SLOs for startups: simple promises your team can keep

Why most startups make reliability too big

Small teams often copy reliability habits from big companies. That sounds sensible until they spend more time naming metrics than fixing the product. A startup with six engineers does not need 50 promises, three dashboards per service, and a weekly meeting about numbers nobody reads.

This usually starts with good intent. Someone wants to take uptime, support tickets, and customer trust seriously. Then the list grows fast: API latency, background jobs, email delivery, admin tools, internal scripts, edge cases, and every screen in the product. Before long, the team tracks everything and protects nothing.

Too many targets create noise. Alerts fire for parts of the product users barely touch. People argue about whether a graph moved from 99.7% to 99.6%. Someone updates a spreadsheet. Meanwhile, nobody fixes the login bug that locked out five customers this morning.

Users judge reliability by a few painful moments. Can they sign in? Can they pay? Can they finish the main task they came to do? If those flows work, most users feel the product is dependable. If those flows fail, polished reporting will not save that impression.

That is why SLOs should stay small and plain in an early-stage company. Pick a few promises that match the actual product experience. For many teams, that means login, checkout, and one core workflow such as creating a project, sending a message, or publishing an update. Those are the promises people remember.

A short list also helps the team move faster. Engineers know what to watch. Support knows what counts as urgent. Founders can decide where to spend time without sorting through weak signals. The work gets less glamorous, but it gets more useful.

A good SLO program at this stage is almost boring. It says: these few things must work, we will measure them, and we will fix them first when they slip. That is enough to build trust.

What an SLO should actually measure

An SLO is a promise your team believes it can keep most of the time. In plain language, it answers one question: when a person tries to do something important in your product, does it work well enough and fast enough?

That is why service level objectives should describe user results, not just system health. Users do not care if a server stayed up all day if they still could not log in, pay, or save their work.

Internal metrics still matter. CPU load, database lag, queue depth, and error counts help your team find the cause of a problem. They are diagnostic numbers. An SLO should sit above them and describe the experience the user actually gets.

A useful SLO often sounds like this:

• Users can log in successfully within a few seconds.
• Buyers can complete checkout and get confirmation.
• Staff can create, save, and reopen a record without errors.

People can feel those promises. When they fail, support tickets show up fast.

Uptime alone misses a lot of real product pain. A site can be "up" while the login form rejects valid passwords, the payment step spins forever, or the final submit button does nothing after a user filled in ten fields. Your monitoring may stay green, but the product is still broken where it counts.

This matters even more for startups. Small teams do not have time to maintain a giant reliability program full of numbers nobody uses. They need a short set of promises tied to the actions that bring in money, keep users active, or let staff do their daily work.

A practical rule helps: measure the full path a person takes to finish the task. For login, that may include the auth service, session creation, and the redirect into the app. For checkout, include payment approval and final confirmation. For a core workflow, include the save step, not just page load.

If the action fails, the SLO should show it. If the action feels slow, the SLO should show that too. That keeps the team focused on what users actually came to do.

Start with the flows users feel first

Users notice broken basics long before they notice a missed internal report. If people cannot log in, sign up, pay, search, or save their work, the product feels unreliable right away. That is where your first SLOs should begin.

Start with the few actions closest to revenue or daily use: login, signup, checkout, search, and save or submit. You do not need all of them. Pick the flows that stop revenue, block activation, or ruin a normal session when they fail.

For an ecommerce product, checkout usually comes first. For a SaaS app, login and save usually matter more than a settings page or a rare export screen.

Support tickets give you one ranking signal. Product analytics give you another. Read the tickets people send when they get stuck, then compare them with drop-off points, retry rates, and failed actions in your analytics. If both sources point to the same step, you probably found a flow worth protecting.

A small example makes this obvious. Say a team keeps getting complaints about password reset emails not arriving, and analytics show many new users fail to complete their first login. That deserves attention before anyone spends time on an admin report that only two staff members use each month.

Skip edge cases at first. A niche import tool, a low-traffic settings page, or a one-time migration screen may matter later, but they should not drive your first reliability promises. Early SLOs work best when they cover a handful of moments users hit every day.

