When something goes wrong in production, how long does it take your team to identify which service, which code path, and which request caused the incident?
Are your alerts calibrated to your services' actual behaviour, or do they fire so often from false positives that the team has learned to ignore them?
Finding out about a production incident from a customer email is not a monitoring strategy.
Monitoring tells you that something is wrong. Observability tells you why. The difference matters when a production incident is active: a dashboard showing CPU at 98% tells you the service is struggling but not which code path is causing it. Traces, structured logs, and distributed tracing tell you the exact request that failed, which services it touched, and where the time went.
We instrument applications and infrastructure with monitoring and observability using Datadog, Grafana, Prometheus, OpenTelemetry, and AWS CloudWatch. From the first alert configured to a mature observability platform with dashboards, SLOs, on-call runbooks, and incident response workflows.
Application performance monitoring with request traces, error rates, and p99 latency for every service endpoint
Infrastructure metrics -- CPU, memory, disk, network -- with alert thresholds calibrated to your services' actual behaviour
Structured logging with search and correlation across services so incident investigation doesn't require SSHing into servers
SLO tracking for services with defined reliability targets -- so you know before a customer does when you're burning error budget
RaftLabs designs and implements monitoring and observability using Datadog, Grafana, Prometheus, OpenTelemetry, and AWS CloudWatch -- APM, infrastructure metrics, structured logging, distributed tracing, SLO tracking, and on-call runbook development. For engineering teams that need production visibility rather than reactive incident response. Most projects deliver in 4 to 8 weeks at a fixed cost.
The engineering cost of poor observability is not paid at the moment instrumentation is skipped -- it is paid during every incident that follows. An on-call engineer at 2am with no traces, no structured logs, and dashboards showing only aggregate CPU metrics is an engineer who will spend the next two hours adding logging, deploying, reproducing the problem, and finally finding the cause. That two hours is the bill for the instrumentation work that was deferred.
Production systems without observability are also systems where incidents repeat. Without data showing the exact cause of the last incident, the postmortem produces guesses. The same guesses get made in the next postmortem. Observability creates a feedback loop: incidents produce data, data produces understanding, understanding produces the specific fix rather than the plausible-sounding one.
What we build
Application performance monitoring
APM instrumentation for your services using Datadog APM, New Relic, or OpenTelemetry. Request traces showing latency breakdown across service calls -- where is the time going, which database query is slow, which external API is adding tail latency. Error rate and p99 latency dashboards per endpoint. Database query performance tracking. External API call monitoring. Performance baseline establishment so anomaly detection has a meaningful reference point.
Infrastructure metrics and alerting
CPU, memory, disk, network metrics for EC2, ECS, EKS, Lambda, RDS, and other AWS and GCP services. Dashboards per service and per environment so the view matches what the team is responsible for. Alert configuration with thresholds calibrated to observed behaviour rather than arbitrary percentages -- 80% CPU on a service that routinely runs at 75% is not an actionable alert. Paging integration with PagerDuty or OpsGenie for on-call escalation with appropriate severity routing.
Structured logging and log aggregation
Structured JSON log format standardised across services so logs are queryable by field rather than parsed by regex. Log aggregation into Datadog, Grafana Loki, or AWS CloudWatch Logs with consistent field naming across services. Log-based metrics derived from structured log fields -- error counts by type, request counts by status code. Log retention and archiving policy. Query examples for common incident investigation scenarios documented in the runbook.
Distributed tracing
End-to-end trace correlation across microservices using OpenTelemetry or Datadog APM. Trace IDs propagated through HTTP headers and message queues so a single user request can be followed across every service it touches. Service dependency map built from trace data showing which services call which. Trace sampling configuration to balance coverage with storage cost. Trace search for high-latency and error traces so incident investigation starts at the relevant request.
SLO and error budget tracking
Service Level Objective definition for reliability targets -- 99.9% availability, p99 latency under 200ms -- with the measurement methodology agreed before implementation. Error budget burn rate calculation and dashboards so the team sees how quickly remaining budget is being consumed. Alert when burn rate indicates the SLO will be missed before the measurement window closes. SLO review incorporated into the postmortem process to adjust targets based on evidence from actual incidents.
On-call runbooks and incident response
Runbook development for each alert: what the alert means, initial investigation steps, common causes and resolutions, escalation contacts. Runbook linked directly from the alert so the on-call engineer has context without waking someone else. Incident response playbook for major incidents covering communication, investigation, and remediation steps. Postmortem template and facilitation process for extracting learning from incidents rather than just documenting what happened.
Flying blind in production?
Tell us your current monitoring setup, what your last incident looked like from the inside, and how long the investigation took. We'll scope the observability platform and give you a fixed cost.
Related DevOps services
DevOps as a Service -- full DevOps capability overview
CI/CD Pipeline Setup -- pipelines that deploy what your monitoring covers
Kubernetes Infrastructure -- infrastructure that monitoring tracks
Infrastructure as Code -- infrastructure provisioned reproducibly and monitored consistently
Related services
Custom Software Development -- applications instrumented for observability from the start
Cloud Migration -- monitoring established as part of cloud migration to replace on-premise tooling
Real-Time App Development -- real-time applications that require latency and throughput monitoring
Frequently asked questions
AWS CloudWatch is the default starting point if you're on AWS -- it captures infrastructure metrics without additional instrumentation and integrates with Lambda, ECS, RDS, and other AWS services natively. Its query language and dashboard capabilities are limited compared to dedicated observability platforms. Datadog is the most capable all-in-one observability platform -- APM, infrastructure metrics, logging, and tracing in one place with good default dashboards. It's more expensive than open-source alternatives. Grafana with Prometheus is the open-source option: more operational overhead to run, but no per-host licensing cost. We recommend based on your team size, engineering operational capacity, and budget.
Monitoring is the practice of collecting and alerting on predefined metrics -- CPU usage, error rate, response time. You get alerted when a known metric crosses a threshold. Observability is the property of a system that lets you understand its internal state from its external outputs -- logs, metrics, and traces. An observable system lets you answer questions you didn't think to ask when you wrote the code, using the data the system emits. Monitoring tells you something is wrong. Observability tells you why, without requiring a code deploy to add more logging after the incident.
Alert fatigue comes from alerts configured with arbitrary thresholds rather than thresholds calibrated to actual service behaviour. The fix: baseline each metric across a normal operational period, set alert thresholds at statistically significant deviations from that baseline, and suppress alerts during known maintenance windows. Every alert should have a runbook with a clear action -- if the on-call engineer doesn't know what to do when the alert fires, the alert isn't ready for production. We audit existing alert configurations as part of monitoring engagements and rationalise them before adding new ones.
Instrumentation and alert configuration for a single service or small application typically runs $8,000 to $20,000. A full observability platform covering multiple services with APM, distributed tracing, SLO tracking, and on-call runbook development typically runs $25,000 to $60,000. Fixed cost agreed before development starts.