You Don't Need Datadog: A Guide to Right-Sized Observability
Most teams are paying for enterprise observability they don't need. Here's an honest look at when Datadog is overkill, what right-sized monitoring actually looks like, and how to get complete observability without the five-figure bill.
Let me start with a confession: we genuinely respect Datadog. They built an incredible product that handles observability at a scale most of us can’t even imagine. If you’re running thousands of microservices across dozens of data centers and you have a dedicated platform engineering team, Datadog is probably exactly what you need. This post isn’t for you.
This post is for everyone else. It’s for the startup founder who just got their first real users and knows they need monitoring but doesn’t know where to start. It’s for the small team running a handful of services who set up Datadog because it’s the name everyone knows, and then got their first invoice and nearly fell out of their chair. It’s for the solo developer who just wants to know when their app breaks at 2 AM without signing a contract that costs more than their hosting.
If any of that sounds familiar, keep reading. We’re going to talk honestly about what observability actually costs, what most teams actually need, and why the tool that everyone recommends might not be the right one for you.
The Invoice That Changes Everything
Here’s a story we hear all the time, with minor variations. A team signs up for an enterprise observability platform. The trial is great - everything’s free, the dashboards are beautiful, and the onboarding is smooth. They connect their services, start collecting logs, traces, and metrics, and for a few weeks everything feels amazing. Then the trial ends and the first real invoice arrives.
Suddenly you’re looking at per-host pricing, per-GB ingestion fees, per-seat license costs, retention add-ons, and overage charges for that one time your app had a logging loop during an incident (which is, ironically, exactly when you need your logs the most). The total is somewhere between “that’s more than our database” and “that’s more than our junior developer.” And the worst part is, you can’t easily turn it off because now all your dashboards, alerts, and runbooks are built on top of it.
We’ve talked to teams paying $500, $1,000, even $2,000+ per month for observability - teams of three or four people running a single product. That’s not right-sized. That’s enterprise pricing being applied to non-enterprise teams, and it creates a genuinely bad situation where people either overpay for monitoring they don’t fully use, or they skimp on monitoring to save money and end up flying blind.
The Real Cost Comparison
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What “Right-Sized” Actually Means
Right-sized observability isn’t about getting fewer features. It’s about getting the features you actually use at a price that makes sense for your scale. Let’s think about what most small-to-medium teams genuinely need on a day-to-day basis.
You need centralized logging. When something breaks, you need to be able to search your logs quickly and find out what happened. You don’t need petabytes of log storage or custom query languages that take a week to learn. You need a search box, some filters, and logs that show up in real time. That covers 90% of debugging sessions for most teams.
You need uptime awareness. You need to know when your app goes down before your customers tell you. Whether that’s a simple HTTP check every few minutes or a heartbeat ping from your background jobs, you need something watching your services and alerting you when they stop responding.
You need alerts that actually reach you. An alert that sits in an email inbox nobody checks is the same as no alert at all. You need notifications that show up where your team already works - Slack, Discord, wherever. And you need to be able to configure them without a degree in query language syntax.
You probably need a status page. Your customers want to know if you’re aware of an issue, and a public status page is the simplest way to communicate that. It’s also a signal of maturity that builds trust, even for small products.
And increasingly, you need some form of tracing. If you’re running more than one service (and most modern applications are, even if it’s just your API plus a background worker), being able to follow a request across service boundaries saves hours of debugging time.
That’s it. That’s the list for most teams. You don’t need custom metrics pipelines. You don’t need AI-powered anomaly detection. You don’t need infrastructure monitoring for servers you don’t manage (most of us are on managed platforms now anyway). You need the fundamentals done well, at a price that doesn’t make you wince every month.
The Hidden Costs Nobody Talks About
The sticker price of enterprise observability tools is only part of the story. There are hidden costs that are harder to quantify but just as real.
Complexity cost. Enterprise tools are built for enterprise use cases, which means they come with enterprise complexity. Configuration that should take minutes takes hours. Dashboards that should be obvious require reading documentation. New team members need training before they can use the monitoring system. All of this is time your team could be spending building your product.
Cognitive overhead. When your observability platform has 47 different product SKUs and your pricing page needs a calculator, something has gone wrong. You end up spending mental energy worrying about costs instead of focusing on reliability. “Should I add this log statement? Will it push us over our ingestion limit?” That’s not a question developers should be asking. You should log what you need to log and not think about it.
Lock-in cost. Once you’ve built dashboards, alert rules, and team workflows around a specific platform, switching is painful. Enterprise tools know this, and their pricing reflects it. The longer you stay, the harder it is to leave, and the less incentive they have to keep your bill reasonable.
Over-engineering cost. When you have access to every possible monitoring tool, there’s a temptation to use all of them. You end up building elaborate dashboards nobody looks at, creating alert rules that fire so often everyone ignores them, and collecting data you never query. More isn’t always better - sometimes more is just more noise.