If you are unsure where to start, ask a blunt question: what failure would make a user leave, ask for help, or stop paying today? Put those flows at the top. Leave the rest for later.

How to choose your first three SLOs

Pick your first three SLOs from the places where users feel failure fast. Login is an obvious one. Checkout is another. The third should be the main action that makes your product useful, like creating a booking, sending an invoice, or publishing a change. If users notice the break before your team does, that flow deserves one of your first promises.

Write each SLO in user language. Skip phrases like "auth service availability" or "payment subsystem health." Say what the user is trying to do: "sign in," "pay," or "finish setup." That wording keeps the whole team honest. It also stops you from chasing neat internal metrics that customers never see.

Then pick one measure for each flow and one time window. Start simple. For login, success rate is often enough. For a core workflow, completion time may matter more. A weekly window works well for young teams because it reacts fast without turning one bad hour into a crisis. A 30-day window can work if your traffic is low.

Set targets your team can actually hold. New teams often reach for 99.99 because it sounds serious. That usually turns into noise and excuses. If you have one engineer on support and a small budget, choose a target that fits that reality. A slightly lower target that you meet is more useful than a perfect number you miss every week.

Add one alert for each SLO, and keep the rule blunt. If checkout success drops clearly below target for long enough to hurt sales, alert the team. Do not build a maze of thresholds, ratios, and edge cases. If nobody can explain the alert in one sentence, it is too complex.

After a few weeks, look at the numbers and ask a plain question: did this SLO help us see real user pain? If the target never moves, it may be too loose. If it fails all the time, lower the target or fix the product before you promise more. SLOs should feel like promises a small team can keep, not a paper exercise.

Starter SLOs for login, checkout, and core work

A small team does not need ten reliability promises. Three is enough if they cover the moments users notice right away: signing in, paying, and doing the main task your product exists to help with.

Each SLO should fit in one sentence. If a support rep or engineer cannot explain it without opening a dashboard, it is too messy.

• Login SLO: "99.5% of sign-in attempts succeed each week, and 95% finish in under 2 seconds."
• Checkout SLO: "99% of payment flows that reach the payment step finish successfully within 60 seconds, excluding user card declines."
• Core workflow SLO: "99.3% of save, submit, or publish actions succeed, and 95% complete in under 3 seconds."

These are rough targets, not sacred numbers. If your product is still young, start with something you can measure cleanly. Tighten it later, after a month or two of real traffic.

The wording matters. "App uptime" sounds nice, but users do not buy uptime. They feel failed logins, stuck payments, and drafts that never save. A good SLO names the action, the success condition, and the time window.

Pick one core action only. For a SaaS product, that might be "publish report." For a marketplace, it might be "send booking request." For an internal tool, it might be "submit form." If you try to cover every workflow at once, the metric turns into noise.

A small team can review these three numbers once a week: how many attempts happened, how many failed, and how long successful ones took. That is enough to spot real trouble. If login slips, people cannot get in. If checkout slips, revenue drops. If the main task slips, trust fades fast.

A simple example from a small product team

A five-person SaaS team runs a product that helps finance staff log in, manage a billing page, and export monthly reports. At first, the team watched everything they could measure. They had charts for CPU, memory, error rate, queue depth, cache hit rate, and more.

The problem was simple: users never complained about queue depth. They complained when they could not sign in, when a payment vanished, or when an export sat there forever.

So the team stopped treating 20 metrics as equally important. They picked three flows that users feel right away: login success each month, completed payments on the billing page, and report exports that finish within a set time.

They also defined failure in plain language. A failed login meant the user entered valid credentials but still could not get into the app. A dropped payment meant someone submitted payment details and never reached a clear success state. A stuck export meant the user asked for a report and did not get the file within two minutes.

That short list changed daily decisions.

One Monday, failed logins jumped after a session change. The team ignored a separate warning about rising database load and fixed login first. That was the right call. If people could not enter the product, nothing else mattered.

A week later, dropped payments rose after a billing page update. Support saw the problem before engineering did. Because payment completion was already on the list, the team rolled back the page change the same day instead of debating whether the issue was big enough.

Then exports started getting stuck for large accounts. The team traced it to one worker that kept retrying oversized jobs. They capped export size, split large jobs into smaller batches, and added a status message so users could see progress instead of guessing.

