Self-Hosted SLO & Error Budget Management: Sloth vs Pyrra vs OpenSLO

Service Level Objectives (SLOs) and error budgets are the foundation of modern reliability engineering. An SLO defines the target level of service reliability that users expect, while an error budget quantifies how much unreliability is acceptable before action must be taken. Without proper tooling, tracking SLOs across dozens of microservices becomes a manual, error-prone process.

This guide compares three open-source platforms for managing SLOs and error budgets: Sloth, Pyrra, and OpenSLO (oslo). Each takes a different approach — from Prometheus-native YAML generators to spec-driven CLI tooling — but all share the goal of making reliability measurable and actionable.

What Are SLOs and Error Budgets?

Service Level Objectives define measurable targets for system reliability — for example, “99.9% of HTTP requests must complete within 200ms over a 30-day window.” The error budget is the inverse: if your SLO is 99.9%, you have a 0.1% error budget to spend on failures, deployments, or maintenance.

When the error budget is exhausted, teams should halt feature releases and focus exclusively on reliability improvements. This creates a data-driven balance between velocity and stability.

Implementing SLOs requires defining Service Level Indicators (SLIs) as the raw metrics that measure service health such as request latency, error rate, and availability. You then set SLO targets as the acceptable thresholds for each SLI, track burn rate to understand how quickly the error budget is being consumed, and apply multi-window analysis combining short-term and long-term burn rates to distinguish between brief spikes and sustained degradation.

Sloth: Prometheus-Native SLO Generator

Sloth is a Go-based CLI tool that generates Prometheus recording and alerting rules from simple YAML SLO definitions. It implements Google Site Reliability Engineering multi-window, multi-burn-rate approach natively.

Sloth strength lies in its simplicity. You define an SLO in YAML, and it produces all the Prometheus rules needed to track and alert on error budget consumption.

Sloth YAML Configuration

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version: "prometheus/v2-alpha"
service: "api-gateway"
slos:
  - name: "availability"
    objective: 99.9
    description: "API gateway availability SLO"
    sli:
      events:
        error_query: sum(rate(http_requests_total{service="api-gateway",status=~"5.."}[{{.window}}]))
        total_query: sum(rate(http_requests_total{service="api-gateway"}[{{.window}}]))
    alerting:
      name: "api-gateway-availability"
      labels:
        category: "availability"
      page_alert:
        labels:
          severity: "page"
      ticket_alert:
        labels:
          severity: "ticket"

Sloth generates Prometheus recording rules for short-window (5m, 1h) and long-window (6h, 30d) burn rates, along with alerting rules that fire when the error budget burn rate exceeds configurable thresholds.

Deploying Sloth with Docker Compose

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version: "3.8"
services:
  sloth:
    image: ghcr.io/slok/sloth:latest
    volumes:
      - ./slo-config:/config
    command: generate -i /config/slos.yaml -o /config/rules.yaml

For CI/CD integration, Sloth runs as a build step:

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sloth generate -i slo-config/slos.yaml -o prometheus-rules/slo-rules.yaml
kubectl apply -f prometheus-rules/

Pyrra: SLO Management with Web UI

Pyrra takes a more opinionated approach, providing both a Kubernetes operator and a standalone web dashboard for SLO management. Built by the Polar Signals team, Pyrra generates Prometheus recording rules and provides a React-based UI for browsing SLO status across all services.

Pyrra Kubernetes Manifest

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apiVersion: pyrra.dev/v1alpha1
kind: ServiceLevelObjective
metadata:
  name: api-availability
  labels:
    prometheus: kube-prometheus
spec:
  target: "99.9"
  window: 30d
  indicator:
    ratio:
      metric: http_requests_total{service="api-gateway"}
      errors:
        metric: http_requests_total{service="api-gateway",status=~"5.."}

Deploying Pyrra with Docker Compose

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version: "3.8"
services:
  pyrra:
    image: ghcr.io/pyrra-dev/pyrra:latest
    ports:
      - "9099:9099"
    command:
      - "api"
      - "--prometheus-url=http://prometheus:9090"
      - "--api-url=http://pyrra:9099"
  pyrra-filesystem:
    image: ghcr.io/pyrra-dev/pyrra:latest
    volumes:
      - ./slos:/etc/pyrra
    command:
      - "filesystem"
      - "--path=/etc/pyrra"

Pyrra dashboard shows each SLO current compliance percentage, remaining error budget, and burn rate trend. It integrates with Grafana for deeper visualization and can send alerts through Alertmanager.

OpenSLO (oslo): Spec-Driven SLO Definitions

OpenSLO is a vendor-neutral specification for defining SLOs, paired with a CLI tool (oslo) for validating, linting, and deploying SLO configurations across multiple monitoring backends.

