Cloud & DevOps
Observability
We set up logs, metrics, traces and alerts so you can see how your systems behave in production and fix problems before users report them.
See it before users do
Logs · Metrics · TracesIs this for you?
You’ll get value from this if…
- You find out about outages from customers
- Debugging production means guessing and redeploying
- Alerts are so noisy the team ignores them
- You can't tell which part of the system is slow
Benefits
What changes for your business
Know before users do
Alerts fire on real problems, not after a customer complains.
Faster fixes
Follow a single request across services to find the cause.
Calmer on-call
Fewer, better alerts and a runbook for each one.
What you get
What we deliver
Logs, metrics and traces
Collected in one place and linked, so one request can be followed end to end.
Dashboards
The few views that show whether each service is healthy.
Actionable alerts
Alerts tied to user impact, routed to the right people.
Incident runbooks
What to check and do when each alert fires.
How it works
From first call to running in production
- 01
Instrument
Add logging, metrics and tracing to the services that matter most.
- 02
Define health
Agree what 'working' means for each service.
- 03
Alert
Set alerts on those definitions and tune out the noise.
- 04
Practise
Walk through incidents so the team is ready for real ones.
Example applications
What this looks like in practice
Typical applications of this service. Illustrative, not client case studies.
- 01Monitoring for APIs, apps and background jobs
- 02Tracing requests across microservices
- 03Monitoring AI features for cost, latency and answer quality
- 04Uptime and error dashboards for leadership
Technology
Tools we work with
- OpenTelemetry
- Prometheus
- Grafana
- Cloud monitoring
- Log aggregation
Why Bitfumes
Built by engineers who ship
Senior team, no hand-offs
The engineers on your first call are the ones who build your product.
10+ years of engineering leadership
Led by Sarthak Shrivastava, Docker Captain, AWS Certified Solutions Architect, AWS Certified Developer.
We teach this for a living
156K+ developers learn from our founder on YouTube, and 100K+ on Udemy.
Production, not prototypes
Tests, monitoring and handover are part of every build, not extras.
How to start
From first conversation to production
- 1
Talk to us
Tell us the problem. We come back with a straight view on whether it is worth building.
Get in touch - 2
Build
A senior team embeds with yours and ships in short cycles, with a demo every week.
- 3
Run and improve
We hand over cleanly, or stay on to monitor, support and extend what we built.
FAQs
Common questions
What is observability?
The ability to understand what a system is doing from the data it emits, such as logs, metrics and traces, so you can find the cause of a problem without guessing.
Will it slow our systems down?
Instrumentation adds very little overhead when set up properly, and we sample where volume would make it costly.
Do we need new tools?
Not always. We start with what you already have and add tools only where there is a gap.
Can you monitor AI and LLM features?
Yes. We track latency, cost and failure rates for model calls, and log inputs and outputs where your data policy allows, so quality issues can be traced.
What should we measure first?
The few signals that reflect what users experience: errors, latency and whether key journeys succeed.
Insights
Related reading
- EngineeringAI in production: the checklist most teams skip.Getting a demo working is the easy 20%. Here's what separates a prototype from something you can trust running unattended in front of customers.Read
- EngineeringBatch your AI calls, halve the bill.A for loop calling the model once per ticket pays full price and competes with live traffic for the same rate limit. The Batch API processes up to 100,000 requests at once, at half the cost — for exactly the work that was never going to need an answer in three seconds.Read
Next step
Want this built into your business, not just explained?
Tell us the problem and we'll come back within one business day with a straight view on whether AI is worth it for you, and what it would take to build.
More in Cloud & DevOps