Dynatrace is what happens when you throw 20 years of engineering at the observability problem. APM, infrastructure monitoring, log analytics, security, digital experience, business analytics, AI ops. They’re publicly traded (NYSE: DT), they’re a Gartner Leader, and they serve companies like Air Canada and Virgin Money.
We built a desktop app and a background worker that SSH into servers and read logs.
I’m not going to pretend these are equivalent products. They’re not. But if you’re an engineer at a company that doesn’t have Dynatrace’s budget, or you already have Dynatrace and your investigation still takes 30 minutes, the comparison is worth making.
What Dynatrace brings to the table
Dynatrace’s core advantage is their Davis AI engine. It combines causal AI (modeling actual cause-and-effect relationships between components), predictive AI (forecasting problems before they happen), and generative AI (natural language answers to operational questions). When something breaks, Davis traces the root cause through the dependency graph automatically.
That’s genuinely impressive. The causal AI approach means Dynatrace models which component actually caused the problem, not which metrics moved at the same time. For large, complex environments with hundreds of services, this kind of automated reasoning is hard to replicate.
They also cover everything: application performance, infrastructure health, log analytics, security vulnerabilities, user experience monitoring, CI/CD pipeline observability. If you buy Dynatrace, you’re replacing 5-10 other tools.
The pricing reflects the scope. Dynatrace uses consumption-based pricing (DPS units) that scales with the volume of data you ingest. Enterprise deployments typically run six figures annually.
Where we fit
We didn’t try to build an observability platform. There are no dashboards, no service maps, no APM traces, no security scanning. We built one thing: investigate incidents by connecting to servers over SSH and having AI interpret the logs.
SuperTerminal runs on your machine. You describe what’s wrong, it SSHes in and runs diagnostic commands, the AI reads the output and tells you what caused the problem. Five minutes to set up. Uses your existing SSH config. You pick the AI provider.
We also sell a separate product, the BitSentry Dashboard, which is a web app with a 24/7 background worker that does this continuously by ingesting signals from Sentry, Wazuh, and similar sources.
The actual comparison
| Dynatrace | BitSentry | |
|---|---|---|
| Scope | Full-stack observability + security + business analytics | Incident investigation + ops automation |
| AI approach | Causal AI (Davis engine, models dependencies) | LLM interpretation (reads logs, reasons about output) |
| Setup | OneAgent deployment across infrastructure | SSH config + AI API key, 5 minutes |
| Root cause method | Dependency graph tracing with causal modeling | Sequential SSH investigation with AI interpretation |
| Data collection | Continuous (OneAgent, OpenTelemetry) | On-demand (SSH) or triggered (Dashboard worker) |
| Target company | Enterprise (Fortune 500, complex multi-cloud) | Any team with servers and SSH access |
| Pricing | Consumption-based, typically $100k+/year for enterprise | $7,200/year flat |
When Dynatrace makes sense
Your company has the budget and the infrastructure complexity to justify it. If you’re running hundreds of services across multiple clouds, Dynatrace’s automated dependency mapping and causal AI earn their keep. You can’t manually reason about a system that large, and Dynatrace’s OneAgent captures signals you’d never instrument yourself.
You need unified observability. If you’re currently paying for Datadog plus Grafana plus PagerDuty plus a security scanner and you want to consolidate, Dynatrace replaces all of them.
You’re an enterprise that needs vendor support, compliance certifications, and a sales team that answers the phone. Dynatrace has been doing this since 2005.
When our tools make sense
You don’t have six figures for observability tooling. Most teams under 200 engineers don’t. We charge $7,200/year and cover the investigation piece that takes the most engineer time.
You already have observability tooling and the problem is the last mile. Dynatrace (or Datadog, or whatever you use) detected the problem. The dashboard shows something is wrong. But you still need to dig into the server to understand why. SuperTerminal handles that dig.
Your infrastructure is simple enough that you don’t need causal AI across hundreds of services. If you have 10-50 servers and the root cause usually lives in application logs or server configuration, SSH investigation gets you there without modeling the entire dependency graph.
You want the AI to read your actual logs. Dynatrace’s Davis AI works on the data Dynatrace collected. Our AI works on whatever is on the server, including data that was never instrumented.
Try SuperTerminal
Free while in beta. Uses your SSH config and your own AI keys. Set it up in 5 minutes.