Datadog
Observability and security across infrastructure, applications and logs
1/3Datadog is a hosted monitoring platform that collects metrics, traces and logs from servers, containers, cloud services and applications and puts them on shared dashboards, so an on-call engineer can move from a graph to the trace and then the log line behind it without changing tools. An agent runs on the host or in the cluster, and more than a thousand built-in integrations pull in the rest. It is sold as modules rather than one price: Infrastructure Monitoring per host, APM per host, Log Management by ingested gigabyte and indexed event, plus Real User Monitoring, Session Replay, Synthetic Monitoring, Database Monitoring, Cloud Cost Management, CI Visibility and Test Optimization. A security line sits alongside, covering Cloud Security, Cloud SIEM, Code Security and workload protection. It is aimed at teams running distributed systems in the cloud. Pricing is per module and scales with hosts, ingested volume and retention, so the bill is worth modelling before committing.
- Metrics, traces and logs on shared dashboards
- Agent runs on hosts, containers and Kubernetes
- 1,000+ built-in integrations
- APM with distributed tracing and a continuous profiler
- Log Management priced apart for ingest and indexing
- Real User Monitoring and Session Replay for front ends
- Synthetic Monitoring runs scripted browser and API checks
- Cloud Security, Cloud SIEM and Code Security modules
Based on what people have said
Every answer links back to the mentions it came fromWhat are the common criticisms regarding Datadog's pricing?
Several users note that the service is expensive. Additionally, some report that forecasting costs is difficult because the bill is driven by telemetry volume rather than infrastructure changes, which is often not monitored as closely as infrastructure itself.
2 mentions · @brankopetric00 · r/FinOps · Aug 2026 – Sep 2026
How do users utilize Datadog for troubleshooting and performance optimization?
Users employ Datadog to trace logic across services, identify bottlenecks during load tests, and investigate issues as part of triage workflows. It is frequently integrated with other tools to help automate the identification and resolution of system spikes.
3 mentions · Hacker News · @karrisaarinen · @GreggLuskin · Aug 2026 – Sep 2026
How does Datadog compare to other monitoring or analytics tools?
Some users suggest that while Datadog is a standard for infrastructure and LLM trace correlation, other tools like UptimeRobot are necessary to provide an external perspective on user accessibility. Others compare it to Posthog, noting that Posthog is viewed as a strong alternative for AI observability.
3 mentions · r/LangChain · @uptimerobot · Sep 2026
What is the general sentiment regarding the Datadog recruitment process?
Candidates who have received offers from Datadog describe the recruitment process as professional and fast, with some individuals choosing to accept these offers over other opportunities.
4 mentions · r/leetcode · Sep 2026
These summarise counts and dates only. Read the quotes themselves under what people are saying.
What people are saying about Datadog
47% positive“Posthog, because it never tried to reinvent the wheel, it was already a great analytics tool that now also has first-class support for AI observability - akin to datadog”
“* Pros: Makes a lot of sense if you're already on Datadog, especially for correlating LLM traces with the rest of your infrastructure.”
“Worth having one check that lives outside your stack. Datadog explains why. UptimeRobot tells you whether users can reach you at all.”
“native hooks: github, datadog, new relic, splunk”
“Datadog: $3,200 per month, great UI, still expensive”
“📊 Visualize all agent activity in Datadog, Grafana, or Langfuse with OpenTelemetry.”
“One time I had to do a system optimisation to increase the throughput of messages and was using Claude with Datadog and Couldwatch MCPs to figure out the bottlenecks by running load tests.”
“Getting an offer from Datadog makes that decision look even better”
“Honestly, withdrawing and taking the Datadog offer sounds like the right call. At some point the interview process itself becomes a red flag about the company”
“Congrats on the Datadog offer. Their recruiters on the other hand seem very professional and fast.”
“I withdraw my candidacy after that and accepted an offer with DataDog.”
“The team set it up as part of triage: it connects to Datadog and Sentry to investigate issues, then assigns them to Linear’s coding agent to fix”
“Datadog spend is one of the harder things to forecast because the bill doesn't move when your infrastructure changes, it moves when your telemetry changes, and almost nobody watches telemetry the way they watch infra.”
“If you're using our Private Action Runner with the Datadog Agent, it'll boot about 750x faster now – thanks Ilya!!”
“Hermes just connected itself to 44+ more services Cloudflare Datadog Metabase GitLab Railway DeepWiki And many more”
“Excited to partner with @FactoryAI to make Droid even more powerful for users by connecting to @datadoghq.”
“Then our agents picked up the spike, used PlanetScale Insights MCP and Datadog MCP to trace the logic across services, and fixed it.”