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Monitoring & Observability

Monitoring and observability services built for the question you ask during an incident

BinaryBrill delivers monitoring and observability services that answer whether the system is doing its job, not just whether a machine is busy. In-house senior engineers build this around service level objectives, distributed tracing and structured logging, so an incident points at which release, which dependency and which users — the questions an infrastructure dashboard was never built to answer.

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A senior engineer replies within 24 hours — not a sales rep.

Why the dashboards are green during a bad incident

CPU and memory look fine while users are getting errors

Every infrastructure graph is within normal range, and the support queue is filling up anyway. Server health and user-facing behaviour are different things, and monitoring built only around the former tells you the machines are comfortable while the product is failing.

Nobody can say which release caused it

An error rate climbs and three deploys happened this week, across four services with dependencies on each other. Without tracing that connects a slow or failing request to the specific hop and the specific deployment behind it, diagnosis becomes a guessing exercise run under pressure.

Alerts fire constantly and nobody trusts them anymore

Thresholds were set once, on infrastructure metrics, and never revisited. Most alerts turn out to be noise, so they get muted or ignored — meaning the one alert that mattered arrived at 3am and nobody looked at it until a customer called.

Logs exist, but nobody can connect one to the request it belongs to

Each service logs independently, with its own format and no shared identifier. Reconstructing what happened to a single user's request across five services means grepping five sets of logs and guessing at the timeline that connects them.

How we build monitoring and observability services that hold up in an incident

Observability earns its cost the moment something goes wrong, so we design it around the question an incident actually asks: which release, which dependency, which users. Everything else is secondary to that.

Service level objectives on what users actually experience

Alerts fire on error budget burn against user-facing behaviour — successful requests, acceptable latency — rather than on raw CPU or memory. A machine can be busy and the product can be fine; the alert should reflect the second thing.

Tracing that follows a request across every service it touches

Distributed tracing shows exactly which hop in a multi-service request added the latency or threw the error, instead of leaving the team to guess which of several services is at fault during an active incident.

Logs, traces and deployments linked by a shared identifier

Structured logging with correlation IDs ties a single log line back to the trace it came from and the deployment it happened under — so "what changed right before this started" has a direct answer instead of a guess based on the calendar.

A runbook and an owner behind every alert

Every alert that can fire has a runbook attached and sits inside an on-call rotation with a defined escalation path. An alert with no documented response is a notification, not a monitoring system.

What this covers

Pick the piece you need, or bring us the problem and we'll tell you which applies.

Service Level Objectives & Error Budgets

Defining what reliable actually means for your product, in terms users would recognise, and alerting against how fast that budget is burning rather than against a raw infrastructure threshold.

Service level objectives on user-facing behaviour, with alerts on error budget burn instead of raw CPU
Error budgets set jointly with engineering and the business, not imposed as a generic target
Budget burn-rate alerting tuned to fire early enough to act, not after the objective is already missed
A regular review of whether the current objectives still match what the product needs

Distributed Tracing Implementation

Following a single request across every service it touches, so a slow or failing hop is identified directly instead of investigated by elimination during an active incident.

Distributed tracing across services, so a slow request identifies the hop that caused it
Instrumentation added with OpenTelemetry so tracing isn't locked to a single vendor
Sampling strategies that keep tracing affordable at volume without losing the incidents that matter
Trace data linked directly to logs and deployment history for faster root-cause analysis

Structured Logging & Correlation

Replacing free-text logs scattered across services with a structured format and shared correlation IDs, so one log line can be traced to the request, the trace and the deployment it came from.

Structured logging with correlation IDs linking a log line to the trace and the deployment it belongs to
Consistent log schema enforced across services, not left to each team's own convention
Log retention and search set up so an incident investigation doesn't mean grepping raw files
PII and sensitive fields redacted at the point of logging, not after the fact

Alerting, On-Call & Runbook Design

Turning a wall of noisy alerts into a small number that fire for genuine reasons, each with a documented response and an owner during the hours it matters.

Runbooks attached to every alert, and an on-call rotation with a defined escalation path
Alert thresholds tuned against real incident history rather than left on default values
Noisy or low-value alerts retired so on-call attention goes to signals that warrant it
Escalation paths tested during a drill, not just documented and assumed to work

Application & Cloud Monitoring Setup

Standing up the underlying monitoring stack — metrics collection, dashboards and infrastructure visibility — as the foundation the SLOs, tracing and alerting above are built on.

Metrics collection and dashboards covering both infrastructure and application-level signals
Dashboards built for the audience reading them — an on-call engineer's view differs from a leadership view
Cost-aware retention and sampling so observability spend stays proportionate to what it protects
Integration with your existing cloud provider's native monitoring where it already covers the basics

The stack we build on

Chosen to fit the problem — not because it's what we used last time.

Metrics & dashboards

Prometheus
Grafana
Datadog
Amazon CloudWatch
Azure Monitor
Thanos

Tracing & APM

OpenTelemetry
Jaeger
AWS X-Ray
Datadog APM
Honeycomb

Logging & error tracking

ELK Stack
Loki
Sentry
Fluent Bit
Splunk

Alerting & incident response

PagerDuty
Opsgenie
Alertmanager
Statuspage
Slack incident workflows

How we'll work together

Every stage ends with something in your hands — not a status update.

