Observability Stack Engineering
We design and operate observability platforms that actually help engineers debug, with intelligent alerting, distributed tracing and OpenTelemetry-native instrumentation, while cutting your Datadog or Splunk bill in half.
Dashboards no one opens are not observability.
Most observability spend goes to data nobody looks at. Metrics with cardinality nobody queries. Logs with retention nobody needs. Traces nobody samples. The result is a five-figure monthly bill for a setup that does not actually help debug. Our approach starts from the questions engineers actually need to answer at 2am and works backward to the minimum data that answers them. We pick the right backend (Prometheus where Datadog is overkill, Tempo where Jaeger is dying), instrument with OpenTelemetry so the stack stays portable and tune retention ruthlessly. The result is a platform that costs less, helps more and survives vendor pricing changes.
Symptom, not cause
Alerts page on customer-experience symptoms (latency, error rate, SLO burn). Cause-level dashboards exist for diagnosis, not for waking people up.
OpenTelemetry-native
Vendor-agnostic instrumentation. The instrumentation outlives any backend choice; switching observability vendors becomes a refactor, never a rewrite.
Cost as a first-class metric
Per-team, per-service observability spend visible monthly. The team that emits cardinality knows the cost of cardinality.
Why observability is the most-overspent line in DevOps budgets.
Three forces are converging to make 2026 the year most engineering orgs rebuild their observability stack.
Observability bills have grown faster than the engineering they support. CFOs are now demanding ROI conversations that observability buyers were not prepared for.
OTel is now the dominant standard. Vendor-locked instrumentation is becoming a quarterly procurement liability instead of a stable foundation.
Most observability data is unused. Smart sampling, retention tiers and dashboard hygiene routinely reclaim 40-60% of the spend with zero loss of utility.
Observability Stack services we offer.
Each item below is a discrete, measurable workstream we own end-to-end, with senior engineers, real timelinesand the test coverage to back it up.
Metrics platform (Prometheus, Mimir, VictoriaMetrics)
High-cardinality metrics with long retention. Tuned recording rules and alerting that distinguish symptom from cause.
Distributed tracing (Tempo, Jaeger, Grafana Cloud)
OpenTelemetry-native instrumentation across services, queues and managed APIs. Latency root-cause analysis in seconds, not in slack threads.
Log aggregation (Loki, Elasticsearch, ClickHouse)
Structured logging at scale with smart sampling, log-to-metric conversion and adaptive retention.
Real-user monitoring and synthetic checks
Browser RUM and global synthetic probes catch user-facing regressions and SLA violations before customers report them.
SLO-driven alerting
Burn-rate alerts on customer-experience SLOs, not on raw resource thresholds. Pages reflect real user impact, not noise.
Cost optimization
Smart sampling, tiered retention and log-to-metric conversion. Datadog or Splunk bills routinely cut 40-60% with no loss of insight.
We're fluent in your stack.
Vendor-agnostic by design. We pick the right tool for the problem in front of us, not the one our partner discounts apply to.
Real engagements. Real numbers.
Cut Datadog spend 53% with no loss of insight
Smart sampling, log-to-metric conversion and tiered retention. Same MTTR, half the bill, every quarter forever.
Six reasons enterprises run Observability Stack with Infivit.
Built for the 2026 reality of Observability Stack: the actual buyer pain, the actual technical constraints and the actual outcomes that matter, not generic DevOps platitudes.
SLO burn-rate, not CPU thresholds.
Pages fire on customer-experience symptoms, never on noisy resource metrics. Alert volume drops, signal quality goes up, on-call sleep returns.
Datadog or Splunk bill cut 40-60%.
Smart sampling, log-to-metric conversion and tiered retention. Same insight, half the bill, every quarter forever.
Vendor-agnostic instrumentation.
Instrumentation outlives any backend choice. Switching from Datadog to Grafana Cloud to self-hosted becomes a refactor, never a rewrite.
Latency root cause in 6 minutes, not 60.
OpenTelemetry traces across services, queues and managed APIs. The slow trace points at the slow line, no Slack thread required.
90% fewer pages, 100% of the signal.
Aggressive alert tuning, deduplication and grouping. The 200-page night becomes a memory; the 20-page night that reflects real impact stays.
Metrics, logs, traces, one workflow.
Engineers do not switch between 12 tools to debug. Metrics drill into traces, traces link to logs, logs lead back to metrics. One workflow, one mental model.
The questions you were already going to ask.
Got a observability stack problem?
Let's ship the fix.
A 30-minute call with one of our senior engineers, no slideware, no scoping doc. You leave with a concrete view of what the first 30 days look like.