vLLM monitoring with Prometheus metrics guide

Why vLLM monitoring with Prometheus metrics matters before go-live

vLLM serves Prometheus-format metrics at /metrics on its API server, so the monitoring work is choosing which signals matter to your service, scraping them from inside your internal network, and turning them into alerts and capacity decisions. Before go-live, you need to answer three questions from the metrics alone: is the server healthy, is it saturated, or is it leaving expensive GPU capacity idle. If your team cannot answer those questions from data it trusts, the first production incident will be the first time anyone looks.

The endpoint looks simple and is easy to get wrong. The metrics share the API port with the inference endpoints, so whoever can reach the server can read the metrics unless you block them upstream. The --api-key option does not cover /metrics, /health or the other operational routes, and the vLLM security documentation advises against relying on that key alone. Histogram buckets are configurable, which means the default latency boundaries may not separate the response times your users actually feel. Scrape exposure and bucket choice are decisions you have to make on purpose, and both affect what you can alert on later.

Raw metrics are not a monitoring design. A queue depth or KV cache usage figure means little until it is read against the model being served, the request mix hitting it, and the GPU size and count behind it. A server that looks busy on one model may be wasting capacity on another, and a latency spike during long-context requests may be expected behavior rather than a fault. Before go-live, agree which model, traffic pattern and accelerator configuration each dashboard and alert is written for, and record the thresholds that would trigger a capacity change, not just an alert.

This topic assumes a vLLM deployment is already running. Installing and configuring the server, choosing serving flags and chat templates, and running Kubernetes or virtual machine infrastructure are covered in the sibling topics on serving configuration and deployment. Access control, reverse proxy allowlists and multi-node network isolation are covered in the authentication and security hardening topics. Use this section to decide what you need to observe and why, then confirm with your own team which of those sibling topics must be finished before you scrape production traffic.

Which vLLM and Prometheus components the monitoring depends on

Monitoring vLLM with Prometheus touches more than one product. The metrics come from the vLLM API server, but scraping, protecting, alerting and visualizing them depend on separate components that each need their own checks before the signals can be trusted.

vLLM API server and /metrics endpoint

The vLLM server, started with vllm serve, exposes Prometheus-format metrics at /metrics on its API server, covering request counts, token throughput, latency histograms and KV cache use. Metric names and labels can change between releases, so confirm which vLLM version you run and check the exact metric names it exposes before writing any dashboard or alert rule. Also confirm how your deployment enables the endpoint and whether any flags change what it reports.

Configurable histogram buckets

Latency histograms use bucket boundaries that can be configured in vLLM. The bucket layout decides how finely time-to-first-token and request latency can be resolved, so buckets that are too coarse hide the tail behavior your SLOs depend on. Ask your team to define the latency targets first, then confirm the bucket settings in your vLLM version cover those thresholds before setting alert levels.

Prometheus server and scrape configuration

Prometheus pulls metrics from each vLLM replica on a set interval, and the scrape configuration defines the targets, labels and timeouts. Each replica needs its own target so per-instance queue depth and KV cache use stay visible. Confirm the scrape interval, retention period and storage size with your platform team, and check that the job names and labels match the dashboards and alerts you plan to build.

Reverse proxy or gateway protecting /metrics

The vLLM --api-key option does not protect /metrics, so the metrics endpoint is open to anyone who can reach the API server unless a network or proxy control blocks it. A reverse proxy or gateway should allowlist only the endpoints meant for users and keep /metrics reachable only from the Prometheus server. Verify this from outside the cluster and from the public route, since a single missing rule exposes queue depth and usage data.

Alertmanager for routing alerts

Alertmanager receives alerts that Prometheus rules fire and routes them to on-call channels, with grouping, deduplication and silences. Alert rules for queue depth, KV cache pressure and error rates are only useful if they reach the right owner, so confirm receivers, escalation paths and maintenance windows with the teams that will respond. Test the routing with a controlled alert before relying on it for production traffic.