OpenSLO YAML Definition

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apiVersion: openslo/v1alpha
kind: SLO
metadata:
  name: api-availability
  displayName: API Gateway Availability
spec:
  description: "API gateway must maintain 99.9% availability"
  service: api-gateway
  budgetingMethod: Occurrences
  objectives:
    - ratioMetrics:
        total:
          source: prometheus
          queryType: promql
          query: sum(rate(http_requests_total{service="api"}[{{.window}}]))
        good:
          source: prometheus
          queryType: promql
          query: sum(rate(http_requests_total{service="api",status!~"5.."}[{{.window}}]))
      target: 0.999
      timePeriod: CalendarMonth
      value: 1
  timeWindows:
    - duration: 1Mo
      isRolling: true

Using the oslo CLI

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# Validate SLO definitions
oslo validate slo.yaml

# Deploy to Prometheus
oslo deploy slo.yaml --provider prometheus

# List all SLOs
oslo get slo

OpenSLO key advantage is portability: the same YAML definition can target Prometheus, Datadog, Splunk, or New Relic. This makes it ideal for organizations migrating between monitoring stacks.

Why Self-Host SLO Management?

Self-hosting your SLO management platform offers several critical advantages over SaaS alternatives.

Data sovereignty and compliance. Error budgets and reliability metrics reveal detailed information about your infrastructure health, deployment patterns, and incident history. For organizations in regulated industries such as finance, healthcare, and government, keeping this data on-premises avoids third-party data processing agreements and audit complications.

Cost predictability at scale. SaaS SLO platforms charge per service or per metric. As your microservice count grows from dozens to hundreds, these costs compound rapidly. Self-hosted tools like Sloth and Pyrra have zero per-service licensing fees — the only cost is the infrastructure to run them.

Deep Prometheus integration. Most self-hosted monitoring stacks already run Prometheus. Sloth and Pyrra generate native Prometheus recording rules that integrate seamlessly with existing alertmanager configurations, Grafana dashboards, and Thanos or Cortex deployments. There is no data duplication or cross-platform correlation needed.

Customization and extensibility. Self-hosted SLO tools can be extended with custom SLI definitions, internal dashboards, and integration with your existing CI/CD pipelines. You can add organization-specific alerting policies, tie error budgets to deployment gates, or build custom reporting.

No vendor lock-in. OpenSLO specification-driven approach means your SLO definitions remain portable even if you switch monitoring backends. The YAML files are plain text, version-controlled alongside your infrastructure code.

For teams managing distributed systems, see our alert routing comparison. For Prometheus-based monitoring setups, check our centralized logging guide. For container security integration, our runtime security comparison covers it.

Choosing the Right SLO Management Tool

Sloth is the best choice for teams already running Prometheus who want a lightweight, no-UI solution. It generates standard Prometheus rules, integrates with existing alerting infrastructure, and has zero operational overhead beyond the CLI. Ideal for small to medium teams (5-50 engineers) managing 10-100 services.

Pyrra is ideal for organizations that need visibility into SLO status across teams. The web dashboard provides a shared source of truth for reliability targets, and the Kubernetes operator automates rule generation. Best for medium to large teams (20-200 engineers) with a Kubernetes-based infrastructure.

OpenSLO (oslo) is the right choice for multi-cloud or hybrid-cloud organizations that need SLO portability. If your team uses different monitoring backends across environments, the OpenSLO spec ensures consistent SLO definitions. Best for enterprises managing heterogeneous monitoring stacks.

FAQ

What is the difference between SLI, SLO, and SLA?

An SLI (Service Level Indicator) is the raw measurement of a service aspect such as request latency, error rate. An SLO (Service Level Objective) is the target value for that SLI, for example 99.9% availability. An SLA (Service Level Agreement) is a contractual commitment with consequences such as financial penalties if the SLO is not met.

How do I calculate error budget burn rate?

Burn rate equals (1 minus current reliability) divided by (1 minus SLO target). For a 99.9% SLO, if your current reliability is 99.5%, the burn rate is (1 - 0.995) / (1 - 0.999) = 0.005 / 0.001 = 5x. This means you are consuming your error budget 5 times faster than planned.

Can Sloth work without Kubernetes?

Yes. Sloth is a CLI tool that generates YAML files. It works with any Prometheus deployment — bare metal, Docker, or Kubernetes. The generated rules can be loaded via Prometheus rule_files configuration or deployed as Kubernetes ConfigMaps.

Does Pyrra support Thanos or Cortex?

Yes. Pyrra works with any Prometheus-compatible API, including Thanos Query and Cortex. Configure the prometheus-url flag to point at your Thanos or Cortex query endpoint.

Is OpenSLO production-ready?

OpenSLO is a specification with growing industry adoption. The oslo CLI is actively developed and supports validation and deployment. For production use, pair OpenSLO definitions with a backend like Sloth or Pyrra that can generate the actual monitoring rules.

How many SLOs should each service have?

A common rule of thumb is 1-3 SLOs per service: one for availability (error rate), one for latency, and optionally one for throughput or freshness. Too many SLOs create alerting fatigue; too few leave critical reliability dimensions unmeasured.