SLO Configuration as Code

SLOs and their underlying Service Level Indicators (SLIs) are defined in human-readable configuration files (e.g., YAML, JSON) that specify the what, not the how. This declarative approach separates intent from implementation, allowing infrastructure-agnostic definitions of metrics like model_inference_latency or retrieval_precision. The system's control plane is responsible for interpreting these files and enacting the necessary monitoring and alerting.

• Example: A YAML file defines an SLO target of 99.9% for requests with latency under 100ms, calculated over a 30-day rolling window.
• Benefit: Enables clear documentation, peer review, and eliminates configuration drift between environments.

Configuration files are stored in a version control system like Git, applying software engineering best practices to reliability management. This enables:

• Change Tracking & Auditing: Every modification to an SLO, including who changed it and why, is recorded in the commit history.
• Peer Review via Pull Requests: Changes to reliability targets undergo formal review before being merged, ensuring consensus and correctness.
• GitOps Workflows: Merging a change to the main branch can automatically trigger a pipeline that validates and deploys the new SLO configuration to production monitoring systems, ensuring state convergence.

Continuous Integration/Continuous Deployment (CI/CD) pipelines automatically validate SLO configurations for syntactic correctness and semantic sanity (e.g., ensuring error budgets are positive) before deployment. This prevents invalid configurations from reaching production. Automated deployment ensures that the monitoring and alerting landscape is always a direct reflection of the committed source of truth, eliminating manual, error-prone dashboard configuration. This principle is critical for maintaining consistency across staging, canary, and production environments.

The same SLO configuration can be parameterized and instantiated across different environments (development, staging, production) with environment-specific variables (e.g., different latency thresholds). This ensures that SLOs are tested early in the development cycle and that teams have a consistent understanding of reliability targets. Composite SLOs can be built by referencing and aggregating other SLO definitions, promoting reuse and accurately modeling complex, dependent services. This modularity is essential for managing AI services with multiple components like retrieval, inference, and post-processing.

The error budget—calculated as 100% - SLO—and its consumption policies are defined as code. This includes configuring multi-window alerting based on burn rate (e.g., alert if 2% of the monthly budget is burned in 1 hour or 5% in 6 hours). These policies dictate automated responses, such as triggering rollbacks, blocking deployments, or escalating pages. By codifying the risk tolerance, teams move from reactive alerting to proactive reliability management, where the error budget becomes a central resource for guiding release velocity and operational focus.

Configuration as Code establishes a single, authoritative source for all reliability definitions, which is then propagated to various observability tools. This prevents the common anti-pattern where SLOs are defined in slides, dashboards, and alerting tools independently, leading to misalignment. The configuration generates consistent artifacts for:

• Time-Series Databases: Creating the queries for SLIs.
• Alerting Systems: Setting up burn-rate-based alerts.
• Dashboards: Visualizing SLO compliance and error budget status.
• Reporting Tools: Generating reliability reports for stakeholders.

This unification is key for SLOs for business metric correlation, as it ensures technical metrics are consistently tied to business outcomes.

Large engineering organizations often build internal platforms with custom Domain-Specific Languages (DSLs) or libraries to enforce SLO best practices and abstract complexity.

• Standardized Libraries: Provide internal Python/Go libraries that validate SLO configurations, generate monitoring code, and integrate with proprietary telemetry.
• Approval Workflows: Embed SLO definition and review into CI/CD pipelines, requiring SLOs for production service deployment.
• AI-Specific Extensions: These platforms are extended to include AI-specific SLIs (e.g., TTFT, hallucination rate) and automate their instrumentation within model serving frameworks.

A Service Level Indicator (SLI) is a directly measurable metric that quantifies a specific aspect of a service's performance, such as latency, error rate, or throughput. It serves as the foundational data point for evaluating a Service Level Objective (SLO).

• Examples for AI Services: Model inference latency, Time To First Token (TTFT), retrieval precision@K, hallucination rate.
• Configuration as Code: SLIs are defined declaratively in version-controlled files, specifying the data source, query, and calculation method.

An error budget is the allowable amount of service unreliability, calculated as 100% - SLO Target. It quantifies the risk a team can accept for deploying changes or conducting experiments without violating the SLO.

• Core SRE Concept: Error budgets drive a balance between innovation velocity and reliability.
• Managed as Code: Burn rate (speed of budget consumption) and alerting policies based on the budget are defined in configuration files, enabling automated risk management and governance.

A golden signal is one of four fundamental metrics—latency, traffic, errors, and saturation—used in site reliability engineering (SRE) to comprehensively monitor the health and performance of a service.

• Universal Health Indicators: These signals provide a holistic view of system behavior.
• Basis for SLIs: Golden signals often form the core of user-centric Service Level Indicators. In Configuration as Code, these are the primary metrics tracked and targeted for AI services.

Infrastructure as Code (IaC) is the practice of managing and provisioning computing infrastructure through machine-readable definition files, rather than physical hardware configuration or interactive configuration tools.

• Foundational Practice: IaC tools like Terraform, Pulumi, and AWS CloudFormation enable consistent, repeatable, and version-controlled infrastructure deployment.
• Direct Precedent: SLO Configuration as Code is a direct application of IaC principles to reliability management, extending automation from the infrastructure layer to the service quality layer.

GitOps is an operational framework that takes DevOps best practices used for application development—such as version control, collaboration, compliance, and CI/CD—and applies them to infrastructure automation and software deployment.

• Declarative & Automated: The desired state of the system is declared in Git; automated operators reconcile the live state.
• Applied to SLOs: SLO Configuration as Code fits perfectly within a GitOps model, where changes to SLO definitions, alerting rules, and dashboards are proposed via pull requests, reviewed, and then automatically applied to the monitoring system.

Continuous batching is an inference optimization technique that dynamically groups requests of varying lengths and processing states to maximize GPU utilization and improve throughput. It is a key method for achieving latency and cost-efficiency SLOs for LLM services.

• Performance SLI Enabler: Directly impacts Time Per Output Token (TPOT) and overall throughput.
• Configuration Impact: The tuning parameters for batching (e.g., maximum batch size, scheduling policy) are critical operational knobs that must be managed alongside SLO definitions to ensure targets are met efficiently.