Grafana or another dashboard tool

A dashboard tool such as Grafana reads from Prometheus and turns the vLLM metrics into views for throughput, latency, KV cache use and queue depth. Dashboards built on metric names from one vLLM release can break silently after an upgrade, so keep dashboard definitions in version control and review them during each upgrade test. Confirm who owns the dashboards and who can edit them, since they also expose operational detail.

Which monitoring setup fits your team: decisions and trade-offs

This table compares the main monitoring design choices for vLLM metrics, with the situations where each option fits and the ongoing cost it adds. Read each row as a decision: pick the option that matches your network layout, replica count and alerting maturity, then plan for the operational cost listed. Most teams get the best result by scraping per replica with labels, keeping default buckets until real traffic shows a gap, and alerting on recording rules that you review after each vLLM upgrade.

Decision	When it fits	Operational cost
In-cluster Prometheus scraping /metrics	vLLM runs in the same Kubernetes cluster or network segment as Prometheus, and you want metrics to stay inside the isolated network	One more Prometheus to run, back up and upgrade, plus storage for retention; metrics endpoints must stay off public routes
Central Prometheus across networks	One team watches several clusters, data centers or air-gapped sites from one place and can open a controlled path to each vLLM metrics port	Cross-network scrape traffic, firewall rules per site, and a single point of failure unless you run federation or redundant servers
Single vLLM instance scraped without labels	A proof of concept or one model on one server, where per-replica detail is not needed yet	Cheapest to run, but you cannot tell replicas apart when latency or queue depth diverges, so troubleshooting slows down
Per-replica scraping with instance and model labels	Several replicas or several models serve production traffic and you need to compare queue depth, KV cache use and latency across them	More time series to store and query; keep label sets stable so dashboards and alert rules do not break when pods are rescheduled
Default histogram buckets	You are starting out and do not yet know your target latencies for time to first token or end-to-end requests	No tuning effort, but coarse buckets can hide the percentile you care about, so a later re-bucketing may need a restart and new baselines
Customized buckets matched to target latencies	Your service level objectives for latency are written down and you need accurate percentiles near those thresholds	Requires measuring real traffic and reviewing bucket choices after each vLLM release, because metric names or bucket settings can change
In-house dashboards	Your team already runs Grafana, has an owner for observability, and needs panels for vLLM-specific queues and KV cache behavior	Initial build time and ongoing edits whenever metric names change between vLLM minor releases, which arrive about every two weeks
Templated dashboards as a starting point	You want a working set of panels quickly and will adjust them to your models, replicas and service level targets	Templates tend to be generic; expect to remove panels that do not apply and add panels for your own latency targets and capacity signals
Alerting on raw metrics	Few alert rules, simple thresholds, and a team that wants direct traceability from alert to metric	Each alert query runs at evaluation time against high-cardinality series, and rules repeat the same math across many alerts
Alerting on recording rules	Several alerts share the same calculations, such as request rate, error ratio or percentile latency per model	Recording rules must be reviewed when vLLM renames or changes metrics, so add a check to each upgrade test to confirm the rules still return data

How to set up vLLM Prometheus monitoring, step by step

This sequence takes a vLLM server from an unscraped /metrics endpoint to a monitored service with dashboards, recording and alert rules, and a tested route to on-call. Each step ends with a check, so the team knows when to move on.

