slo-error-budget

SLO and Error Budget Skill

Produce a complete, implementable SLO document for a service — covering what to measure, what target to set, how to calculate the error budget, and what to do when it burns.

A good SLO is not a target to hit. It is an agreement about what reliability means for your users — and a framework for making principled trade-offs between reliability and velocity.

Required Inputs

Ask for these if not already provided:

• Service name and brief description of what it does
• Primary users — who depends on this service and how
• User-facing interactions to protect — e.g. API calls, page loads, transactions
• Current reliability data — error rate, latency, uptime (last 30–90 days if available)
• Existing on-call setup — who responds to alerts?
• Deployment frequency — how often does the team ship?
• Any existing SLAs with customers — these constrain SLO targets

Key Definitions

Always establish these before writing the SLO:

Term	Definition
SLI (Service Level Indicator)	The metric being measured — e.g. "% of requests completing successfully in <500ms"
SLO (Service Level Objective)	The target for that metric — e.g. "99.5% of requests"
SLA (Service Level Agreement)	The contractual commitment to customers — must be looser than the SLO
Error budget	The allowed headroom below 100% — the budget for planned and unplanned downtime
Burn rate	How fast the error budget is being consumed

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Output Format

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SLO Document: [Service Name]

Service: [Name] | Team: [Team name] Owner: [Name / role] | Approved by: [Name] Effective date: [Date] | Review date: [Date + 3 months] Version: [1.0]

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Why This SLO Exists

[2–3 sentences. What reliability problem are we solving? What was happening before this SLO that made us need it? What decision-making does this SLO enable?]

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Service Overview

What this service does: [One sentence] Who depends on it: [Internal teams / external customers / both — describe] Critical user journeys protected by this SLO:

1. [Journey 1 — e.g. "User completes a payment"]
2. [Journey 2]
3. [Journey 3]

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SLIs — What We Measure

Define one SLI per user journey or reliability dimension. Keep it to 3–5 SLIs maximum.

SLI 1: [Name — e.g. Request Success Rate]

Field	Detail
What it measures	[e.g. "% of API requests that return a non-5xx response"]
Good event definition	[e.g. "HTTP response with status 2xx or 4xx, completed within 500ms"]
Bad event definition	[e.g. "HTTP response with status 5xx, or any response taking >500ms"]
Measurement source	[e.g. "Application load balancer access logs / Datadog APM / Prometheus"]
Measured over	Rolling 28-day window
Exclusions	[e.g. "Health check endpoints excluded / Requests during planned maintenance excluded"]

SLI 2: [Name — e.g. Latency]

Field	Detail
What it measures	[e.g. "P99 response time for the /checkout endpoint"]
Good event definition	[e.g. "Request completes in ≤500ms at P99"]
Bad event definition	[e.g. "Request takes >500ms at P99"]
Measurement source	[Source]
Measured over	Rolling 28-day window
Exclusions	[Any exclusions]

SLI 3: [Name — e.g. Data Freshness / Queue Depth / etc.]

[Same structure]

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SLO Targets

SLI	Target	Window	Error Budget
[SLI 1 name]	[X]%	28-day rolling	[100 - X]% = [Y minutes/month]
[SLI 2 name]	[X]%	28-day rolling	[100 - X]% = [Y minutes/month]
[SLI 3 name]	[X]%	28-day rolling	[100 - X]% = [Y minutes/month]

How targets were set:

• Historical baseline (last 90 days): [X]%
• Target is set [above / at] historical baseline to [improve reliability / reflect current reality while formalising the commitment]
• Rationale: [1–2 sentences]

What 100% is NOT the target: [Brief explanation of why targeting 100% is counterproductive — it discourages feature development and doesn't reflect user reality]

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Error Budget Calculation

For SLI 1 ([Name]), at [X]% target:

Error budget = (100% - SLO target) × measurement window
             = (100% - [X]%) × 28 days × 24 hours × 60 minutes
             = [Y]% × [Z total minutes]
             = [N] minutes of allowed failure per 28-day window

In plain terms: We can afford [N] minutes of [bad events] in any rolling 28-day window before we breach the SLO.