What We Built Instead
When we built Loggy, we made some deliberate choices that go against the grain of how most observability companies work, and we think those choices matter.
Flat-rate pricing. No per-seat fees, no per-GB ingestion charges, no overage penalties. Our free tier gives you one project with a week of retention at no cost. Pro is $10 per month. Team is $50 per month. That’s the whole pricing page. We don’t charge you more when your app has a bad day and generates a spike in error logs - that’s exactly when you need your logging tool the most, and punishing you for it with overage charges is backwards.
Five-minute setup. We have a CLI that detects your framework, installs the SDK, creates your project, and saves your token to your environment file. Run npx @loggydev/loggy-cli init and you’re done. No agents to install on your servers, no configuration files to write, no YAML to debug. If you prefer to do it manually, the SDK takes about three lines of code:
import { CreateLoggy } from '@loggydev/loggy-node';
const loggy = CreateLoggy({
identifier: 'my-app',
accessToken: 'your-project-token',
});
loggy.info('Hello from Loggy!');Everything in one place. Logging, heartbeat monitoring, uptime checks, alerting, status pages, feature flags, performance metrics, and distributed tracing. You don’t need to stitch together five different tools and pay five different bills. Everything works together out of the box, and the data is correlated - click on a trace and see the related logs, check a heartbeat and see its history, set up an alert and get notified in Slack.
Multi-language SDKs. We have first-class SDKs for Node.js, Python, and Go, each built to feel native in their respective language. The Node.js SDK has Express middleware for automatic tracing. The Python SDK has Flask and FastAPI middleware. The Go SDK has HTTP middleware with context propagation. And if you’re using a language we don’t have an SDK for, our HTTP API is simple enough to call from anywhere.
An Honest Comparison
We’re not going to pretend that Loggy does everything an enterprise platform does. It doesn’t, and that’s by design. Here’s an honest look at the trade-offs.
Where enterprise tools win: If you need to monitor infrastructure (CPU, memory, disk usage on your own servers), you’ll need something else - Loggy focuses on application-level observability. If you need to ingest terabytes of logs per day, enterprise tools are built for that scale. If you need compliance certifications like SOC 2 Type II or HIPAA BAAs, larger vendors have those. And if you need to support hundreds of engineers with role-based access controls and audit logs, enterprise tools are designed for that level of organizational complexity.
Where Loggy wins: Setup time (minutes vs. days). Pricing transparency (flat rate vs. calculator). Cognitive simplicity (one dashboard vs. dozens of sub-products). Time to value (your first log appears in seconds, not after a week of configuration). And honestly, for most small-to-medium teams, the features you actually use day-to-day are just as good - search your logs, get alerted when things break, trace requests across services, show your customers a status page.
The question isn’t “which tool is better?” - it’s “which tool is right for your team, right now?” And for most teams under 20 people, the answer is probably something simpler and cheaper than what you’re currently using or considering.
The “But What If We Outgrow It?” Question
This is the most common objection we hear, and it’s a fair one. “Sure, Loggy is great now, but what if we grow and need to migrate later? Isn’t it better to just start with the enterprise tool?”
Here’s our honest take: maybe, but probably not. Starting with an enterprise tool “just in case” is like renting a warehouse for your two-person startup because you might need the space someday. You’re paying for capacity you don’t use, dealing with complexity you don’t need, and optimizing for a future that might never arrive - or might look completely different than you expect.
If your team grows to 50 people and you need infrastructure monitoring, custom metrics pipelines, and SOC 2 compliance, then yes, you’ll probably want a bigger platform. And that migration won’t be fun, but it also won’t be the end of the world. The alerting rules and workflows you’ve built will need to be rebuilt regardless of which tool you started with, because your needs at 50 people are fundamentally different from your needs at 5.
In the meantime, you’ve saved thousands of dollars and hundreds of hours of engineering time that you spent building your product instead of configuring your monitoring stack. That’s not a bad trade.
Getting Started
If any of this resonated with you, here’s what we’d suggest. Sign up for a free Loggy account - it takes about 30 seconds. Set up logging in your main project using the CLI or the SDK directly. Add a heartbeat for your most important background job. Create a status page for your customers. Set up a Slack or Discord notification channel so alerts actually reach you.
The whole thing takes less than 15 minutes, and you’ll have better observability than most teams who are paying 100x what you are. That’s not a knock on those teams - it’s just a reflection of the fact that most of what they’re paying for, they’re not using.
npx @loggydev/loggy-cli initWe’re not trying to be everything to everyone. We’re trying to be exactly the right thing for teams who want solid, simple observability without the enterprise price tag. If that’s you, we’d love to have you.
And if you try Loggy and decide it’s not right for you, that’s totally fine too. We’d rather you use the right tool than use our tool for the wrong reasons. But we have a feeling that once you see how simple observability can be, you won’t want to go back to the complicated version.