Confirm the metrics endpoint from the scraper
Start from the vLLM API server and confirm that the Prometheus-format metrics endpoint at /metrics responds to requests from the host that runs Prometheus, over the internal network only. Check from a second network location as well, and confirm that the endpoint is not reachable through any public load balancer, ingress or reverse proxy route. Metrics are served by the API server, and the vLLM security docs do not place /metrics behind the --api-key option, so network isolation or a reverse proxy allowlist is what keeps it private. The step is done when the scraping host gets a valid response and an outside test gets none.
Define the scrape job and instance labels
Add a scrape job to the Prometheus configuration that targets each vLLM instance on its internal address and port. Set a stable instance label per replica and a job label per model or deployment, and agree the scrape interval with the team that owns the dashboards and the storage budget before you commit it. Keep label names consistent across environments so queries work the same in test and production. The step is done when Prometheus loads the configuration without errors after a reload.
Verify target health in Prometheus
Open the Prometheus targets view and confirm that every vLLM instance shows as up with a recent last-scrape time and no scrape errors. If a target is down, check the network path from the Prometheus host, the port, and whether the server has finished loading its model, since the endpoint may not answer during startup. Test one target with a direct request to the metrics endpoint and compare the result with what Prometheus recorded. The step is done when all expected targets are up over several scrape cycles.
Confirm core series after a test request
Send a small test request through the OpenAI-compatible endpoint, such as a completion or chat request, and then query Prometheus for the request count, token throughput, latency histogram and KV cache usage series. Record the exact metric names that appear, because naming can differ between vLLM releases, and confirm them against the version you run in the official metrics documentation. The step is done when each series in scope returns a value that moves after the test request.
Tune histogram buckets for latency targets
Compare the default latency histogram buckets with the latency targets your application owners have agreed for time to first token and end-to-end response time. Configure the bucket boundaries in vLLM so that the targets fall on bucket edges, because percentile estimates are only as precise as the buckets around them. Keep the bucket set small enough to limit series cardinality. The step is done when a load test shows percentile estimates that are stable and sensible around the targets.
Build dashboards for the operating questions
Create dashboards that answer four questions: latency percentiles over time, token throughput, KV cache use, and queue depth of waiting requests. Group panels by model and instance, and add a row that shows request errors and scrape health so that a silent scrape failure is visible. Review the panels with the people who will be paged, and remove any panel that nobody would act on. The step is done when each panel shows live data for every instance and the operators have walked through a sample incident using it.
Write recording and alert rules
Add recording rules for the expensive queries that dashboards and alerts reuse, such as percentile latency and per-minute token rates, and write alert rules for sustained latency above target, growing queue depth, KV cache near capacity and missing scrapes. Set thresholds from the load test and the agreed service objectives, and use a for duration so that brief spikes do not page anyone. Store the rule files in version control with the label conventions beside them. The step is done when Prometheus loads the rules and each rule evaluates without errors.
Route alerts and test one end to end
Configure Alertmanager to send alerts to the on-call channel and paging tool your team already uses, with grouping and inhibition rules that avoid duplicate pages for one outage. Then trigger one alert on purpose, for example by stopping a test instance's scrape or pointing a test rule at a low threshold, and confirm that it fires, reaches the channel, and clears when the condition ends. The step is done when the on-call person confirms receipt and the resolved notification arrives.
Document labels and confirmed metric names
Write a short reference that lists each label convention, each metric name confirmed on the running vLLM version, the bucket configuration, the scrape interval and the owner of each alert. Record the vLLM version used during confirmation, and add a checklist item to re-confirm metric names before each upgrade, because minor releases arrive often. The step is done when a new engineer can read the document and add an instance or a dashboard without asking how the labels work.

How monitoring differs for single node, multi-node and Kubernetes vLLM

On a single host, vLLM runs as one serving process with one API endpoint, so Prometheus has one scrape target and the label set stays simple. The main decisions are where the scraper runs and how the metrics path is reached. Most teams place Prometheus on the same host or inside the same private network, so the /metrics path never crosses a user-facing interface.

One target: configure a single scrape job for the API server port, with a scrape interval your team agrees on.
Network path: keep /metrics on a private interface, or block it at the firewall or reverse proxy, because the --api-key option does not cover that path.
Labels: rely on the job and instance labels that Prometheus adds, since there are few per-node distinctions to make.
Local storage: size the disk on the host for the retention period your team chooses and confirm that the scraper can write to it during restarts.

Confirm that the metrics path returns nothing from the user-facing listener and that your proxy rules block it.

