groundcover vs. Dynatrace

Compare groundcover vs. Dynatrace for Observability. We want you to choose the most suitable tool for your use case, even if it’s not us.

As cloud-native environments continue to grow in complexity, observability has become essential for ensuring the reliability, performance, and scalability of modern applications. From monitoring infrastructure health, enabling deep visibility into distributed systems, or getting real-time insights into reasoning paths, token usage of LLM Agentic applications. However, traditional vendors sliced visibility into separate products (APM, Log Management, Infrastructure Monitoring, LLM Observability) and priced them in ways that forced tradeoffs making it important for team to choosing the right observability platform is critical to operational success.

groundcover and Dynatrace each bring unique strengths to observability, with distinct capabilities and trade-offs. The best fit depends on your organization’s priorities—whether that’s cost efficiency, deployment flexibility, developer experience, or ecosystem integrations.

The right choice depends on your priorities: cost, control, scale, and flexibility. In the following sections, we’ll compare both platforms to help you determine which best fits your needs, even if the answer isn’t us.

groundcover vs. Dynatrace at a glance

groundcover
Dynatrace
BYOC (Data Residency, Compliance, Security)
On Prem (Data Residency, Compliance, Security)
Correlation between logs, metrics, traces
Kubernetes native
Limited
Full HTTP Payloads of Request as well as response including headers
Smart Sampling

groundcover vs. Dynatrace at a glance

groundcover
Dynatrace
RBAC for user roles
RBAC for Pages
RBAC for Actions
Fine-grain access control to data/resources (namespace setup support) to provide access to specific teams or by data type (MELT)

groundcover vs. Dynatrace at a glance

groundcover
Dynatrace
All data types included in standard plan
APM, Logs, LLM, RUM, Infrastructure Monitoring all sold separately
No Ingestion based pricing
No ingestion based fees (ingestion depends on own storage)
Additional costs based on retention
No retention pricing. Keep as much as you want
Costs increase when retention policies applied
Additional costs for indexed data
All data is indexed at no additional costs
Additional price bands for hot/cold storage
Out of the box LLM Observability (tokens, hallucinations, drift)
(Requires the installation of an additional agent; token count only)
LLM Observability: No Additional Cost
RUM
RUM: No Additional Cost
(Additional charges are incurred by events + sessions)

groundcover overview

groundcover is a full-stack observability platform that goes beyond traditional SaaS limitations by giving teams complete control of their telemetry in their own cloud. It delivers APM, log management, infrastructure monitoring, LLM observability, and Real User Monitoring (RUM) in a single, unified solution — all with zero instrumentation, powered by eBPF.

With a BYOC (Bring Your Own Cloud) model, groundcover eliminates SaaS markups and data tradeoffs, providing complete visibility, infinite retention, and flat, predictable pricing. From infrastructure to applications to Agentic Application workloads, groundcover captures every signal — without sampling or rate limits — and keeps it private and secure inside your VPC. Whether running in the cloud, on-prem, or in regulated environments, groundcover gives engineering teams operational simplicity, cost clarity, and observability that just works, without compromise.

Dynatrace overview

Dynatrace is a software-intelligence platform that provides observability, automation, and security for cloud environments. It offers application performance monitoring (APM), infrastructure monitoring, digital experience monitoring (DEM), log management, application security, and AIOps in a single system. Dynatrace uses a single agent, called OneAgent, to automatically instrument applications, services, and infrastructure. Its Smartscape technology maps dependencies across environments, while the Davis AI engine analyzes telemetry to detect anomalies, identify root causes, and trigger automated responses. The platform also includes Grail, a data lakehouse designed for storing and analyzing observability and security data with contextual insights. Organizations use Dynatrace to monitor complex IT environments, reduce time to resolution, and automate operational tasks across the software delivery lifecycle.

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