We engineer reliability as a feature: full-stack observability with OpenTelemetry, SLO-driven operations, distributed tracing, intelligent alerting, and chaos engineering that gives you confidence before failures find your users.
Most organizations monitor infrastructure metrics, CPU, memory, disk. SRE is different: we instrument the user experience directly, define explicit SLOs, and manage error budgets that align engineering capacity between new features and reliability work.
We implement the full observability triad: metrics with Prometheus and VictoriaMetrics, distributed traces with Jaeger/Grafana Tempo, and structured logs with Loki/OpenSearch, unified in Grafana with correlated dashboards that let engineers jump from a slow trace to the corresponding logs and the Kubernetes pod metric in one click.
Key differentiator: We don't just install monitoring tools. We define SLOs with your stakeholders, build error budget policies, and configure multiwindow/multiburn-rate alerts that page engineers only when the error budget is actually at risk. The result: 85% reduction in alert noise with zero SLO violations missed.
The specific tools and SRE practices we implement for every observability engagement.
OTEL Collector deployed as DaemonSet and Gateway with full pipeline configuration, receivers, processors, exporters. Auto-instrumentation for Java (javaagent), .NET (auto-instrumentation package), Go (otelgo), and Python (opentelemetry-distro). Custom business metrics and spans for SLO-relevant operations. Intelligent sampling strategies: head-based for high-volume services, tail-based (Jaeger remote sampling) for anomaly preservation.
Prometheus with recording rules for pre-computing expensive queries, alerting rules with optimal label cardinality, and Thanos for long-term metric retention with global query view across clusters. VictoriaMetrics as a high-performance Prometheus-compatible alternative for high-cardinality environments. PromQL optimization for sub-second dashboard query response times. Prometheus Operator for Kubernetes-native monitoring configuration.
Jaeger and Grafana Tempo for end-to-end distributed tracing across microservices. Trace sampling configured with tail-based sampling to preserve anomalous traces (high latency, errors) while sampling down normal traffic. Trace-to-log correlation via TraceID injection into structured log lines. Exemplars linking Prometheus histogram metrics to representative traces for latency investigation.
Grafana Loki with LogQL for cost-effective log aggregation, logs indexed only by labels, not full-text, reducing storage costs 10x vs. Elasticsearch. OpenSearch/ELK with Index Lifecycle Management (ILM) policies for automated hot/warm/cold/delete tier transitions. Splunk Heavy Forwarder for regulated environments requiring Splunk. Structured JSON logging standards enforced across all services via shared logging libraries.
SLO framework design with business stakeholders, availability SLOs (request success rate), latency SLOs (p99 response time), and throughput SLOs. Error budget policies determining when to freeze feature work and focus on reliability. SLO-based alerting using Google's multiwindow/multiburn-rate algorithm, pages only when error budget burn rate is unsustainable, eliminating low-urgency alert storms.
LitmusChaos for Kubernetes chaos experiments, pod failure, network latency injection, CPU/memory stress. AWS Fault Injection Simulator (FIS) for cloud-native chaos against EC2, ECS, EKS, and RDS. Gremlin for enterprise chaos with blast radius controls and safety guardrails. GameDay methodology: structured hypotheses, defined steady states, chaos injection, and learning capture. Chaos experiments integrated into CD pipeline for regression testing.
SRE is both a technical and organizational practice. We embed with your engineering teams to define SLOs, build the observability platform, tune alerting, and establish the on-call culture that sustains reliability over time.
Our SRE engineers are Google-SRE-methodology practitioners with deep Kubernetes, distributed systems, and operational experience across financial services, healthcare, and federal platforms.
Assess current monitoring coverage across all services, what percentage have meaningful metrics, traces, and logs. Measure current MTTD and MTTR for P1 incidents. Identify on-call alert volume and false positive rate. Map critical user journeys that SLOs should protect. Deliverable: observability maturity assessment and SLO candidate list.
Deploy OTEL Collector infrastructure (DaemonSet + Gateway). Instrument all services with appropriate OTEL SDKs, auto-instrumentation for standard frameworks, manual instrumentation for business-critical operations. Configure Prometheus, Loki, and Tempo as storage backends. Build the Grafana observability platform with trace/log/metric correlation dashboards.
Facilitate SLO workshops with product managers, engineers, and operations teams. Define availability and latency SLOs for each critical service. Configure SLO dashboards in Grafana with real-time error budget burn rate visualization. Establish error budget policies, when to halt feature work, when to declare an SLO emergency, and how to conduct post-mortems after budget exhaustion.
Migrate from threshold-based alerts to SLO-based multiburn-rate alerts. Audit all existing alert rules, eliminate redundant, noisy, or untriggered alerts. Configure PagerDuty/OpsGenie escalation policies with appropriate on-call rotations. Add synthetic monitoring for proactive user-journey testing. Target: reduce alert volume by 85% while maintaining zero SLO violation blind spots.
Design and execute structured chaos engineering experiments, start with LitmusChaos pod failures in staging, progress to production chaos with blast radius controls. Run quarterly GameDays simulating failure scenarios against steady-state SLOs. Capture findings in runbooks and post-mortems. Integrate chaos experiments into CD pipeline as reliability regression tests for critical paths.
How SRE and observability investment translates into measurable reliability gains.
Modernized a federal agency's NOC from disparate Nagios/SNMP monitoring to a unified Grafana + Prometheus + Loki platform with OpenTelemetry instrumentation across 200+ microservices. MTTD reduced from 47 minutes to under 4 minutes. Alert volume dropped 82% after SLO-based alerting replaced threshold alerts. First full-year with zero P1 incidents exceeding SLA targets.
MTTD 47min → 4min, 82% alert reductionBuilt a complete SRE program for a payments platform processing $2B/month. Defined 12 SLOs covering payment success rate, authorization latency (p99 <200ms), and settlement accuracy. Error budget policies aligned engineering sprint planning with reliability targets. After 6 months, 99.97% payment success rate maintained, on-call burden reduced 60%, and deployment frequency doubled without SLO violations.
99.97% payment SLO, 60% on-call reductionImplemented distributed tracing for an e-commerce platform with 150+ microservices ahead of a peak shopping season. Identified 3 latency bottlenecks in the checkout flow via Grafana Tempo trace analysis that were invisible to existing monitoring. Resolved bottlenecks 6 weeks before peak. Peak season: 4.2M transactions/day processed with 99.96% success rate and p99 checkout latency of 380ms.
4.2M tx/day at 99.96% success rateDeployed chaos engineering program for a healthcare SaaS platform with contractual 99.9% uptime SLAs. LitmusChaos experiments revealed unhandled database connection pool exhaustion under load, a failure mode that would have caused a production outage during a patient-care peak. Fixed pre-production. AWS FIS used for quarterly DR validation. Platform has maintained 99.97% availability for 18 consecutive months.
99.97% availability, 18 months maintainedStart with an Observability Assessment, we audit your current monitoring, identify SLO candidates, and deliver a roadmap to full-stack observability.