I’ve used both for my small SaaS. I ship a React front end, a Node API, and a Postgres database. We run on a tiny cluster. Nothing fancy. I care about two things: what users do, and why my app breaks at 2 a.m. If you’d like the blow-by-blow details, I put together a deep dive on PostHog vs Datadog that expands on the stories below.
Here’s the thing—you can use both. I do. But they shine in different moments.
Quick take
- PostHog helps me see user behavior. Funnels, recordings, feature flags, experiments.
- Datadog helps me keep the app up. Traces, logs, metrics, alerts.
If you also need laser-focused analytics on subscription revenue and account health, give Scout Analytics a look—it zeroes in on the financial side of user behavior.
I know, that sounds neat and tidy. It mostly is. But there’s overlap and a few gotchas.
My setup (so you know where I’m coming from)
- Front end: React with PostHog JS.
- Backend: Node/Express with Datadog APM and logs.
- Infra: K8s on DigitalOcean, Datadog Agent on nodes.
- I tried PostHog self-hosted for three months. Then I moved to PostHog Cloud. Fewer upgrades. Fewer sighs.
Coffee, dashboards, and a little panic
I love dark mode dashboards. I hate surprise alerts. I’m not a big-team person; I’m the person who builds the feature and then fixes the oops. So I want tools that make sense fast.
Where PostHog won for me
- The funnel that saved my onboarding: I tracked “Signup Started,” “Email Verified,” and “Project Created.” The funnel showed a 42% drop on mobile Safari. Yikes. Session recordings made it clear: the “Continue” button jumped on resize. It looked tap-able, then moved. We fixed the CSS. Conversion went up 9% that week. Felt good.
- Heatmaps I actually used: Our “New Project” page had a dead zone. People kept clicking a label. Not the button. I turned the label into a button. Clicks moved where I wanted. Less rage clicking. Fewer “I’m stuck” emails.
- Flags and small tests: I shipped a new sidebar with a feature flag. I rolled it to 10% of users first. I ran a simple experiment. Users who saw the new sidebar created 13% more projects. Not earth-shaking. But enough to keep it.
- Nudge timing: PostHog funnels showed drop-offs at “Connect Git.” I used in-app surveys to ask, “What’s blocking you?” Top answer: “I don’t have permissions.” I added a one-line tip and a help link. Drop-off shrank. Support tickets dipped too.
If you think funnels only matter in B2B SaaS, remember that every swipe-heavy consumer app lives or dies by that first-minute hook. For a fun (and slightly NSFW) reminder of how aggressive the competition gets, check out this unapologetic roundup of “download-tonight” hookup apps—thumbing through the examples shows just how polished and friction-free modern onboarding has become, and it might spark a few ideas for tightening your own activation flow. Likewise, if you’ve ever wondered how hyper-local discovery directories translate clicks into foot traffic, take a quick look at Rubmaps’ Winter Park listings—they demonstrate how clear categorization and user-generated ratings can nudge undecided visitors toward booking, offering inspiration for structuring your own product pages.
Curious how PostHog stacks up against more tour-oriented tools? I compared the two in my write-up on Pendo vs PostHog—worth a look if you’re eyeing in-app guidance.
PostHog feels like product sense in a box. It doesn’t fix code. It makes your screens better.
Where Datadog won for me
- The slow endpoint I couldn’t see: Datadog APM showed p95 for GET /projects was 1.8s. Not great. Traces pointed at one bad query with an N+1 pattern on tags. I added an index and fixed the join. p95 dropped to 320ms. Users felt it right away.
- The memory leak night: At 1:12 a.m., an alert: memory climbed for the worker pod. The flame graph gave me a hint—image processing hung on rare files. We swapped the library and set a time limit. The graph went flat. I slept.
- Log search that actually helped: A customer said, “Exports fail sometimes.” I filtered logs by user ID and route. Found a spike of 504s from our storage service. The link timed out on big CSVs. I bumped the timeout and added backoff. The errors vanished.
- Synthetics caught my cron going stale: I set a heartbeat check on a daily job. One day it missed. The alert hit Slack. I’d changed a cron expression and gave it the wrong schedule. Simple fix. Big save.
If your decision is really between product analytics and an error-first platform like Sentry, my field notes on PostHog vs Sentry might help clarify the trade-offs.
Datadog feels like the control room. Lights, gauges, alarms. Not cute—just clear.
Where each one bugged me
- PostHog
- Self-hosting was heavy. Upgrades ate time. I moved to Cloud.
- Event names matter. If you name stuff badly, funnels get messy.
- Session recordings can feel creepy. I keep them masked and strict.
- Datadog
- Price creep is real. Logs ballooned one month after a bad release. My bill did too.
- High-cardinality metrics can make dashboards slow. Keep tags tight.
- First setup has a lot of knobs. Great power, but you can click yourself into a maze.
Money talk (yes, it matters)
- PostHog: Pay by events and recordings. For me, this was steady. Feature flags and experiments felt “free” since they ride on events.
- Datadog: Pay by hosts, APM, logs, and how long you keep them. Logs hurt if you don’t trim them. I now sample noisy logs and drop debug in prod.
You know what? A simple rule helped: keep what you need now, archive the rest.
Who should choose what?
- If you care most about onboarding, funnels, and UI changes: pick PostHog first.
- If you care most about uptime, errors, and deep code issues: pick Datadog first.
- If you run paid ads and want to track user paths into product: PostHog makes it clear. I even stacked it up against the old standby in PostHog vs Google Analytics to see which told the richer story.
- If you run a busy API with many services: Datadog keeps your head on straight.
I still think both is best if you can.
How I use both together (my playbook)
- PostHog sends me product signals: “Users drop here.” “They click that.” I change copy, adjust layout, and try flags.
- Datadog tells me if the change broke stuff: “CPU spiked.” “This trace got slow.” I roll back fast if needed.
- I hook Datadog alerts to Slack. I check PostHog dashboards over coffee.
For anyone itching to wire these dashboards together, integrating PostHog and Datadog can give you that single pane of glass over both user behavior and system performance. Tools like n8n let you build no-code automations between the two, while Pipedream can trigger serverless workflows that react to events from either side.
It’s a loop. Watch users. Ship. Watch systems. Repeat.
Tips I wish I knew on day one
- Name events with verbs: “Project Created,” not “CreateProjectModal.”
- Use PostHog cohorts for messy groups, like “Mobile Safari in EU.”
- Mask sensitive fields in session recordings. Err on the safe side.
- In Datadog, set log sampling before a big launch. Your wallet will smile.
- Add one SLO per key flow. Keep alerts tight. No alert storms.
- Tag traces with user IDs (hashed) and plan level. Debug gets easier.
Little things that made me smile
- PostHog’s “Paths” showed a weird loop. Users bounced between Pricing and Docs. I added a short FAQ on the Pricing page. That loop calmed down.
- Datadog’s RUM caught a front end error only on old iPads. I added a small polyfill. Crash rate dropped to near zero. My mom’s iPad could use the app again. She told me.