Dynatrace ExploreOpenPipeline

Name: Dynatrace ExploreOpenPipeline
Brand: Dynatrace
Rating: 4.6 (2353 reviews)

Leader

Cloud done right.

Est 2005|Valuation $18.4B|Raised $2.1B

LinkedIn 413K|Website|Web traffic 1.3M|FTEs 5,200|HQ Boston, Massachusetts, US

MarketsInfrastructure Monitoring Observability Platform

Dynatrace ExploreOpenPipeline screenshot

01Competitive Landscape

Narrative analysis

**Dynatrace vs Datadog** - Datadog provides published list pricing across modules, but community threads also raise concerns about pricing surprises at scale. - Dynatrace is commonly selected when buyers prioritize strong auto-discovery/topology mapping and AI-assisted root-cause analysis and are prepared for an enterprise-level rollout. **Dynatrace vs New Relic** - New Relic emphasizes more transparent pricing and can be easier to start with; enterprises still commonly negotiate for larger deployments. - Dynatrace’s differentiator in some accounts is platform cohesion (agent + topology + AI + ingest processing) for large estates. **Dynatrace vs Splunk Observability Cloud** - Splunk provides packaging for Observability Cloud and has roots in logs and security ecosystems; total cost depends on scale and portfolio choices. - Buyers selecting Dynatrace may prefer an agent-first platform and integrated problem workflows; buyers selecting Splunk may prioritize integration with broader security and data platforms. **Competitor pricing and public pages** - Datadog, New Relic, and Splunk publish public pricing information and FAQs; details should be validated for large-scale scenarios.

	Dynatrace OpenPipeline (within Dynatrace)	Datadog (platform + pipelines)	New Relic (platform + data management)
Starting Price	Dynatrace pricing is conceptually published but typically sales-led and negotiated	Public module pricing exists (published usage-based and product-specific pricing)	Public pricing page with tiered and usage-based models
Recommended for	Enterprises seeking governed ingest and contextualization integrated with topology/AI workflows	Teams that prioritize a broad integration ecosystem and a SaaS UX	Organizations seeking easier entry with published tiers and querying capabilities
Key Strength	Ingest processing integrated with platform context (topology/AI) and ingest-level masking/filtering/routing	Large integration ecosystem and strong analytics; wide adoption	Unified platform with a transparent pricing approach for many entry use cases
Key Weakness	Pricing and contract opacity; learning curve; verify billing impact of transforms	Pricing can become unpredictable as usage scales; onboarding complexity for large-scale use	Feature depth across enterprise modules can vary depending on specific needs

01Competitive Landscape

Narrative analysis

Dynatrace vs Datadog

Datadog provides published list pricing across modules, but community threads also raise concerns about pricing surprises at scale.
Dynatrace is commonly selected when buyers prioritize strong auto-discovery/topology mapping and AI-assisted root-cause analysis and are prepared for an enterprise-level rollout.

Dynatrace vs New Relic

New Relic emphasizes more transparent pricing and can be easier to start with; enterprises still commonly negotiate for larger deployments.
Dynatrace’s differentiator in some accounts is platform cohesion (agent + topology + AI + ingest processing) for large estates.

Dynatrace vs Splunk Observability Cloud

Splunk provides packaging for Observability Cloud and has roots in logs and security ecosystems; total cost depends on scale and portfolio choices.
Buyers selecting Dynatrace may prefer an agent-first platform and integrated problem workflows; buyers selecting Splunk may prioritize integration with broader security and data platforms.

Competitor pricing and public pages

Datadog, New Relic, and Splunk publish public pricing information and FAQs; details should be validated for large-scale scenarios.

	Dynatrace OpenPipeline (within Dynatrace)	Datadog (platform + pipelines)	New Relic (platform + data management)
Starting Price	Dynatrace pricing is conceptually published but typically sales-led and negotiated	Public module pricing exists (published usage-based and product-specific pricing)	Public pricing page with tiered and usage-based models
Recommended for	Enterprises seeking governed ingest and contextualization integrated with topology/AI workflows	Teams that prioritize a broad integration ecosystem and a SaaS UX	Organizations seeking easier entry with published tiers and querying capabilities
Key Strength	Ingest processing integrated with platform context (topology/AI) and ingest-level masking/filtering/routing	Large integration ecosystem and strong analytics; wide adoption	Unified platform with a transparent pricing approach for many entry use cases
Key Weakness	Pricing and contract opacity; learning curve; verify billing impact of transforms	Pricing can become unpredictable as usage scales; onboarding complexity for large-scale use	Feature depth across enterprise modules can vary depending on specific needs