How to keep vLLM metrics private while staying observable

The /metrics endpoint on a vLLM server reveals traffic patterns, model names and capacity limits, so it needs the same care as any other production interface. The controls below work together: the proxy decides what users can reach, the network decides who can reach metrics, and the monitoring tools decide who can read the results.

Why the API key does not cover metrics

The vLLM --api-key option protects only some endpoints, under /v1, /v2, /inference and /cohere. It does not protect /metrics, /health, /pause, /resume, /abort_requests or /invocations, and the vLLM security docs warn against relying on it alone. Check each of these paths against your deployed version before you assume any of them is protected.

Proxy allowlist for user endpoints

Place a reverse proxy in front of vLLM and allow only the paths your applications call, such as the OpenAI-compatible completion and chat routes you actually use. Deny everything else by default, including control endpoints and /metrics, and test the allowlist with requests that should fail. Confirm the exact paths your version exposes before writing the rules.

Metrics reachable only from Prometheus

Restrict /metrics so that only the Prometheus servers or scrape agents can connect to it, either through a separate listener, a firewall rule or a security group that admits the monitoring subnet alone. Verify from an application subnet and from a user network that the request is refused. Confirm whether your deployment method allows a separate metrics port.

Network isolation for vLLM nodes

Keep vLLM hosts, including every node in a multi-node deployment, on a private subnet with firewall or security group rules that allow only required ports. The vLLM security docs state that inter-node traffic is insecure by default, so isolation is a requirement rather than an option. Review rules after each change to node counts or ports.

Label hygiene for metric series

Keep prompts, user identifiers, API keys, tenant secrets and request content out of metric labels, since each distinct label value creates a new time series that is stored and may be exposed. Use coarse labels such as model name or route instead. Review your Prometheus configuration and any relabeling rules to confirm that no sensitive field reaches a label.

SSO and roles for dashboards and alerts

Restrict Grafana and Alertmanager with SSO through your identity provider and map groups to read-only, editor and administrator roles. Limit who can silence alerts or change routing, because those actions can hide capacity problems. Confirm that anonymous access is disabled and that dashboard and data source permissions match your team structure.

Scrape credentials in a secrets manager

If your Prometheus scrape setup uses a bearer token, basic authentication or client certificate, store that credential in a secrets manager rather than in configuration files or container images. Rotate it on a schedule you define and confirm that Prometheus reloads it without exposing it in logs. Ask who can read the secret and audit those reads.

How to test that vLLM alerts fire and metrics stay private

The test plan for vLLM monitoring should prove two things before anyone relies on the dashboards: that the signals move when the server is under known load, and that the metrics endpoint cannot be read from outside the trusted network. Both claims need evidence, and the evidence should be written down in a test record that the operations team can reuse. Start by agreeing the list of alert rules and dashboard panels in scope, then run each test against a non-production copy of the serving stack that matches the production configuration, including the reverse proxy and the Prometheus scrape job.

For positive tests, drive a load pattern with a load generator whose request count, token volume and concurrency you control. Confirm that the vLLM metrics at /metrics appear as series in Prometheus after the scrape interval, that request and token counters increase by roughly the amount the generator sent, and that dashboard panels for latency, throughput and KV cache use agree with the generator's own report. Then force each alert condition in turn, such as a queue backlog created by sending more concurrent requests than the server can schedule, or sustained high KV cache use with long prompts. For each alert, record the threshold, the observed value, the time from condition to firing, and the time from recovery to resolution. Illustrative example: if an alert is meant to fire after a sustained backlog, the record should show the backlog started, the alert went to the receiver, and the notification was received by the on-call channel.

For negative tests, run the checks from outside the private network through the public route. A request to /metrics should not return metric data, and the reverse proxy should return a denial such as a 403 or 404 that the team has chosen and documented. Repeat the check for the other control endpoints the vLLM security documentation says the API key does not protect, such as /health, /pause and /resume. Confirm the denial in the proxy logs, not only in the client output. The API key alone is not evidence of privacy because the documentation says it does not cover /metrics.

