For many products, the first sign of trouble is a customer email, a drop in signups nobody can explain, or a cloud bill twice as large as last month's. Codersarts Monitoring & Observability sets up the dashboards, alerts and tracing that show your team what is happening in your product, so issues are caught in minutes and fixed fast, including every LLM call and agent run.
What Is Monitoring & Observability?
Monitoring tells you that something is wrong, such as rising errors or a slow page. Observability tells you why, by letting you follow requests, logs and traces through the system to the cause. Together they combine metrics, dashboards, alerts, centralized logging, distributed tracing, error tracking and, for AI products, LLM and agent tracing.
This service sets up both as part of Launch & Scale Engineering, built on the tools you already use where possible.
At a Glance
Item | Detail |
|---|---|
Who it is for | Products that hear about problems from customers first, or run AI features they can't debug |
Three pillars | Metrics, logs and traces, plus error tracking and uptime checks |
AI coverage | Prompts, retrievals, tool calls, responses, latency and cost per feature and user |
Starting price | Monitoring Essentials from $2,500, 1 to 2 weeks |
Tools | Grafana, Prometheus, OpenTelemetry, Sentry, Datadog, New Relic, Langfuse, LangSmith |
Alert routing | Slack, email, SMS or on-call tools, with severity levels |
Handover | Alert runbooks, training and a test incident |
Why Do Most Teams Find Out Last?
By the time a customer reports a problem, it has usually been running for hours or days. When the team starts investigating, there's little to go on: scattered logs, no history of how the system behaved, and no way to follow a single request through the system to see where it failed.
AI products make this harder. A bad answer from an agent could come from the prompt, the retrieved data, a tool call or the model itself, and without tracing it's guesswork.
Signs You Need Better Monitoring
You usually hear about outages or bugs from customers first.
When something breaks, it takes hours to work out what happened.
Logs are scattered across servers and services, or not kept at all.
You don't know your real uptime, error rate or response times.
Alerts either don't exist, or fire so often that everyone ignores them.
You can't see which AI feature, user or prompt is driving your LLM costs.
When an AI agent gives a bad answer, there's no way to see what it saw or did.
What Do We Set Up?
Area | The problem it solves | What we implement |
|---|---|---|
Metrics and dashboards | No clear picture of system health | Dashboards for uptime, response times, error rates, traffic, and server and database health |
Alerting | Problems found by customers, or alerts nobody trusts | Meaningful alerts with sensible thresholds, routed to the right people |
Centralized logging | Logs scattered across servers, hard to search | All logs in one searchable place, structured and retained for the right time |
Distributed tracing | No way to follow a request through the system | End-to-end traces across services, databases and external APIs using OpenTelemetry |
Error tracking | Bugs hidden until users complain | Automatic error capture with stack traces, frequency and affected users, using tools like Sentry |
Uptime and synthetic checks | Outages noticed late | External checks that test key pages and user journeys every minute from multiple regions |
LLM and agent tracing | AI failures are impossible to debug | Traces of every prompt, retrieval, tool call, response, latency and cost |
Cost monitoring | Cloud and LLM bills that surprise you | Cost dashboards by service, feature and customer, with budget alerts |
Status page | Customers left guessing during incidents | A public or private status page linked to your monitoring |
What Will Your Team See?
We design dashboards for the people who use them, not just for engineers.
Dashboard | Who it's for | What it shows |
|---|---|---|
Business health | Founders and product leads | Uptime, active users, key user journeys, failed payments or signups |
System performance | Engineering team | Response times, error rates, traffic, server, database and queue health |
AI operations | AI and product teams | LLM latency, error and fallback rates, cost per feature and per user, eval results |
Cloud costs | Founders and finance | Spend by service and environment, trends and budget alerts |
Incidents | On-call engineers | Active alerts, recent deploys, error spikes and links to traces and logs |
Monitoring vs Observability vs AI Observability
Monitoring | Observability | AI observability | |
|---|---|---|---|
Question it answers | Is something wrong? | Why is it wrong? | Why did the model or agent do that? |
Main signals | Metrics, uptime checks, alerts | Logs, distributed traces, error context | Prompt, retrieval, tool call and response traces, eval scores |
Typical tools | Prometheus, Grafana, cloud monitoring | OpenTelemetry, Sentry, centralized logging | Langfuse, LangSmith |
Who uses it | On-call and operations | Engineers debugging | AI and product teams |
Which Metrics Matter Most?
We start with the four golden signals, the metrics most widely used in site reliability engineering, then add business and AI signals on top.
Signal | What it measures | Example alert |
|---|---|---|
Latency | How long requests take, tracked at p95 and p99 | Checkout p95 above 2 seconds for 5 minutes |
Traffic | Requests, users or jobs per second | Sudden drop in signups compared with the same hour last week |
Errors | Share of requests that fail | Error rate above 2% on any key endpoint |
Saturation | How full resources are: CPU, memory, connections, queue depth | Database connections above 85% of the limit |
Business health | Payments, signups, key journeys completed | Payment success rate below normal range |
AI quality and cost | Fallback rate, eval scores, cost per request | LLM spend per hour above budget |
Who Is Monitoring & Observability For?
Founders who want to know about outages before customers do, without reading logs.
Engineering teams that spend hours on every incident because they can't follow a request end to end.
AI product teams that need to explain bad answers and rising LLM costs.
SaaS companies selling to enterprise that are asked about uptime reporting and incident processes.
What Makes an Alert Worth Sending?
Alert fatigue is one of the most common problems we find. An alert should be actionable, urgent and owned. We set severity levels, route each alert to a named owner, link it to a runbook, and remove alerts that fire without needing action. The goal is a small number of alerts each week that all mean something.
How Do We Work?
Step | What happens | Timeline |
|---|---|---|
1. Review | We review your current logging, metrics and alerts, and agree on what matters most to track. | Week 1 |
2. Instrument | We add metrics, structured logging and tracing to your services and AI components. | Weeks 1 to 2 |
3. Build dashboards | We create dashboards for each audience and connect cost and uptime monitoring. | Weeks 2 to 3 |
4. Tune alerts | We set thresholds, routing and escalation, and remove noisy alerts. | Week 3 |
5. Handover | Runbooks for each alert, team training and a test incident to make sure everything works. | Final week |
Already had our Launch Readiness Audit? We use its findings and start at step 2.
What Do You Get?
Live dashboards: business health, system performance, AI operations and costs
Tuned alerts: routed to the right people, with clear severity levels and no alert fatigue
Centralized logs and traces: search any request or AI interaction from start to finish
Error tracking: every error captured, grouped and linked to the code that caused it
Alert runbooks: for each alert, what it means and what to do
Status page: an optional public or internal status page
Training session: so your team knows how to use and extend everything
Example Scenario: Catching a Silent Payment Failure
This is an illustrative scenario based on problems we commonly see. It shows our approach, not the results of a specific client project.
A fintech startup learns about most incidents from support tickets. When a payment integration starts failing silently, it takes most of a day to notice and hours more to find the cause. We would add structured logging, distributed tracing, error tracking and synthetic checks on the payment flow, build dashboards for the team and founders, and route tuned alerts to an on-call owner. Typical targets are detection within minutes, root cause found in under an hour, and most incidents caught by alerts rather than customers.
Pricing
Package | Best for | Includes | Timeline | Price |
|---|---|---|---|---|
Monitoring Essentials | Early-stage products with little or no monitoring | Uptime checks, error tracking, core dashboards, basic alerting | 1 to 2 weeks | From $2,500 |
Full Observability | Growing products with multiple services | Everything in Essentials, plus centralized logging, distributed tracing, cost monitoring, tuned alert routing and runbooks | 3 to 4 weeks | From $6,000 |
AI Observability | AI products, agents and RAG systems | LLM and agent tracing, cost per feature and user, quality and eval dashboards, AI-specific alerts | 2 to 3 weeks | From $4,000 |
Works Well With
Launch Readiness Audit: find the gaps in your visibility, and everything else
AI Hardening: pair AI tracing with fallbacks, guardrails and evals
Deployment Engineering: link deploys to alerts so you see what each release changed
Post-Launch Support: let our team watch the dashboards and respond to alerts for you
Frequently Asked Questions
What's the difference between monitoring and observability?
Monitoring tells you something is wrong. Observability lets you follow requests, logs and traces to find out why, and we set up both.
Which tools do you use?
Grafana, Prometheus, OpenTelemetry, Sentry, Datadog, New Relic, cloud-native monitoring from AWS, Google Cloud and Azure, and Langfuse or LangSmith for AI tracing.
Will monitoring slow down our application?
Not noticeably. Modern instrumentation has very low overhead, and we sample high-volume traces to control both performance and cost.
How much will monitoring tools cost us?
It depends on traffic and tool choice. We can set up a low-cost open-source stack or managed tools, and estimate ongoing costs before we start.
Can you monitor AI agents and LLM features?
Yes. We trace every prompt, retrieval step, tool call and model response, with latency and cost.
We already have some monitoring. Can you improve it?
Yes. Many projects start with an existing setup that has gaps or noisy alerts, and we fill the gaps and clean up what's there.
Who responds to alerts after you've set things up?
Your team, using the runbooks we provide, or our Post-Launch Support team if you'd like us to handle it.
Know What's Happening in Your Product
Tell us how you monitor today and whether your product uses AI. We'll reply with what we'd set up first and a fixed quote.