Know When Your App Breaks Before Your Users Do
We set up monitoring, alerting, and observability that actually tells you what is wrong — not just that something is wrong. Dashboards your team reads, alerts your on-call team responds to.
Full-Stack Observability Setup & Configuration
From Prometheus metrics to Sentry error tracking to SLO dashboards — we wire together the observability stack your team needs to operate production with confidence.
Prometheus & Grafana Setup
Prometheus scrape config for all your services, Node Exporter for host metrics, and Grafana dashboards that show the things you actually care about — not just the defaults that come with every template.
Datadog / New Relic Integration
Datadog Agent deployment via Helm or Ansible, APM instrumentation for your language (Node, Python, Java, Go), log pipelines, and custom dashboards per team. We configure it for your stack, not a generic demo environment.
Error Tracking (Sentry)
Sentry configured with proper project structure, release tracking, source maps for minified JS, and alert rules that group similar errors together. Not 1,000 duplicate error events per hour flooding your inbox.
Log Aggregation (ELK / Loki)
Centralized log management with Elasticsearch + Logstash + Kibana, or Grafana Loki + Promtail for lighter-weight setups. Structured logging enforced at the application layer, indexed fields that make queries fast.
Uptime Monitoring
External uptime checks (Pingdom, Better Uptime, or self-hosted Uptime Kuma) with multi-location checks, SSL certificate expiry alerting, and status page generation so your users know what is happening when something goes wrong.
Custom Alerting Rules
Alerting rules tuned to your actual traffic patterns — not default thresholds. PromQL or Datadog alert conditions that fire on things that need human attention, with severity routing (critical to PagerDuty, warning to Slack).
APM (Application Performance)
Distributed tracing, service dependency maps, slow query detection, and request latency breakdowns. You can see exactly where time is being spent in a slow request — without adding log statements and redeploying.
SLO/SLA Dashboard Setup
Service Level Objectives defined with your product team, error budget burn rate calculations, and dashboards that show engineering and business stakeholders the same source of truth about reliability.
From Zero to Production-Ready
Observability Audit
We review what you currently have — or do not have. We identify the gaps: missing metrics, no log aggregation, alerts that fire on the wrong things, or no alerting at all. Output is a prioritised list of what to fix first.
Metrics & Logging Setup
Prometheus, Grafana, or your chosen commercial tool deployed and scraping metrics. Log aggregation pipeline configured. Application instrumented for APM. All data flowing before we move to dashboards.
Dashboard & Alert Configuration
Dashboards built per audience (engineering, operations, business). Alert rules written with PromQL or native tool syntax. Routing to PagerDuty, OpsGenie, or Slack configured. Severity levels mapped to escalation policies.
On-Call Runbook Documentation
Runbooks written for the 10 most common alert types — what the alert means, how to diagnose it, how to fix the common causes. Linked from alert messages so the on-call engineer has context at 3am.
Why Businesses Trust Us with Their Observability
Alerts that fire on things that matter
We have seen monitoring setups where engineers mute everything because they are flooded with noise. We write alert rules against your actual SLOs — if error rate is above X% for 5 consecutive minutes, that is worth waking someone up. A 1-second CPU spike is not.
Dashboards per team and audience
Engineering gets detailed pod-level metrics. Operations gets service health and SLA status. Business gets request volume and error rate trends. One monitoring platform, multiple purpose-built views.
Sentry that does not cry wolf
Sentry configured with proper fingerprinting so similar errors are grouped, not counted individually. Performance sampling set correctly so you get representative data without burning through your event quota on high-traffic endpoints.
Log retention that does not bankrupt you
Log pipelines designed with tiered retention — hot storage for recent logs, cold storage for compliance archival. We have cut client log storage costs by 60% by filtering noise at ingestion rather than storing everything and indexing nothing useful.
SLO tracking so you see burndown
Error budget burn rate dashboards so you know whether you are on track to meet your monthly SLO before month-end. Not "we missed SLA again" in a post-mortem — but a warning 2 weeks in that you are burning down fast.
Runbooks linked from every alert
Every PagerDuty or OpsGenie alert includes a link to the relevant runbook. The on-call engineer does not have to remember what high memory pressure on the API service usually means — it is written down and linked.
Set Up Monitoring Before Your Next Incident
The best time to set up monitoring is before your next outage. We will get your observability stack live within 2 weeks and hand you dashboards your team will actually use.
Didn't Find What You Were Looking For?
We're here to help you get the answers you need, quickly and clearly.
Prometheus + Grafana is open source and free (you pay for hosting/storage), requires more configuration, and gives you full control. Datadog is a paid SaaS that handles the infrastructure for you, has excellent APM out of the box, and is significantly faster to get running. For teams with strong Kubernetes experience and engineering time to invest, Prometheus works brilliantly. For teams that want monitoring running in days without ongoing maintenance, Datadog is worth the cost. We implement both — we will recommend based on your budget and team capacity.
A Prometheus + Grafana setup for a Kubernetes cluster with dashboards and alerting: ₹60,000–1,00,000. Datadog or New Relic integration with APM, log management, and dashboards: ₹80,000–1,50,000. The ongoing tool cost varies — Datadog bills per host per month (roughly $15–$23/host), while Prometheus is free but requires your own infrastructure. We include a cost estimate in our proposals.
The four golden signals (Google SRE Book): latency (how long requests take), traffic (how much load you are handling), errors (rate of failed requests), and saturation (how full your resources are). For web services, that means HTTP request duration, request rate, 5xx error rate, and CPU/memory utilisation. Beyond those, what matters is specific to your application — we define the right metric set during the observability audit.
ELK Stack (Elasticsearch, Logstash, Kibana) is powerful but resource-intensive and complex to operate. Grafana Loki is much lighter, indexes labels rather than full text, and works well when combined with Grafana for dashboards. Datadog Logs and Logz.io are managed SaaS options. For most teams with under 50GB/day of logs, Loki is the best balance of capability and cost. For heavy analysis workloads or compliance requirements, ELK or a managed service makes more sense.
The basics: PagerDuty or OpsGenie with a rotation schedule, escalation policies (if primary on-call does not acknowledge in 5 minutes, page the secondary), and alert severity tiers (P1 wakes people up at 3am, P3 creates a Jira ticket). Every alert needs a runbook. Every runbook needs a "if this does not fix it" escalation path. We set this up as part of the monitoring engagement and document the on-call process for your team.
Yes. Monitoring agents (Prometheus exporters, Datadog Agent, Sentry SDK) are additive — they run alongside your application without requiring restarts or code changes for infrastructure metrics. APM instrumentation (distributed tracing, request tracking) requires adding an SDK to your application code, but this is typically a 2–5 line change and a redeploy, not a risky migration. We prioritise zero-downtime instrumentation in every engagement.
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