
Data sovereigntyFull (self-hosted)SaaS (US/EU regions)SaaS (US/EU regions)AWS regions only
APM / tracingRequires Tempo/Jaeger (separate)Built-inBuilt-inX-Ray (separate)
Operational overheadMedium-High (self-managed)None (SaaS)None (SaaS)Low (AWS managed)

What We Deliver

Prometheus Deployment

Production-hardened Prometheus deployed via the Prometheus Operator with service discovery, relabeling rules, and recording rules optimized for Kubernetes and cloud workloads. We configure retention policies, TSDB storage sizing, WAL configuration, and scrape interval optimization to balance metric resolution with resource consumption. High availability is achieved through Prometheus replicas with Thanos deduplication.

Thanos / Cortex Long-Term Storage

Long-term metrics storage, global query view across clusters, and automatic downsampling for cost-effective retention. Thanos sidecar uploads Prometheus blocks to S3/GCS/Azure Blob, and the Thanos Query component provides a unified PromQL endpoint across all clusters. We configure compaction, retention policies, and bucket lifecycle rules to optimize storage costs while maintaining query performance.

Grafana Dashboards & Visualization

Custom dashboards for infrastructure health, application performance, business metrics, and SLO tracking with role-based access control. We build dashboards using Grafana best practices — template variables for dynamic filtering, annotation layers for deployment markers, and alert panels for at-a-glance status. Grafana is configured with LDAP/OIDC authentication and folder-based permissions so each team sees only their relevant dashboards.

Alertmanager & Escalation

Multi-tier alerting with routing trees, silences, inhibition rules, and integrations with PagerDuty, Slack, OpsGenie, and Microsoft Teams. We design alert routing hierarchies that match your on-call structure — critical infrastructure alerts go to SRE, application-specific alerts go to the owning team, and business metric alerts go to stakeholders. Inhibition rules prevent alert storms during known outages.

Custom Exporters & Instrumentation

Custom Prometheus exporters for applications, databases, message queues, and legacy systems that do not natively expose metrics. We build exporters in Go or Python using the Prometheus client library, instrument application code with custom metrics (counters, gauges, histograms, summaries), and configure recording rules that pre-aggregate expensive queries for dashboard performance.

Loki & Tempo Integration

Grafana Loki for log aggregation with label-based querying that integrates seamlessly with Prometheus metrics. Grafana Tempo for distributed tracing with trace-to-metrics and trace-to-logs correlation. We deploy the complete Grafana LGTM stack (Loki, Grafana, Tempo, Mimir) for organizations wanting full-stack open-source observability without any commercial dependencies.

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What You Get

Production Prometheus deployment via Prometheus Operator with HA and GitOps management
Thanos or Cortex long-term storage with object storage backend and downsampling policies
Grafana instance with OIDC/LDAP authentication, folder-based RBAC, and team-specific dashboards
Alertmanager with routing trees, inhibition rules, and PagerDuty/Slack/OpsGenie integration
Infrastructure dashboards for Kubernetes clusters, node health, and persistent volume utilization
Application SLO dashboards with error budget burn rate alerts and golden signal metrics
Custom exporters for databases, message queues, and application-specific metrics
Recording rules library for pre-aggregated queries optimizing dashboard performance
Capacity planning documentation with growth projections and scaling thresholds
Team training workshop covering PromQL, Grafana dashboard creation, and Alertmanager configuration
Opsio's focus on security in the architecture setup is crucial for us. By blending innovation, agility, and a stable managed cloud service, they provided us with the foundation we needed to further develop our business. We are grateful for our IT partner, Opsio.

Jenny Boman

CIO, Opus Bilprovning

Investment Overview

Transparent pricing. No hidden fees. Scope-based quotes.

Monitoring Assessment

$8,000–$18,000

Architecture design, tool selection, and migration planning

Most Popular

Prometheus + Grafana Implementation

$25,000–$55,000

Full stack with Thanos, Alertmanager, dashboards, and alerting

Managed Monitoring Operations

$4,000–$12,000/mo

24/7 stack operations, capacity planning, and alert tuning

Pricing varies based on scope, complexity, and environment size. Contact us for a tailored quote.

