What Even Is HyperDX?
If you’ve ever stared at a wall of logs trying to figure out why your app is slow, you already know the pain. Datadog costs a fortune. Grafana’s setup makes you question your career choices. And Splunk? Let’s not even go there.
HyperDX showed up on my radar about six months ago when a friend on a mid-size SaaS team mentioned they’d ditched Datadog for it. I was skeptical — open source observability tools have a track record of being either half-baked or requiring a PhD in YAML to configure. But after running HyperDX on two production services for the last few months, I have thoughts.
The Setup Experience
Getting HyperDX running took me about 20 minutes with their Docker Compose setup. That’s it. No Helm charts, no Kubernetes operator, no “please configure your fleet of 14 microservices first.” You pull the repo, run docker compose up, and you’ve got a working observability stack.
The stack includes:
- ClickHouse for storage (fast columnar queries on log data)
- OpenTelemetry Collector for ingestion
- A web UI that actually looks like it was designed in this decade
- MongoDB for metadata and saved searches
I had my Node.js API sending traces and logs within an hour. The OpenTelemetry SDK integration is straightforward — if you’ve used any OTel-compatible tool before, this feels familiar. If you haven’t, their docs walk you through it without assuming you already know what a span is.
The UI: Where HyperDX Actually Shines
Here’s what surprised me most: the search interface is genuinely good. You type a query, and results come back fast. Not “fast for an open source tool” — actually fast. Sub-second on most queries across millions of log lines.
The session replay feature links your frontend errors directly to the backend traces that caused them. I found a bug in our checkout flow in about 3 minutes that would have taken an hour with separate tools. You click on a user session, see exactly what they did in the browser, then drill down into the API calls and database queries behind each action.
The correlation between logs, traces, and metrics happens automatically. When you’re looking at a slow API endpoint, you can jump from the trace to the relevant logs to the infrastructure metrics without manually building dashboards or writing queries. It just works.
How It Compares to the Big Names
HyperDX vs Datadog
Datadog is the 800-pound gorilla, and for good reason — it does everything. But “everything” comes at a price. A small team running a few services can easily hit $500-1000/month on Datadog. HyperDX is free to self-host. You’re paying for the server it runs on (a decent VM costs $50-100/month) and your own time maintaining it.
Feature-wise, Datadog has more integrations, better APM, and a more mature alerting system. HyperDX’s alerting is basic — you can set up threshold alerts on log patterns and metrics, but don’t expect the ML-powered anomaly detection Datadog offers. For most small-to-mid teams though, “alert me when error rate goes above 5%” is all you actually need.
HyperDX vs Grafana Stack (Loki + Tempo + Prometheus)
The Grafana ecosystem is powerful but fragmented. You need Loki for logs, Tempo for traces, Prometheus for metrics, and Grafana for the UI. Each has its own config, its own scaling concerns, its own quirks. HyperDX bundles all of this into one thing.
Grafana’s advantage is flexibility — you can swap out any component, scale them independently, and customize everything. HyperDX trades that flexibility for simplicity. If you want to get observability running this week instead of this quarter, HyperDX wins.
HyperDX vs SigNoz
SigNoz is the closest competitor in the open source space. Both use ClickHouse, both support OpenTelemetry natively, both have modern UIs. The differences are subtle: HyperDX’s session replay is more polished, SigNoz’s dashboarding is more flexible. I’d say HyperDX is slightly easier to get started with, while SigNoz gives you more control once you’re up and running.
What’s Actually Missing
Let me be real about the gaps:
- No real APM — you get traces, but no automatic service maps, dependency graphs, or performance baselines like you’d get in Datadog or New Relic
- Limited alerting — basic threshold alerts work, but there’s no PagerDuty/OpsGenie integration out of the box (you can hack it with webhooks)
- Scaling is on you — if you’re pushing 100GB/day of logs, you need to figure out ClickHouse scaling yourself. The docs don’t cover this well
- Single-node default — the Docker Compose setup is single-node. For production HA, you’ll need to design your own deployment
- The community is small — their GitHub has about 7k stars, and the Discord is active but not huge. If you hit an edge case, you might be on your own
Who Should Actually Use This?
HyperDX fits a specific sweet spot:
- Startups and small teams who can’t justify $500+/month on Datadog but need more than console.log debugging
- Teams already using OpenTelemetry who want a backend that’s easy to set up
- Developers who want full-stack visibility (frontend to backend) without stitching together three different tools
- Self-hosters who are comfortable running Docker in production and don’t mind occasional maintenance
If you’re an enterprise with a dedicated SRE team and a budget, Datadog or the Grafana stack will serve you better long-term. If you’re a 5-person startup shipping fast and need to debug production issues without going broke, HyperDX is worth trying.
My Setup After 3 Months
I’m currently running HyperDX on a $60/month Hetzner dedicated box (32GB RAM, 8 cores, 500GB NVMe). It handles about 5GB of logs per day from two services plus frontend session recordings. ClickHouse barely breaks a sweat.
I’ve got it ingesting:
- Application logs via the OpenTelemetry SDK
- Nginx access logs via Fluentd
- Frontend errors and session recordings via their browser SDK
- Basic infrastructure metrics via the OTel host metrics receiver
Total monthly cost: $60 for the server. Compare that to what Datadog would charge for the same volume, and you start to see why people are interested.
The Bottom Line
HyperDX is the best open source observability platform I’ve used for small teams. It’s not trying to be Datadog — it’s trying to be the observability tool you can actually set up and use without a dedicated platform team. And at that, it succeeds.
The session replay feature alone sets it apart from most alternatives. The search is fast, the UI is clean, and the OpenTelemetry integration means you’re not locked in. If it doesn’t work out, your instrumentation carries over to whatever you try next.
Is it perfect? No. The alerting needs work, scaling docs are thin, and you’ll need some Docker/Linux comfort to keep it running. But for the price (free) and the value it delivers, HyperDX earns a strong recommendation from me. Give it a weekend — you might not go back to paying $1000/month for logs.
Is HyperDX Still Worth It in 2026?
We’ve updated this review to reflect the current state of HyperDX and its market position.
FAQ: Common Questions About HyperDX
Q: Is there a free trial?
A: Check the official website for current pricing.