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Burn Rate Alerts

Burn rate = how fast the error budget is being consumed relative to the budget window. A burn rate of 1 = consuming the budget at exactly the rate that would exhaust it over 28 days.

Alert	Burn rate	Window	Severity	Response
Page (critical)	>14×	1 hour	P1	Page on-call immediately — budget exhausted in <2 hours
Page (high)	>6×	6 hours	P2	Page on-call — budget exhausted in <5 days
Ticket (warning)	>3×	3 days	P3	Create ticket — review at next team meeting
Info	>1×	28 days	Info	Log only — budget on track to exhaust by end of window

Alert implementation: [Link to alert config in monitoring tool — e.g. Datadog, Prometheus/Alertmanager, Grafana]

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Error Budget Policy

This policy defines what to do with the error budget — both when it's healthy and when it's burning.

When budget is healthy (>50% remaining)

• Feature development and deployments proceed at normal pace
• The team may take on riskier experiments
• Reliability improvements are scheduled but not urgent

When budget is at risk (25–50% remaining)

• Deployment frequency reduced — team ships only well-tested changes
• One reliability improvement added to current sprint
• Weekly error budget review added to team standup

When budget is nearly exhausted (<25% remaining)

• Feature work paused in favour of reliability improvements
• No new deployments without explicit on-call approval
• Daily review of error budget burn rate
• CSM / support notified to manage customer expectations

When budget is exhausted (0% remaining — SLO breached)

• All feature work stops
• On-call engineer and engineering manager notified immediately
• Post-incident review (PIR) required within 5 business days
• SLO target may be temporarily relaxed (with stakeholder approval) while root cause is addressed

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Dashboard and Reporting

SLO dashboard: [Link to Datadog / Grafana / etc. dashboard]

Metrics exposed:

• Current SLO compliance (rolling 28-day)
• Error budget remaining (% and minutes)
• Burn rate (current and trend)
• Incident count and MTTR this window

Reporting cadence:

Audience	Frequency	Format
Engineering team	Weekly	Slack summary — #[service]-slo
Engineering manager	Monthly	SLO review meeting
Stakeholders / customers	Quarterly	SLO compliance summary

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Exclusions and Edge Cases

Planned maintenance: Error budget is not consumed during pre-announced maintenance windows. Maintenance must be communicated [X hours] in advance via [channel].

Dependency failures: If SLO breach is caused by an upstream dependency outside our control, document it — but it still counts against our error budget (our users don't distinguish between our failures and our dependencies' failures).

Force majeure: [Policy for cloud provider outages, major infrastructure events]

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SLO Review Cadence

Review	When	Who	Output
Error budget review	Weekly	Team	Budget health check — adjust if burning fast
SLO target review	Quarterly	Team + EM	Adjust targets if baseline has shifted significantly
Annual SLO audit	Annually	Team + Stakeholders	Review SLIs — are we measuring the right things?

When to change the SLO target:

• Historical baseline has improved significantly and target no longer reflects real reliability
• User feedback indicates the target is misaligned with what users actually experience
• The SLO is being gamed (metric is healthy but users are unhappy)

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Quality Checks

• SLIs are user-facing — they measure what users experience, not internal system metrics
• Good and bad events are precisely defined — no ambiguity about what counts
• Targets are based on historical data, not aspirational round numbers
• Error budget policy has clear triggers and clear actions — not "discuss as a team"
• Burn rate alerts have different windows to catch both fast burns and slow burns
• Exclusions are documented so they don't silently inflate the SLO number

Anti-Patterns

• Do not set SLO targets at 100% — this discourages feature development and does not reflect how users experience reliability
• Do not measure internal system metrics as SLIs — SLIs must reflect what users directly experience, not internal CPU or memory
• Do not write an error budget policy with vague triggers — "discuss as a team" is not an actionable policy; triggers must be specific percentages
• Do not base targets on aspirational round numbers — always derive from historical baseline data
• Do not configure only one burn-rate alert window — a single window misses both fast burns and slow burns that exhaust the budget quietly