Questions about pricing? Let's discuss your specific requirements.

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Why Choose Opsio

No Vendor Lock-In

Open-source stack you own completely — migrate, fork, or extend without permission. Your data, your infrastructure, your rules.

Kubernetes-Native

Prometheus Operator, ServiceMonitor CRDs, kube-state-metrics, and node-exporter — production-ready from day one with GitOps deployment.

Cost Predictability

Storage costs only — no per-host, per-metric, or per-user pricing surprises. Clients save 60-80% compared to equivalent commercial platforms at scale.

Expert PromQL

Custom recording rules, alerting expressions, and dashboards built by engineers who think in PromQL. We optimize query performance for large-cardinality environments.

Full-Stack Open Source

Prometheus + Grafana + Loki + Tempo provides metrics, logs, and traces without any commercial licensing. The complete LGTM stack for organizations with open-source mandates.

24/7 Managed Operations

We monitor, upgrade, and scale your Prometheus infrastructure so you get SaaS-like reliability from an open-source stack. Includes capacity planning, storage optimization, and incident response.

Not sure yet? Start with a pilot.

Begin with a focused 2-week assessment. See real results before committing to a full engagement. If you proceed, the pilot cost is credited toward your project.

Our Delivery Process

01

Design

Architecture planning — federation vs. Thanos, retention policies, and storage backend selection.

02

Deploy

Prometheus Operator, Thanos, Grafana, and Alertmanager with Helm and GitOps.

03

Instrument

Service discovery configuration, custom exporters, and recording rules for your applications.

04

Operate

Dashboard buildout, alert tuning, capacity planning, and team training.

Key Takeaways

  • Prometheus Deployment
  • Thanos / Cortex Long-Term Storage
  • Grafana Dashboards & Visualization
  • Alertmanager & Escalation
  • Custom Exporters & Instrumentation

Industries We Serve

SaaS Platforms

Multi-tenant metrics isolation with per-customer SLO dashboards and alerts.

Financial Services

Sub-second metrics resolution for trading system latency monitoring.

Telecommunications

Network equipment monitoring with custom SNMP exporters and Grafana maps.

Gaming

Real-time player concurrency, server performance, and matchmaking latency dashboards.

Prometheus & Grafana — Open-Source Observability Stack FAQ

Should we use Prometheus or Datadog?

Prometheus is ideal when you want zero licensing costs, full customization, and no vendor lock-in — especially for Kubernetes-native environments with 200+ hosts where commercial per-host pricing becomes expensive. Datadog is better when you need a managed SaaS solution with minimal operational overhead, built-in APM with distributed tracing, and a single platform covering metrics, logs, and synthetics. The break-even point is typically around 100-200 hosts: below that, Datadog's convenience justifies the cost; above that, Prometheus's zero-licensing model delivers significant savings. Opsio implements both and performs a total cost of ownership analysis including operational overhead before recommending a platform.

How do you handle long-term metrics storage?

We deploy Thanos or Cortex on top of Prometheus for long-term storage with object storage backends (S3, GCS, Azure Blob). Thanos uses a sidecar model that uploads TSDB blocks to object storage every 2 hours, with a compactor that merges and downsamples older data (5-minute resolution after 30 days, 1-hour resolution after 90 days). The Thanos Query component provides a unified PromQL endpoint that seamlessly queries both recent data from Prometheus and historical data from object storage. Most clients retain 13 months of metrics for year-over-year comparison at a storage cost of $200-$500/month.

Can Prometheus monitor non-Kubernetes workloads?

Yes. Prometheus has exporters for virtually everything — databases (PostgreSQL, MySQL, MongoDB, Redis), message queues (Kafka, RabbitMQ), hardware (IPMI, SNMP), network devices (via SNMP exporter), cloud services (CloudWatch exporter, Azure Monitor exporter), and custom applications. We deploy node-exporter for VM-based workloads with file-based service discovery or Consul integration. For applications that cannot expose a /metrics endpoint, we build custom exporters or use the Pushgateway for batch jobs. The Prometheus ecosystem has over 200 official and community exporters covering almost every technology stack.