Failure mode tests matter as much as the happy path. Stop the vLLM process or block the scrape target and confirm that Prometheus marks the target down, that an up-style alert fires, and that dashboards show a gap rather than a flat line. Restart vLLM under load and check that counters reset to zero and that rate-based panels and alert expressions handle the reset without false alarms. Upgrade vLLM in a staging environment and compare the metric names and label sets before and after, because the vLLM metrics documentation should be checked for each release you adopt, and any renamed or new series will break dashboards or alert rules that reference the old names. Ask the vendor or the upgrade plan owner which metric changes each target version contains.

Write the test record as a handover artifact. For each test, include the environment version, the vLLM and Prometheus versions, the load generator settings, the expected result, the observed result, the evidence location such as a screenshot, query output or proxy log excerpt, the tester, and the date. Keep failed tests and the fixes that followed. Store the record with the alert rule files and dashboard definitions so that a later change can be checked against the same cases. The record should also name who reruns these tests after each planned upgrade and after any change to the proxy or network policy.

What drives the effort and cost of vLLM monitoring

This table lists the factors that change how much work vLLM monitoring takes and how to keep each one manageable. Read each row as a question to answer before scoping, since most of the effort comes from decisions made early about instances, labels, alert ownership and existing tooling. The main takeaway is that reusing an existing Prometheus stack and agreeing on alert ownership early usually matter more than the number of dashboards.

Driver	Why it changes effort	How to keep it down
Model instances and replicas	Each vllm serve process exposes its own /metrics endpoint, so scrape targets, dashboards and alert rules grow with every replica and model.	Template dashboards per model and use consistent job and model labels so one panel set covers all instances.
Nodes and accelerators	Multi-node runs add per-node exporters and GPU telemetry, and metrics from pipeline-parallel workers need correct mapping to the serving instance.	Confirm which processes expose vLLM metrics in your topology and scope GPU telemetry collection to the nodes that serve traffic.
Number of environments	Development, staging and production each need scrape configs, retention settings and alert thresholds, and drift between them creates false alarms.	Keep one parameterized configuration as code and vary only thresholds and endpoints per environment.
Integration with an existing observability stack	Reusing Prometheus, Alertmanager, Grafana or a long-term store is cheaper than standing up a parallel stack, but naming and label conventions must match.	Confirm the existing stack's scrape model, label conventions and dashboard tooling before designing new dashboards.
Compliance evidence needs	Assessors may ask for proof that metrics and alerts are retained, access-controlled and reviewed, which adds documentation and export steps.	Ask early which evidence the assessor expects, such as alert history or access records, and collect it from the existing stack.
Retention and cardinality	Histograms and per-model or per-user labels multiply series, raising storage cost and slowing queries. Configurable histogram buckets add a further choice.	Limit label sets to model and instance, review bucket settings with the team, and set retention per the use case (short for operations, longer for capacity planning).
Alert recipients and on-call workflows	Each alert needs an owner, a severity and a runbook, and unclear routing causes duplicate pages or missed incidents.	Agree alert ownership, severity levels and escalation paths before writing rules, and keep the recipient list in one place.
Metrics exposure and access controls	The /metrics endpoint is not covered by the vLLM API key, so exposing it beyond the monitoring network adds reverse proxy or network rules to build and test.	Keep /metrics on the internal monitoring network and verify access controls as part of the initial build.

What to monitor, back up and document once vLLM monitoring is live

Once vLLM metrics reach dashboards and alerts, the team's job shifts from building monitoring to keeping it trustworthy. Monitoring can fail silently: a Prometheus scrape target can drop out, storage can fill, or an alert route can break while the dashboards still look healthy. Treat the monitoring stack as a production service in its own right. Track scrape success for every vLLM replica, the number of active series and storage growth in Prometheus, and whether alerts actually reach an on-call channel. Schedule a periodic test that fires a known alert and confirms delivery end to end, and record the result in the runbook.

Store everything that defines monitoring as code under version control. That means the Prometheus configuration and scrape jobs, alert rules, recording rules, Alertmanager routing and receivers (with secrets kept in the client's secret store, not in the repository), and dashboard exports from Grafana or the team's dashboard tool. Export dashboards on a regular schedule and after each approved change, so a lost or edited dashboard can be restored to a known state. Confirm the backup and restore path works by restoring the configuration into a non-production Prometheus instance at least once before go-live is signed off.

Review alert thresholds after every vLLM minor release. The project ships new minor releases about every two weeks, and the researched release dates in 2026 were August 26, September 9, September 22 and October 5 for v0.28.0 through v0.31.0. Metric names, histogram buckets and label sets may change between releases, so an alert that referenced a renamed metric can stop firing without any error. Before rolling a new vLLM version, diff the exposed metric names against the confirmed list, check that every alert expression still returns data under representative load, and adjust thresholds where batching or scheduling behavior has changed. Ask the team that owns the upgrade process to make this review a required gate.

The runbook should record the vLLM version, container image digest or install source, model names and revisions, and the hardware each replica runs on, including GPU type, driver and CUDA or ROCm version, and the parallelism settings in use. Without this record, an alert that fires after an upgrade cannot be tied to a specific change. Document label conventions (for example, which labels identify model, replica, cluster and environment), alert ownership by service or team, severity definitions, and the escalation path from first responder to the model platform owner and to the hardware or networking team.

The handover package should contain five items: the version-controlled configuration files for Prometheus, alert rules and Alertmanager; the dashboard exports; the runbook; the test record showing scrape, storage and alert delivery checks and their results; and a list of confirmed metric names tied to the vLLM version they were verified against. Ask the implementation partner to state in the written scope who maintains each item after handover, how often the metric list is re-verified, and whether the review after each minor release is included in support or billed separately.

What teams ask before monitoring vLLM with Prometheus

How much effort does monitoring vLLM take compared with the rest of the deployment?

Monitoring is generally a smaller workstream than GPU procurement, model compatibility testing or gateway work. vLLM exposes Prometheus-format metrics at /metrics on its API server, so the data source already exists. The effort goes into the parts around it: a Prometheus environment that scrapes the right hosts, dashboards for latency, throughput and KV cache use, alert rules tuned to your traffic, and a notification path that someone answers. The main drivers are the number of vLLM instances, whether Prometheus and an alerting tool already exist, and how many teams need their own views. Ask any partner to show the scope as a written list of dashboards, rules and alert channels so you can compare it with your own estimate.

Can our in-house platform team set up vLLM monitoring without a partner?

Yes, a capable platform team can do it, provided it has someone who knows Prometheus well and time to tune alerts against live traffic. The hard part is rarely the scrape configuration. It is deciding which signals matter for your models, setting thresholds from observed behavior, and keeping rules correct as vLLM changes. vLLM ships new minor releases about every two weeks, so whoever owns monitoring must review release notes and recheck dashboards after upgrades. Teams new to Prometheus often underestimate alert noise. A partner makes more sense when the team lacks that skill or capacity, or when it wants a fixed-scope deliverable with documentation and a handover. Confirm your in-house skills and available hours before deciding.

What does vLLM monitoring work need from our team before it starts?

The work needs four things from your side. First, access to each vLLM host, including confirmation that the server runs with metrics enabled and that the endpoint is reachable from the monitoring network. Second, a Prometheus environment you run, or permission to deploy one, with rights to add scrape targets and recording and alerting rules. Third, an alert channel such as your paging or chat tool, with credentials or webhook access. Fourth, named owners who receive alerts, approve thresholds and decide what a page means for your service. Without named owners, technically sound alerts often go unanswered. Expect to provide a test window, a change approval process and a contact for the GPU or infrastructure team, since many alerts originate outside vLLM itself.

How long does vLLM Prometheus monitoring take to set up?

Duration depends on scope, and no single figure fits every environment. A few vLLM instances with existing Prometheus and alerting form a narrow engagement. Duration grows with the number of serving instances, the number of models, whether Kubernetes service discovery is involved, how many alert channels must integrate, and how many dashboards each team expects. Schedule risk usually comes from access approvals and from agreeing thresholds, which needs live traffic to tune. Engagements are normally fixed-scope, so the written scope should state deliverables and the timeline before work starts. Ask for the timeline as milestones tied to access and owner sign-off, and ask what changes if a dependency, such as network approval, slips.

Is vLLM monitoring a one-time project or ongoing work?

The initial build is one-time work: deploying the scrape setup, dashboards, alert rules and documentation. Several costs recur. Prometheus storage and retention, alert channel fees and on-call time continue indefinitely. Rules and dashboards need review whenever models, traffic patterns or vLLM versions change, and because minor releases arrive about every two weeks, metric changes are routine. Thresholds also drift as capacity grows. Ongoing support, upgrades and continuous testing can be covered by a retainer, separate from the build. When requesting a quote, ask which items are one-time and which recur, who pays for storage and hosting, and how many review cycles the price assumes.

Can we monitor vLLM metrics without exposing them to the public internet?

Yes. The --api-key option does not protect the /metrics endpoint, so the key alone cannot keep metrics private. The vLLM security documentation recommends a reverse proxy that allowlists only the endpoints meant for users. In practice, keep the metrics path on a private network segment, restrict access with firewall or security group rules to your Prometheus servers, and confirm that the public listener or gateway never forwards /metrics. If Prometheus runs in another cluster or cloud account, use private connectivity or an internal load balancer. Test from outside the network after every change, and confirm your network team's policy for internal scraping before building anything.

What is DeployOpen responsible for in vLLM monitoring, and what stays with our team?

DeployOpen is responsible for the work described in the written scope: configuring Prometheus scraping, building the agreed dashboards and alert rules, testing that alerts fire and resolve, documenting the setup and handing it over. Your team is responsible for the vLLM hosts, the GPU and infrastructure they run on, the Prometheus and alerting platforms you own, network approvals, the on-call rota, and decisions about what a page means for your service. Model behavior, application service levels and incident response remain your responsibility unless a separate retainer covers them. DeployOpen does not issue audit reports or certifications. If monitoring supports a compliance assessment, you prepare the evidence and independent auditors or certification bodies assess it. Write this split into the scope document before work begins.

How DeployOpen scopes, builds and hands over vLLM monitoring

DeployOpen plans, builds and documents Prometheus monitoring for vLLM inside your own infrastructure, using the metrics endpoint that vLLM exposes on its API server. Engagements are fixed-scope, so the metrics, dashboards, alert rules and access controls are agreed in writing before work starts.

How we scope

Confirm which vLLM instances, models and nodes the monitoring must cover in your environment.
Agree which metric families, such as request counts, token throughput, latency and KV cache use, are in scope.
Define how the /metrics endpoint is kept off public networks, and who may scrape it.
Record scope, deliverables and timelines in a written agreement before any work begins.

How we build

Configure Prometheus scrape jobs against each vLLM server's /metrics endpoint in your network.
Set histogram bucket boundaries for latency metrics to match the response times your applications expect.
Build dashboards for latency, throughput, KV cache pressure and queue depth, then review them with your team.
Write alert rules for sustained KV cache saturation and rising queue depth, and test each rule before handover.

How we hand over

Deliver the scrape configuration, dashboards and alert rules as versioned files in your repositories.
Document each metric, what it measures and the thresholds chosen, with the reasoning behind them.
Walk your operations staff through alert triage and capacity review using the delivered dashboards.
Provide a runbook covering how to add a new vLLM instance to scraping and update histogram buckets.

How we support

Review monitoring after each vLLM version change, since metric names can change between releases.
Tune alert thresholds from observed traffic patterns during the first operating period.
Offer ongoing support and continuous review as a retainer, scoped separately from the build.
Confirm with your team which retention period and storage costs apply to your Prometheus setup.