01
Map what users actually depend on
We identify the user-facing behaviours that matter — the checkout completing, the search returning results, the report generating — and audit what's currently monitored against what actually predicts a bad experience.
You get: A service inventory mapped to user-facing behaviours, and an audit of current monitoring gaps against them.
02
Define service level objectives that mean something
SLOs are set against the behaviours identified in step one, with error budgets your team agrees are the right trade-off between reliability and shipping speed — not arbitrary round numbers.
You get: A documented set of SLOs and error budgets per service, agreed with both engineering and the business.
03
Instrument tracing and structured logging
Distributed tracing and correlation IDs get wired through the request path across services, so a slow or failing request can be followed end to end rather than reconstructed from separate, disconnected logs.
You get: Tracing instrumented across the request path, and structured logging with correlation IDs deployed to production.
04
Attach runbooks and stand up on-call
Every alert gets a runbook and a place in an on-call rotation with a defined escalation path. We sit with your team through the first real incidents using the new tooling, then step back.
You get: Alerting wired to SLOs, a runbook per alert, an on-call rotation, and dashboards your team can read without translation.

Where we've applied this

SaaS platforms

SLOs per customer-facing feature, so a degraded integration for one tenant is visible before it escalates into a churn conversation.

Financial services

Tracing across payment and settlement services so a delayed transaction can be attributed to the exact dependency responsible, with an audit trail intact.

Retail & e-commerce

Checkout and search latency treated as the primary signal during a trading peak, ahead of infrastructure metrics that stay comfortable while conversions drop.

Healthcare

Structured logging with correlation IDs supporting the audit trail a clinical system needs, alongside the reliability monitoring itself.

Logistics

Observability across tracking and dispatch services, where a delayed message queue shows up as a customer-visible delay before any server looks unhealthy.

Media & streaming

Playback and buffering behaviour monitored as the primary SLO, since server load can look entirely normal while viewers experience stalls.

Questions buyers ask us

Is our system too small to need proper observability yet?

If you're running a single service with a handful of users and the person on call can already reason about the whole system in their head, basic infrastructure monitoring may genuinely be enough for now. The moment you have more than one service calling another, or more than one engineer who needs to understand an incident without the original author present, tracing and structured logging start paying for themselves quickly.

Should we build this on open-source tooling or buy a commercial platform?

It depends on your team's appetite for operating the tooling itself. Open-source stacks like Prometheus, Grafana and the ELK stack cost engineering time to run but avoid per-host or per-GB pricing that scales with your traffic. Commercial platforms like Datadog trade that operational burden for a subscription cost that can grow substantially at volume. We'll size both options against your actual traffic and team capacity rather than defaulting to either.

What drives the cost of an observability implementation, and how long does it take?

Cost follows the number of services needing instrumentation, how much tracing needs adding to existing code versus configuring at the infrastructure layer, and the retention and volume of logs and traces you need to keep. Standing up SLOs and dashboards for a handful of services typically takes a few weeks; full tracing instrumentation across a larger service estate is more of a phased rollout. We scope this properly rather than quoting blind.

Do our logs and traces leave our own infrastructure?

Only if you choose a commercial SaaS platform that requires it, and we'll tell you exactly what that involves before you commit. With an open-source stack deployed in your own cloud accounts, none of your telemetry data leaves your infrastructure. Either way, we redact PII and sensitive fields at the point of logging, not as an afterthought.

When is investing in observability not the priority right now?

If your actual problem is that releases are slow and risky rather than that incidents are hard to diagnose, better monitoring won't fix that — a CI/CD and deployment problem needs a CI/CD fix. And if you're pre-launch with no real traffic yet, comprehensive tracing and SLOs are premature; basic error tracking and infrastructure monitoring is enough until you have real user behaviour to observe.

How is this different from your CI/CD and infrastructure-as-code work?

Monitoring and observability is about a system that's already running — is it doing its job, and if not, why. CI/CD is about how a change gets from a merge to that running system, and infrastructure as code is about how the environment it runs in gets defined. They feed into each other — correlation IDs linking a log line to the deployment that caused it depend on your pipeline recording that information — but observability is specifically the after-the-deploy question, not the deploy process itself.

Will this replace the on-call process we already have, or work with it?

It works with it. We build on your existing on-call rotation and escalation tooling rather than replacing it, adding the runbooks, alert tuning and tracing that make the process your team already runs more effective. Where there's no on-call process yet, we'll help design one, but the goal is always a system your team can operate, not a dependency on us.

Who sets this up, and where are they based?

Our own in-house engineers in Sahibzada Ajit Singh Nagar, Punjab — 45+ of them, with over a decade of combined delivery experience, delivering for clients in 15+ countries. Nothing is subcontracted. You own the dashboards, the configuration and the instrumentation code from day one, and a senior engineer replies to any question within 24 hours.

Tell us what your last bad incident actually looked like

Send us a short account of how you found out something was wrong last time, and how long diagnosis took. A senior engineer replies within 24 hours with a read on where observability would have shortened that.

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