Voter IQ instrumented and load-tested to thousands of concurrent users before an election deadline
- p95 347ms
- measured at 1,000 concurrent users under load test
Cloud Monitoring and Observability Services
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
Recent outcomes
Voice AI · Research
6× deeper insights
Text-based interviews converted to automated phone calls
AI Automation · Ops
20k+ txns day one
Manual invoice OCR across 40+ gas stations
Loyalty · Retail
1,062 users in 4 weeks
SuperValu & Centra loyalty platform with receipt validation
SaaS · Logistics
2,000+ shipments yr 1
Multi-carrier shipping hub for Indonesian eCommerce
4.9
on ClutchThe problem
Sound familiar?
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?
Short answer
RaftLabs sets up cloud monitoring and observability with Datadog, Grafana, Prometheus, OpenTelemetry, and AWS CloudWatch: APM, structured logging, distributed tracing, SLO and error-budget tracking, and on-call runbooks. Instrumenting a first service starts around $8,000 to $20,000 in about 2 to 3 weeks; a full observability platform grows to $25,000 to $60,000 over 4 to 8 weeks, at a fixed cost.
Key takeaways
- Instrumenting a first service starts around $8,000 to $20,000 at a fixed price agreed before work starts, and ships in about 2 to 3 weeks.
- A full observability platform with distributed tracing and SLO tracking grows to $25,000 to $60,000 over 4 to 8 weeks.
- Median MTTR for high-impact outages is 51 minutes (New Relic, 2024); the platform is built to cut the time spent finding the cause.
- Tooling covers Datadog, Grafana, Prometheus, OpenTelemetry, and AWS CloudWatch depending on your stack.
- Deliverables include APM, infrastructure metrics, structured logging, distributed tracing, SLO and error-budget tracking, and on-call runbook development.
Trusted by
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.
The cost of that gap is measurable. New Relic's 2024 Observability Forecast puts the median time to resolve a high-business-impact outage at 51 minutes, and 39% of teams said resolution took an hour or more. The same report priced a high-impact outage at a median of $1.9 million per hour of downtime. ITIC's 2024 survey found that 91% of mid-size and large enterprises lose more than $300,000 for a single hour of downtime. The minutes an on-call engineer spends hunting for a cause are minutes the business is paying for. The useful goal is not more dashboards. It is a shorter path from alert to root cause across metrics, events, logs, and traces.
- 51 min
- Median time to resolve a high-impact outage
- New Relic Observability Forecast, 2024
- $1.9M
- Median cost per hour of a high-impact outage
- New Relic Observability Forecast, 2024
- 91%
- Of large enterprises lose $300K+ per hour of downtime
- ITIC Hourly Cost of Downtime Survey, 2024
Capabilities
What we build
- 01Application performance monitoring
APM instrumentation that shows where request latency comes from at the level of individual service calls, database queries, and external APIs, not aggregate CPU. Every inbound request generates a full trace waterfall, with slow-query and N+1 detection, per-vendor latency tracking, and a 14-day rolling baseline per endpoint to cut false-positive alerts.
- Built with
- Datadog APM · OpenTelemetry
- 02Infrastructure metrics and alerting
Infrastructure metrics across compute, database, network, and serverless, with alert thresholds calibrated to actual service behaviour rather than arbitrary percentages that create fatigue. Each metric is baselined over 14 days before thresholds are set, and alerts route by severity, critical pages on-call, warnings to Slack, informational to logs, with every alert linked to its runbook.
- Built with
- Datadog · CloudWatch · PagerDuty · Slack
- 03Structured logging and log aggregation
Structured JSON logging standardised across every service, so incident investigation is a query against consistent fields, not a grep through six different log formats. A standard schema enables correlation between logs and traces for a single user request, logs are retained for 30 days and archived for 12 months, and PII fields are automatically redacted before logs leave the service.
- Built with
- Datadog Log Management · Grafana Loki · CloudWatch Logs
- 04Distributed tracing
End-to-end trace correlation follows a single user request across every microservice, queue, and database it touches, so a slow checkout becomes one trace showing exactly where the time went. Trace context propagates across HTTP and async boundaries, tail-based sampling keeps 100% of error and slow traces while controlling cost, and a live dependency map shows error rates on each edge.
- Built with
- OpenTelemetry · Datadog · Grafana Tempo · Jaeger
- 05SLO and error budget tracking
Service Level Objectives defined with measurement methodology agreed before implementation, because an SLO that measures the wrong thing creates false confidence. We define the reliability dimension, the measurement query, and a realistic target, then express error budget as allowed failures per window, with burn-rate alerting that catches both sudden outages and slow degradation without flooding on-call.
- 06On-call runbooks and incident response
Runbook development that makes on-call effective rather than exhausting, each runbook written so a newly on-call engineer can investigate an unfamiliar alert without waking a senior. It covers what the alert measures, investigation steps, common root causes, and escalation, with every alert linked to its runbook and a major-incident playbook plus postmortem template that produces owned action items.
- Built with
- PagerDuty · OpsGenie
Where observability is heading
Two shifts are worth planning for, and worth being honest about.
eBPF-based instrumentation reads traces, metrics, and network data straight from the Linux kernel, so you get visibility without adding an agent library to every service or redeploying code. It is strong for infrastructure and network-level signals, and for polyglot fleets where per-language instrumentation is a chore. It does not replace application traces that carry your business context, so we treat it as a complement to OpenTelemetry, not a swap for it.
AI-assisted anomaly detection earns its place on one specific problem: surfacing a metric that drifted in a way no static threshold would have caught. It is not a reason to skip SLOs or runbooks. A model that flags an anomaly still needs a human-readable action attached to it, or it becomes one more alert the team learns to ignore. We wire anomaly detection to the same runbook discipline as every other alert, so a flag always points to a next step.
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
What clients say
What our clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
Charles E.
USAEntrepreneur at Aggie Technologies
All of the sprints were completed on schedule and on budget. We highly recommend RaftLabs!
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Read moreFrequently 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 and ships in about 2 to 3 weeks. 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 over 4 to 8 weeks. We start with one instrumented service and expand from there. Fixed cost agreed before development starts.
Work with us
Tell us what you need. We'll tell you what it would take.
We scope Cloud Monitoring and Observability in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.