A short SLO list did not solve every reliability problem. It gave the team a filter. They still watched technical metrics, but those metrics became clues, not goals. The goal stayed the same: people can log in, pay, and finish their work without friction.

Mistakes that make SLOs useless

Most teams do not ruin SLOs by aiming too low. They ruin them by making promises their team cannot keep and by measuring the wrong things.

A common mistake is copying enterprise targets. If your product runs in one region, with a small team and a modest budget, a 99.99% target for every service is fantasy. You do not need fantasy. You need a promise your current stack, team, and support hours can meet most weeks.

Another mistake is watching servers instead of users. A dashboard can show healthy CPU, memory, and uptime while customers still fail to log in, complete checkout, or finish the one task they came to do. If users click "Pay" and the order does not go through, your service is not healthy in any way that matters.

Too many SLOs break attention fast. Teams want to cover everything at once. Then nobody knows which chart matters, which alert deserves action, or which number tells the real story. Three clear SLOs beat 15 half-used ones every time.

Teams also make a mess by changing targets every week. One bad incident happens, so the target moves. One good week follows, so the target moves again. That turns your SLO into a mood, not a contract. Pick a target, keep it steady for a while, and learn from a few real cycles before you adjust it.

Alerts can cause damage too. If the team gets paged for every small spike, people stop paying attention. A failed checkout rate above the agreed threshold may need fast action. A brief slowdown in an internal admin page may not.

A small product team usually does better with simple rules: measure completed user actions, keep the first SLO set short, and use targets that match real operating limits. When an SLO stops helping decisions, rewrite it. When it helps the team choose what to fix first, keep it.

A quick weekly checklist

A weekly SLO review should feel small and useful. If it turns into a long meeting, the team will stop doing it. For most startups, 15 to 20 minutes is enough.

Keep the review tied to the few promises users notice most, like login, checkout, and the main task your product exists to help with. Pull up the last seven days, look at real misses, and decide what deserves action now.

• Check each promise on its own. Did users fail to log in, get blocked at checkout, or hit a slow or broken core flow?
• Look for repeat causes. One cause often sits behind several misses.
• Compare alerts with user pain. If the team got paged but nobody noticed a problem, the alert is too noisy. If users got stuck and no alert fired, the alert missed the point.
• Cut any metric nobody uses to make a decision.
• End with one small fix for the next sprint.

That last step matters more than a perfect dashboard. A short list of fixes that ship every week will do more for reliability than a big spreadsheet full of red and green numbers.

A small team might review the week and notice that login missed its promise twice for the same reason: sessions expired too early after a config change. The right next move is not a new reliability program. It is one fix, one owner, and a check next week to see if the problem stayed gone.

What to do next

Start small and stay honest. For most teams, three promises are enough: users can log in, users can pay, and users can finish the main job your product exists to do.

Put those promises where people will see them every week. A dashboard is fine. A shared doc is fine too. If the numbers hide in a monitoring tool that only engineers open, the SLOs will fade into the background.

Review them with product, engineering, and support in the same conversation. Each group sees a different part of the problem. Product knows which failures hurt trust fastest. Engineering knows what changed. Support knows what people actually complained about.

A simple weekly rhythm works well:

• Check whether each promise held up.
• Look at the biggest misses, not every small blip.
• Write down one fix the team will ship this week.
• Ask whether the target still matches what users expect.

Keep that meeting short. Thirty minutes is usually enough if the numbers are clear.

Do not add a fourth SLO just because the team feels more organized. Add one only when the first three feel routine. That usually means people know where the data comes from, alerts are not noisy, and the team can explain what to do when a promise slips.

If one of your targets still feels vague, trace the user path step by step. For login, that might mean entering a password, receiving a code, and landing on the account page. For checkout, it might mean cart, payment, and confirmation. Once the path is clear, the target gets easier to measure and easier to defend.

That is the point. You are not building a giant reliability program. You are making a few plain promises your team can keep.

If you want a second opinion on which flows matter most, Oleg Sotnikov at oleg.is works with startups and small companies as a Fractional CTO and advisor. He helps teams keep architecture and operating choices practical, especially when they want better reliability without adding a lot of process.