The client had a functioning inference platform, but it was not yet ready to operate as a production-grade service. The system could serve models, but the team had limited visibility into performance, scaling decisions were mostly manual, and incidents were often discovered through user reports instead of proactive alerts.
We operationalized the platform end to end by adding LLM-specific observability, structured logging, distributed tracing, Kubernetes-native autoscaling, SLO-based alerting, and clear incident response procedures. The result was an inference service the team could monitor, scale, debug, and support with real operational confidence.
The client's inference platform worked, but it was difficult to operate safely at production scale.
Incidents were often discovered by users before the engineering team had enough signal to respond. Engineers could see whether the service was up or down, but they lacked the deeper model-serving metrics needed to understand why latency was increasing, why requests were backing up, or whether GPU capacity was being used efficiently.
The platform also relied heavily on manual scaling. Capacity was provisioned for peak demand and left running during off-peak periods, which increased cost. At the same time, scaling decisions were based on rough infrastructure signals rather than the actual pressure of inference workloads.
The core problems were:
The platform needed to move from "working" to "operable."
We instrumented the full request path with metrics, logs, and traces focused on the signals that matter most for inference workloads.
Traditional infrastructure metrics such as CPU, memory, and pod health were not enough. LLM inference has its own operational profile: long-running requests, variable prompt sizes, variable output lengths, batching behavior, GPU memory pressure, queue buildup, and token-generation latency.
We added observability around both service health and model-serving behavior.
Key metrics included:
Prometheus was used to collect metrics from the serving layer, Kubernetes, and GPU infrastructure. Grafana dashboards were created for platform-wide health, per-model behavior, per-tenant usage, and SLO burn-rate visibility.
Structured logs were introduced across the request path so engineers could trace a request from gateway entry through routing, model execution, response streaming, and completion. Logs were standardized with fields such as request ID, tenant ID, model name, prompt token count, output token count, latency, status code, and failure reason.
OpenTelemetry was added to provide distributed traces across the serving path. This made it easier to identify whether latency was coming from request routing, queueing, model execution, downstream dependencies, or response streaming.
Alerting was redesigned around symptoms and SLO impact instead of raw noise. Rather than alerting on every small metric movement, the platform alerted when customer-facing behavior was at risk: latency budget burn, rising timeout rate, queue saturation, GPU memory exhaustion, replica unavailability, or sustained error-rate increases.
We replaced manual capacity management with Kubernetes-native autoscaling based on real inference pressure.
For inference workloads, request count alone is often a poor scaling signal. One request with a long prompt and long response can consume far more GPU time and memory than many small requests. Scaling needed to reflect token pressure, queue buildup, and active generation behavior, not just the number of incoming HTTP calls.
We built a custom metrics pipeline that exposed inference-level signals to the autoscaling layer. These metrics were used by Kubernetes autoscaling components to make better scaling decisions.
The autoscaling design included:
This gave the platform a more accurate relationship between demand and capacity. When traffic increased, the system could scale replicas and infrastructure automatically. When traffic dropped, it could reduce excess capacity instead of leaving expensive GPU nodes idle.
Observability and autoscaling were only part of the work. The platform also needed clear operational procedures so the internal team could support it without relying on outside consultants.
We created runbooks for the most common failure modes, including latency spikes, queue saturation, GPU memory pressure, replica crashes, failed deployments, model-specific errors, and cluster capacity constraints.
Each runbook included:
We also introduced load testing and failure testing to validate operational behavior before production incidents occurred. Load tests simulated realistic traffic ramps, prompt lengths, output lengths, and concurrency patterns. Failure tests validated how the system behaved when pods restarted, nodes became unavailable, or downstream dependencies slowed down.
This gave the team confidence that alerts, dashboards, autoscaling, and runbooks worked together in real scenarios.
The inference platform became a production-operable service.
The team could now detect issues earlier, understand root causes faster, and respond using documented procedures. Dashboards provided visibility into model behavior, tenant usage, infrastructure health, and SLO risk. Autoscaling reduced the need for manual capacity management and helped keep infrastructure aligned with real demand.
Most importantly, the platform became something the client could put operational commitments behind. Instead of hoping the system would hold up under production traffic, the team had the tooling, metrics, alerts, and processes needed to manage it confidently.
The client gained: