Datadog already provides support. What does AceMQ add?

Datadog supports their platform, and they are good at it. They do not tune your tag cardinality, redesign your monitors, fix the tracer instrumentation in your services, or tell you which team's metrics are driving the bill — because those live in your code and your account, not in their platform. That is the work we do, on a 15-minute emergency SLA, alongside your Datadog support rather than replacing it.

Our Datadog bill is out of control. What can you actually do about it?

Start with attribution, because most teams cannot say which services or teams are driving spend. We break usage down by custom metric, log index, APM host, and span volume, then find the specific tags creating cardinality — usually one or two metrics carrying an unbounded dimension like user ID or pod name. From there it is exclusion filters, log index retention tuning, sampling configuration, and instrumentation fixes so the growth does not simply return next quarter.

Why did our bill jump without any infrastructure change?

Custom metrics are billed per unique combination of metric name and tag values, so the trigger is usually a code change rather than an infrastructure one. Someone adds a tag for user ID, request path, or container ID to an existing metric, and a metric with 200 series becomes one with 200,000. The same pattern happens with log ingestion when a service ships to debug level. We identify the exact change, cap it, and add guardrails so the next one is caught before the invoice.

How fast do you respond to a Datadog observability outage?

P1 emergencies — agents dark across a cluster, monitors silent, telemetry missing during a live incident — get a 15-minute first response with a senior engineer on a bridge. P2 critical issues are 1 hour, P3 high-priority 4 hours, and P4 standard questions next business day. These are contractual response times. We treat blind observability as P1 because it makes every concurrent incident harder to resolve.

Our agents stopped reporting after a Kubernetes upgrade. Can you fix that?

Yes, and it is a routine one. Kubernetes upgrades commonly break the Cluster Agent's RBAC when a resource type moves API version, change how the admission controller injects tracer libraries, or invalidate autodiscovery annotations after a container rename in the pod spec. The agent itself usually reports healthy throughout, which is why these take a while to notice. We diagnose against agent status output and the actual pod spec rather than the Helm values file.

Should we move off Datadog to Prometheus and Grafana?

Sometimes, and we will give you an honest answer because we run both in production. Datadog's value is real — correlated APM, logs, and infrastructure in one place with almost no operational overhead. That value stops scaling when a small number of very high-volume workloads dominate the bill, and those specific workloads are often much cheaper self-hosted. A hybrid split is frequently the right outcome rather than an all-or-nothing migration.

How is AceMQ Datadog support priced?

Support is an annual contract priced on estate size, number of instrumented services, required SLA tier, and coverage hours — never per incident or per ticket. It is separate from and additional to your Datadog subscription. For most customers the cost governance work alone returns more than the contract. Contact us with your environment details and we return a quote within 24 hours.

Datadog Support

24/7 Datadog Support — Cost Control, Agents & APM, 15-Min SLA

Datadog runs the platform. What most teams actually need help with is operating it: the custom metric cardinality that doubled the bill, agents that stopped reporting after a Kubernetes upgrade, traces that never link to their logs, and monitors that page eight people at 3am for something nobody acts on. AceMQ engineers work at that layer, on a 15-minute emergency SLA.

Senior Datadog engineers on call right now — 24/7/365

15 min emergency SLA24 /7 global coverage130 + enterprise customers26 + countries served

Available Now

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Trusted for mission-critical Datadog by teams in finance, healthcare, defense, and telecom

Escalation Path

Your first hour of a Datadog outage

Most vendors publish an SLA number. This is what actually happens, minute by minute, when you page a senior AceMQ engineer.

T+0

You page us

Phone, email, or Slack — any channel reaches the on-call senior engineer directly. No web form, no tier-1 queue.

T+15

Named engineer live

A senior engineer who already knows your environment joins a live bridge. Zero cold-start, no re-explaining your topology.

T+30

Root cause isolated

Direct broker access, log and metric review, and a working hypothesis with a rollback plan before we touch anything.

Post

Written RCA

Documented root cause, the fix applied, and the prevention steps — delivered after every P1, not just when asked.

Response Times

SLA tiers, contractually guaranteed

Every tier reaches a senior Datadog engineer. There is no tier-1 triage layer to get through.

P1 — Emergency

15 min

Production down, messages not flowing, cluster or broker failure

P2 — Critical

1 hour

Severe degradation, rising error rates, approaching capacity limits

P3 — High

4 hours

Performance issues, configuration problems, non-critical failures

P4 — Standard

Next day

Questions, guidance, best practices, non-urgent improvements

Incident Triage

Datadog problems we fix every week

These are real symptoms from real Datadog production environments — and the first thing our engineers check when one comes in.

Symptom you're seeingWhat we check firstTypical resolution

Bill jumped 40% this month with no infrastructure change

What we check firstThe custom metrics usage page, sorted by metric name. A team almost certainly added a tag carrying a user ID, request ID, or pod name — custom metrics bill per unique tag combination, so one tag on one metric can create hundreds of thousands of billable series overnight.

Typical resolutionUnder 2 hours

Agent healthy but a container's integration reports nothing

What we check firstThe agent status output for autodiscovery, then the pod annotations. Container checks are configured by annotation or label, so a renamed container in the pod spec silently detaches the check while the agent itself stays perfectly green.

Typical resolution1–2 hours

Log costs high while the logs you want are not searchable

What we check firstIngestion volume against indexed volume, then your exclusion filters. Ingest and index are billed separately, so it is entirely possible to pay to ingest terabytes of debug logs while the errors you actually query fall outside a retention filter.

Typical resolution1–3 hours

APM traces exist but do not link to the corresponding logs

What we check firstWhether trace_id and span_id are being injected into the log records. Correlation depends on those fields reaching the log pipeline in the format the remapper expects — custom JSON loggers and structured logging wrappers commonly drop them.

Typical resolution1–2 hours

The trace for the incident you are investigating is missing

What we check firstThe sampling configuration. Head-based sampling decides at the entry span, so low-rate errors are exactly what gets discarded. Error and rare-trace retention filters plus targeted trace sampling on the affected service fix this properly.

Typical resolution1–3 hours

Monitors paging repeatedly for conditions nobody acts on

What we check firstWhich monitors fired most over the last quarter and how many produced a real action. Alert fatigue is nearly always a handful of monitors with no evaluation window, no recovery threshold, and no downtime scheduled around known deploy windows.

Typical resolutionSame day

Agent DaemonSet running but cluster-level metrics missing

What we check firstCluster Agent RBAC. Kubernetes state metrics come from the Cluster Agent talking to the API server, so a ClusterRole that was not updated for a new resource type produces exactly this — node metrics fine, cluster metrics absent.

Typical resolution1–2 hours

Terraform plan wants to recreate monitors somebody edited in the UI

What we check firstWhich monitors have drifted and who owns them. Datadog resources managed in Terraform but edited in the console produce permanent diffs, and applying blindly reverts thresholds that an on-call engineer deliberately changed at 4am.

Typical resolutionUnder 2 hours

Resolution times reflect typical Datadog engagements under an active AceMQ support contract. Every P1 closes with a written root-cause analysis.

Not on the list? Tell us what's breaking

Support In Practice

Datadog problems we've already solved

Representative engagements showing how these incidents get diagnosed and closed under an AceMQ support contract.

All use cases

Remediation

Datadog Agent Telemetry Gap Remediation

Global Logistics Provider

Remediation of intermittent Datadog telemetry gaps traced to agent buffering, container lifecycle, and network egress behavior on the customer's own infrastructure.

DatadogKubernetesLinux

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Support

Datadog Monitor and Alert Noise Support

B2B SaaS Platform Provider

Ongoing support for a Datadog monitor estate producing more alerts than the on-call rotation could meaningfully act on.

DatadogKubernetesTerraform

Read case study

Assessment

Datadog APM Instrumentation Coverage Assessment

Global Payments Processor

Assessment of APM instrumentation coverage and trace completeness across a service estate where distributed traces kept breaking at service boundaries.

DatadogOpenTelemetryKubernetes

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What's Included

Everything in your Datadog support contract

No add-on pricing for incidents. No per-ticket charges. One contract covers the whole surface.

Cost Governance & Cardinality Control

Custom metric attribution by team and service, tag cardinality reduction, log ingest versus index tuning, and APM host and span commitment sizing. We find what is driving the bill and cut it without losing the signal you rely on.

Emergency Incident Response

Agents dark across a cluster, monitors silent, or telemetry missing during a live incident. A senior engineer joins a bridge within 15 minutes — losing observability mid-incident extends every other outage you are having.

Agent & Kubernetes Operations

DaemonSet and Cluster Agent deployment, autodiscovery annotations, Cluster Role and RBAC, admission controller injection, and the upgrade paths that quietly change how checks are configured.

APM, Tracing & Log Correlation

Tracer instrumentation, service and env tagging discipline, trace_id injection into logs, sampling and retention filters, and making sure the trace you need during an incident is one that was actually kept.

Monitor & On-Call Hygiene

Pruning monitors that never led to action, setting evaluation windows and recovery thresholds against real noise, scheduling downtime around deploys and maintenance, and routing so one failure pages one team once.

Migration Onto or Off Datadog

Consolidating from several tools into Datadog, or moving workloads to Prometheus and Grafana where the economics stop working. We run both stacks in production and will tell you honestly which one your case calls for.

Anywhere You Run It

We support Datadog wherever it's deployed

Cloud, Kubernetes, bare metal, hybrid, and air-gapped — including environments where you can't give us outbound network access.

Kubernetes (DaemonSet, Cluster Agent, Operator)OpenShiftAWS (EC2, ECS, EKS, Fargate, Lambda)Microsoft Azure (AKS, App Service, Functions)Google Cloud (GKE, Cloud Run)VMware vSphere & TanzuBare metal & on-premise hostsHybrid cloud estatesTerraform-managed Datadog resourcesOpenTelemetry Collector pipelinesDatadog APM, Logs, Infrastructure & RUMMulti-org and multi-region accounts

Why AceMQ

What you get that you don't get elsewhere

We Are Paid to Reduce Your Datadog Bill

Nobody selling you the platform has that incentive. Cardinality attribution, log index tuning, and sampling design regularly take six figures off an annual spend — and the savings are permanent because we fix the instrumentation, not just the filters.

Named Engineers, Zero Cold Start

The same senior engineers stay on your account. They know your tag taxonomy, your monitor structure, and which services are instrumented properly — so a P1 opens with diagnosis instead of orientation.

No Tier-1 Triage Layer

You reach a senior engineer directly by phone, email, or Slack. Nobody collects details to pass along, and there is no escalation approval standing between you and the person who can fix it.

We Work On Your Side of the Line

Datadog owns the platform and supports it well. Your agents, your tags, your instrumentation, your monitor design, and your bill sit on your side of that line. That is the side we work on, alongside vendor support rather than instead of it.

Vendor-Neutral on the Build-vs-Buy Call

We also run Prometheus and Grafana in production. When Datadog is the right answer we make it work properly; when a high-volume workload would be cheaper self-hosted, we say so and can move it.

Genuine Follow-the-Sun Coverage

Engineers across 26+ countries and every time zone. Your 3am incident is someone's mid-afternoon — no overnight skeleton crew, no waiting for a region to come online.

FAQ

Datadog support questions

Get Control of Datadog Before the Next Renewal

Whether you need emergency response tonight, a cardinality audit before renewal, or agents that stay reporting through the next cluster upgrade, AceMQ staffs every engagement with a named senior engineer. Support quotes returned within 24 hours.

Get in Touch

Talk to a Support Expert

Send us a message and we'll follow up within one business day — or book a free 30-min consultation directly.

305-204-2607

info@acemq.com

66 W. Flagler St. 9th Floor
Miami, FL 33130

Prefer to talk now? Call us directly or use the consultation tab to find a time that works.