Know what is wrong, and why, before your customers tell you
Many teams collect plenty of data but still learn about problems from customers, because alerts are noisy and dashboards are hard to read. DoneDeploy sets up metrics, logs and traces around what matters to your users, and adds AI that groups alerts, summarises incidents and points engineers at likely causes. Your team spends less time searching and more time fixing, and decisions stay with them.
- Fixed price
- Free first call
- In your own cloud
From design to implementation, and everything after
What goes wrong without it
Alert fatigue
So many alerts fire that the important ones get ignored.
Blind spots
Some services have no metrics or traces at all, so failures there are invisible until users complain.
Slow investigations
Finding the cause of an incident means jumping between tools and reading raw logs by hand.
What we do
- Instrument services with OpenTelemetry for metrics, logs and traces
- Set up Prometheus and Grafana, or work with Datadog and Sentry if you already use them
- Define alerts around user-facing symptoms rather than every internal number
- Use AI to group related alerts and write a plain summary of what is happening
- Use AI to draft incident timelines and post-incident reports for engineers to check
- Monitor AI and LLM services too: latency, errors, token use and cost
What you get
- Dashboards for each service and for the business-critical paths
- Alert rules and routing in code
- On-call and incident runbooks
- A post-incident report template with AI-drafted summaries
From first call to continuous improvement
Free first consultation
We learn what you monitor today and which failures hurt most.
Review
We review existing dashboards, alerts and gaps, with AI helping sift through alert history.
Fixed-price proposal
We agree the services to cover, the tools and the price.
Build
Engineers instrument services, build dashboards and alerts, and add AI summaries.
Improve after launch
We review alert quality and incidents regularly and tune what is noisy or missing.
Datadog
Elasticsearch
KubernetesRelated articles
Observability for LLM ApplicationsWhat to measure in an LLM application: tokens, latency, cost per request, OpenTelemetry traces and the quality signals that show when answers get worse.
Serverless MonitoringLearn the key metrics and tools for serverless monitoring and observability, including AWS CloudWatch and Datadog, to ensure optimal performanceOften done together
Frequently asked
Does AI decide what to do during an incident?
No. AI summarises and suggests likely causes; engineers decide and act.
Do we have to replace our current tools?
No. We can work with Datadog, Sentry or an existing Grafana setup, and use open-source tools where they fit better.
Can you monitor our LLM features?
Yes. We track latency, errors, token use and cost per feature so you can see how AI services behave in production.
Tell us what you run and what you want to change. We reply personally, and the first consultation is free.
Let's talk »