How much does a Prometheus + Grafana implementation cost?

A monitoring assessment and architecture design runs $8,000-$18,000 over 1-2 weeks. Implementation of Prometheus, Thanos, Grafana, and Alertmanager with dashboards and alerting typically costs $25,000-$55,000. Adding Loki for logs and Tempo for tracing adds $15,000-$30,000. Ongoing managed monitoring operations run $4,000-$12,000 per month. The total cost of ownership is typically 60-80% less than equivalent commercial platforms for environments with 200+ hosts, even after accounting for operational management costs.

How does Prometheus handle high availability?

Prometheus itself is designed for reliability through simplicity — each instance is independent with its own TSDB. For high availability, we run two identical Prometheus replicas scraping the same targets. Thanos or Cortex provides deduplication at the query layer so dashboards show clean data despite duplicate ingestion. Alertmanager supports native clustering with gossip protocol, ensuring alerts are deduplicated and routed correctly even if one instance fails. For the query layer, Thanos Query is stateless and horizontally scalable behind a load balancer.

What is PromQL and why is it important?

PromQL (Prometheus Query Language) is a functional query language for selecting, aggregating, and transforming time-series data. It enables powerful analysis like calculating request error rates (rate(http_requests_total{status=~'5..'}[5m]) / rate(http_requests_total[5m])), predicting disk full events (predict_linear(node_filesystem_avail_bytes[6h], 3600*24)), and computing SLO burn rates. PromQL is what makes Prometheus powerful — and also what makes it challenging for teams new to time-series analysis. Opsio builds pre-configured recording rules and dashboard templates so your team gets value immediately while learning PromQL incrementally.

How do you handle alerting without creating noise?

Alertmanager provides three key mechanisms for noise reduction: routing trees that direct alerts to the right team based on labels (cluster, namespace, severity), inhibition rules that suppress downstream alerts during known outages (if the entire cluster is down, do not fire individual service alerts), and grouping that batches related alerts into a single notification. We also implement recording rules that pre-compute SLO burn rates, alerting only when error budget is burning faster than acceptable — which is far more meaningful than static threshold alerts. Teams typically see 70-80% noise reduction compared to threshold-based monitoring.

Can Prometheus scale to monitor 10,000+ targets?

Yes, with proper architecture. A single Prometheus instance can scrape 10,000-50,000 targets depending on metric count per target and scrape interval. For larger environments, we implement federation (hierarchical Prometheus) or sharded Prometheus with Thanos for a global view. Cortex and Mimir provide horizontally-scalable alternatives for extremely large environments. Key optimization techniques include reducing scrape intervals for non-critical targets, using relabeling rules to drop unnecessary metrics at ingestion, and recording rules to pre-aggregate high-cardinality series.

When should I NOT use Prometheus?

Prometheus is not the best choice when: your team lacks infrastructure engineering capability to operate the stack (a managed SaaS like Datadog requires zero operational effort); you need a single platform covering metrics, logs, traces, and synthetics out of the box (Prometheus handles metrics only — logs and traces require separate tools); you need commercial support with SLA guarantees (open-source support is community-driven unless you use a managed Prometheus service like Grafana Cloud or Amazon Managed Prometheus); or your environment is primarily serverless/managed services with minimal hosts (the cost advantage over SaaS platforms diminishes).

How does Prometheus integrate with OpenTelemetry?

OpenTelemetry (OTel) is becoming the standard for telemetry collection, and Prometheus integrates fully. The OpenTelemetry Collector can receive metrics from OTel-instrumented applications and remote-write them to Prometheus or Thanos. Prometheus can also scrape the OTel Collector's metrics endpoint directly. For organizations adopting OpenTelemetry as their instrumentation standard, we configure the OTel Collector as the central telemetry pipeline that feeds metrics to Prometheus, traces to Tempo or Jaeger, and logs to Loki — providing vendor-agnostic instrumentation with open-source backends.

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Editorial standards: Written by certified cloud practitioners. Peer-reviewed by our engineering team. Updated quarterly.
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Prometheus & Grafana — Open-Source Observability Stack

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