Praesidia exposes a standard Prometheus metrics endpoint that your existing scraper can collect on its normal interval, giving you time-series data on task throughput, queue depth, latency percentiles, live operator connections, and per-organization activity — all in the format your Grafana, alerting, and SLO tooling already understands. You do not need a proprietary dashboard to observe the platform; you wire it into whatever monitoring stack you already run.

Why Prometheus for an AI Control Plane

Prometheus has become the default instrumentation format for server-side systems, and for good reason: its pull-based model, text exposition format, and label-based data model map cleanly onto the multi-dimensional signals that an AI platform generates. For an AI control plane, that means you can slice agent task latency by organization, track queue depths across job types, and correlate spend-rate spikes with specific workflows — all using the same PromQL queries and Grafana panels you already use for the rest of your infrastructure.

The alternative — custom dashboards that only live inside the product — requires operators to context-switch between tools and prevents meaningful correlation with infrastructure-layer signals such as database latency or container CPU. A standards-based scrape surface avoids this. It also means that AI agent observability fits naturally into your existing service level objectives for AI services without requiring a separate toolchain. For a broader look at the full observability picture beyond raw metrics, see observability for AI agents: logs, metrics, and traces.

What the Metrics Endpoint Exposes

Praesidia's metrics endpoint serves the standard Prometheus 0.0.4 text exposition format. Counters, gauges, and histograms cover the areas that matter most for AI workloads:

Task and queue metrics. Counters for tasks submitted, completed, and failed, broken down by type. Queue depth gauges reflect how much work is waiting at any given moment, which is the first signal of a capacity or throughput problem.

Latency histograms. Hot-path stage timings are recorded as histograms, so you can query p50, p95, and p99 latency for the critical execution steps without resorting to log sampling.

Live connection gauges. Active real-time connections per replica are tracked as a gauge, giving visibility into live operator sessions.

Dependency health gauges. Upstream dependency states — database, cache, and other critical services — are reflected in named gauges, allowing alerting rules that fire when a dependency degrades before it affects end users.

Per-organization labels. Where meaningful, metrics carry an organization-label dimension so you can break down activity by tenant. This is particularly useful for capacity planning and for identifying which organizations are driving unusual load patterns. For how these per-org signals connect to cost attribution, see Tracking Per-Connection AI Usage and Cost.

Multi-Replica Aggregation

In a horizontally scaled deployment, each replica independently maintains its own in-process metric registry and responds to Prometheus scrape requests. Prometheus itself handles federation across replicas through its standard sum, rate, and histogram_quantile aggregation functions — the same way you aggregate metrics from any multi-instance service.

Praesidia also provides an operator-facing aggregated view for tooling that needs a single fleet-level snapshot without running a full Prometheus query layer. This surface returns a roll-up alongside the per-replica breakdown so you can distinguish fleet totals from individual instance data, and is protected by the same access controls that govern other privileged operator operations.

Wiring Into Grafana

The integration pattern follows standard Prometheus/Grafana conventions:

  1. Add a scrape job. In your Prometheus configuration, add a job that targets the metrics path at your desired scrape interval. A 15-second interval is typical for operational dashboards; 60 seconds is sufficient if you are primarily interested in billing and capacity trends.

  2. Build panels with PromQL. Task throughput, error rates, and queue depths each map to straightforward PromQL expressions using rate() on counters and direct gauge reads. Latency dashboards use histogram_quantile() against the histogram metrics.

  3. Segment by organization. Labels on the metrics let you filter or group-by organization within panels, useful when you are running Praesidia as a multi-tenant service and need per-customer capacity visibility.

  4. Set alerting rules. Prometheus alerting rules on queue depth, error rate, and latency thresholds give you pager-level signals from the same data. These integrate with AlertManager, PagerDuty, or whichever notification path your team already uses.

For teams using Grafana Cloud or another managed Prometheus-compatible backend, the scrape path works identically — point the remote write or scrape agent at Praesidia's metrics endpoint and the data flows into your existing workspace.

Connecting Metrics to SLOs

Service Level Objectives for AI workloads generally focus on three signal classes: latency (are tasks completing within the expected window?), availability (are tasks completing at all?), and cost (is spend staying within defined bounds?). Praesidia's metrics surface covers the first two directly.

