Tink vs Grafana + Prometheus: Why Most Small Teams Don't Need a Full Observability Stack

Grafana and Prometheus are the gold standard for production monitoring at scale. Search any SRE conference talk from the last five years and you'll find them mentioned reverently alongside distributed tracing, service meshes, and error budgets. They're genuinely powerful. They're also almost always the wrong choice for small teams.

Here's why — and what to use instead.

The Grafana + Prometheus Stack in Practice

On paper, the stack is elegant. Prometheus scrapes metrics from your infrastructure using exporters (node_exporter for system metrics, various app-specific exporters for everything else). It stores those metrics as time-series data. Grafana queries Prometheus and renders dashboards. Alertmanager handles routing alerts to email, Slack, PagerDuty, or wherever you want them.

In practice, getting this running in production looks like:

Stand up a Prometheus server with enough RAM for your metrics cardinality (low cardinality: fine; high cardinality: expensive fast)

Install and configure node_exporter on every machine you want to monitor

Configure Prometheus scrape targets for each exporter

Install and configure Grafana

Either build dashboards from scratch or import community-maintained ones and inevitably spend hours making them actually work for your setup

Write alerting rules in PromQL — a powerful query language with a steep learning curve

Configure Alertmanager for alert routing, grouping, and silencing

Set up authentication on both Grafana and Prometheus (they ship with essentially none)

Plan for Prometheus storage: default retention is 15 days, and you'll want to think about long-term storage (Thanos, Cortex, or Grafana Cloud) before you need it

Keep everything updated as security patches land

For a dedicated DevOps team, this is table stakes. For a freelancer with three DigitalOcean droplets, this is a multi-day project that keeps growing.

The PromQL Problem

Prometheus's query language is PromQL. It's remarkably expressive — you can compute per-second rates, aggregate across label dimensions, build recording rules for expensive queries, and write alerting expressions that capture subtle failure modes.

You need to understand it to use it.

A simple alert like "disk usage is above 85%" looks like this in PromQL:

(1 - (node_filesystem_avail_bytes / node_filesystem_size_bytes)) * 100 > 85

That's actually one of the simpler ones. Writing accurate alerting rules for sustained high CPU, predicting when a disk will fill based on trend, or detecting that a service keeps restarting takes real PromQL fluency.

Most small team developers — even experienced ones — don't have that fluency and don't particularly want to develop it. They want to know when something is broken, why it's broken, and what to do about it. PromQL is a tool for answering nuanced metric questions. It doesn't answer those questions.

What "Free" Actually Costs

Grafana and Prometheus are open-source software. Running them is not.

You need a server to host Prometheus. Depending on your metrics volume, it might need 2-4 GB of RAM just for the time-series database. You need a server for Grafana (or use the same one, but then resource pressure becomes a concern). You need an Alertmanager instance. That's potentially $30-60/month in cloud infrastructure before you've monitored a single production server.

Then there's time. Setting up the initial stack takes a senior engineer 4-8 hours if they've done it before. Onboarding additional team members, building useful dashboards, tuning alert thresholds to reduce false positives — add another 10-20 hours. Ongoing maintenance (upgrades, new machines, configuration drift) runs 1-4 hours per month indefinitely.

At $100/hour of engineering time, the "free" monitoring stack costs $1,500-3,000 to stand up and $100-400/month to maintain. That's before the opportunity cost of your engineers not building product.

What Small Teams Actually Need

Most small teams don't need an observability platform. They need answers to these questions:

Is everything running right now?

If something is wrong, what is it and why?

What do I do to fix it?

Will I know before my users do?

Grafana+Prometheus can answer the first question with beautiful dashboards. The other three require human interpretation, documentation, and expertise.

Tink is built specifically for those four questions. When disk is running low, it doesn't show you a chart — it sends you a message: "Disk on prod-3 is at 88% and filling at about 1.2 GB/day. At this rate it fills in roughly 6 days. The largest directories are /var/log/nginx (12 GB) and /var/backups (8 GB). Here's how to clean them up."

That's the difference. Not "more metrics" vs "fewer metrics" — it's "charts you have to interpret" vs "explanations you can act on."

When Grafana + Prometheus IS the Right Choice

To be fair, Grafana+Prometheus excels in specific situations:

You have application-level metrics you own. If your team instruments your code with Prometheus client libraries and exports custom business metrics (request rate, checkout conversion, queue depth, P99 latency), Grafana+Prometheus is the natural visualization layer. You've already paid the expertise cost; the dashboards are worth it.

You're running a large fleet. At 50+ servers, the economics shift. A centralized metrics platform amortizes setup costs across more machines. You probably also have a dedicated platform team at this scale.

You need long-term metric retention. Tink focuses on recent data and trends. If you need to correlate today's performance with metrics from six months ago, a purpose-built metrics store wins.

You have Kubernetes. The Prometheus operator and kube-state-metrics are purpose-built for this environment. Kubernetes monitoring without Prometheus is painful.

None of these apply to most small teams. If you're a freelancer, a startup with fewer than 10 engineers, or a small company that ended up with three VPS instances because you needed them — you're paying the full Grafana+Prometheus setup tax for a fraction of its intended use case.

The Actual Alternative

Install Tink on a server in 30 seconds:

curl -fsSL https://tink.bot/install | sh
tink onboard

That's it. No Prometheus server to configure. No PromQL to learn. No dashboards to build. Tink starts scanning your system immediately — CPU, memory, disk, running services, TLS certificates, system logs, open ports — and sends you alerts when something needs attention.

When it alerts, it explains in plain English what's wrong and what to do. When you have questions, you ask them in Telegram and Tink responds like a knowledgeable colleague. When you want a weekly summary, it shows up in your Telegram on Monday morning.

The monitoring stack Grafana+Prometheus requires a DevOps engineer to configure and maintain. Tink requires a terminal and 30 seconds.

The Decision Framework

Here's how to decide:

Choose Grafana + Prometheus if:

You have a dedicated SRE or platform team

You need custom application-level metrics

You're running Kubernetes

You have 50+ servers

You already know PromQL

Choose Tink if:

You're a freelancer or small team without dedicated DevOps

You need monitoring running today, not after a two-week setup project

You want explanations and fix guidance, not dashboards

You have fewer than 20-30 servers

You prefer talking to your monitoring tool over querying it

You can also use both. Grafana+Prometheus for custom application metrics and long-term trends; Tink for real-time infrastructure health, plain-English diagnosis, and alert management. Many teams do exactly this.

But if you're currently not monitoring your servers because setting up Grafana+Prometheus felt like too much work — Tink removes that barrier entirely. Free for your first server, $9/month per machine after that. No PromQL required.

Compare Tink and Grafana+Prometheus side by side →