For latency SLOs, use the hot-path histogram metrics to define a target: for example, 95% of agent task completions should complete within a given duration over a rolling window. The histogram data lets you compute this with histogram_quantile() in PromQL and track it as an error budget.

For availability SLOs, track the ratio of failed tasks to total tasks submitted using the counter metrics. A sustained failure rate above your threshold triggers an alert and begins burning the error budget.

Cost SLOs are a different category — they depend on the billing and credit-ledger data that Praesidia surfaces through its cost monitoring features rather than through the Prometheus endpoint. The two sources complement each other: Prometheus tells you about operational health in real time, while the cost APIs give you attribution-level spend data suitable for FinOps analysis. For a broader picture of how these signals fit together, see the advanced analytics for AI operations overview.

For how these metrics feed into availability targets, see service level objectives for AI services for current approaches to defining error budgets from task counters and latency histograms.

Production Considerations

Network access. The Prometheus scrape path is designed for your monitoring infrastructure, not for public internet exposure. In production, place it behind network-level access controls — a private network segment, VPN, or scrape-endpoint allowlist — so that only your Prometheus instance can reach it. For how health probes and readiness signals complement the metrics surface, see Health Probes and Readiness for AI Infrastructure.

Scrape frequency and cardinality. Per-organization labels increase cardinality in proportion to the number of tenants. For platforms with many organizations, evaluate your Prometheus storage retention and cardinality limits before enabling very high-frequency scraping. In most deployments a standard 15- or 30-second interval with standard Prometheus retention is sufficient.

Cross-replica gauge interpretation. When summing gauges across replicas — for example, WebSocket connection counts — the aggregated value correctly represents the fleet total. For gauges with latest-value semantics such as per-organization watermarks, however, the per-replica breakdown is more meaningful than the fleet sum. The snapshot endpoint returns both, and the documentation notes which interpretation applies to each metric.

Common questions

Does Praesidia require a specific version of Prometheus? The metrics endpoint serves the Prometheus 0.0.4 text exposition format, which is the stable, widely-supported format that all current Prometheus versions and compatible scrape agents understand. No specific Prometheus version is required; any scraper that supports the standard text format works without configuration changes.

Can I use this with Datadog, New Relic, or other backends? Yes. Any backend that can scrape Prometheus text-format metrics — which includes Datadog Agent, New Relic's Prometheus integration, Grafana Alloy, Victoria Metrics, and many others — works with Praesidia's metrics endpoint. You configure the scrape target in your agent the same way you would for any other Prometheus-instrumented service.

Are cost and spend metrics available through Prometheus? Operational counters and latency data are available through the Prometheus endpoint. Detailed cost attribution, credit balances, and per-agent spend figures come from Praesidia's billing and cost monitoring APIs, which provide richer financial semantics than a time-series metric format. The two surfaces are designed to complement each other rather than overlap. For teams focused on spend visibility, visualizing AI usage and cost covers the cost-specific tooling in detail.

How do I alert on AI agent failures in my existing alerting system? Use the task failure counter metrics with a rate() expression over a rolling window as the alert condition. Set the threshold to match your availability SLO. The alert fires into whichever notification path you have wired to Prometheus AlertManager — PagerDuty, Opsgenie, Slack, or others — with no change to your existing on-call workflow. For alerting that runs through the platform's own notification surfaces, see Slack and multi-channel alerting.

Fitting Into an Existing Monitoring Stack

The design goal behind Praesidia's metrics surface is that it should add zero friction to an existing observability practice. Your Prometheus already scrapes dozens of services; Praesidia adds one more job. Your Grafana already has dashboards; Praesidia's metrics use the same PromQL patterns you write for everything else. Your alerting rules already fire on error rate and latency thresholds; the same patterns apply here.

This matters especially as AI workloads become a larger share of total compute. Keeping AI agent observability inside the same toolchain as the rest of your infrastructure means incidents surface in the same dashboards, on-call rotations see the same alerts, and post-mortems have consistent data. Praesidia is designed to fit that model rather than demand a separate screen.