# Dynatrace AI visibility in Observability & Monitoring

Canonical: https://devtune.ai/verticals/observability-monitoring/dynatrace

[Website](https://www.dynatrace.com/)

Updated: 2026-10-15T01:11:58.916899+00:00
Prompts: 25
Runs: 6


## Platforms

- chatgpt-search
- perplexity
- bing-copilot-search
- google-ai-mode
- google-ai
- xai-search

Rank: 4
Total brands: 14
Measured responses: 150
Presence percent: 15.333333333333332
Share of voice percent: 12.546125461254611
Average position: 24.176470588235293
Docs presence percent: 13.333333333333334
Blog presence percent: 3.3333333333333335
Brand mention percent: 44.666666666666664


## Profile

Overview: Dynatrace (NYSE: DT) is an AI-powered observability and analytics platform founded in 2005 in Linz, Austria, and headquartered in Waltham, Massachusetts. The platform unifies application performance monitoring, infrastructure observability, digital experience monitoring, log management, runtime application security, and business analytics in a single SaaS offering. At its core is the Grail™ data lakehouse, which stores logs, metrics, traces, and business events in context at massive scale, and Davis®, Dynatrace's proprietary AI engine combining causal, predictive, and generative intelligence. OneAgent enables automatic full-stack instrumentation without manual configuration. As of March 2025, Dynatrace serves approximately 4,100 enterprise customers across 105+ countries and has been named a Gartner Magic Quadrant Leader in observability platforms for 15 consecutive years.
Product summary: Dynatrace is an enterprise-grade, AI-powered observability platform that provides unified full-stack monitoring across applications, infrastructure, digital experience, security, and business analytics. It combines the Grail™ data lakehouse, Davis® AI (causal + predictive + generative), and OneAgent auto-instrumentation to deliver automatic dependency mapping, anomaly detection, and root-cause analysis at cloud scale. The platform supports cloud-native, hybrid, and on-premises environments and integrates with 800+ technologies.


### Key capabilities

- AI-powered observability via Davis® causal, predictive, and generative AI (Davis CoPilot)
- Grail™ unified data lakehouse storing logs, traces, metrics, events, and business data in context
- OneAgent auto-instrumentation for full-stack, zero-config monitoring
- Application Performance Monitoring (APM) with distributed tracing and code-level profiling
- Infrastructure Observability across multi-cloud, hybrid, and on-premises environments
- Digital Experience Monitoring with Real User Monitoring, Session Replay, and synthetic testing
- AI Observability for GenAI applications, LLMs, and AI agents
- Runtime Application Security with vulnerability detection and threat protection
- Business Observability connecting IT telemetry to business KPIs and SLOs
- Cloud automation and AIOps-driven incident remediation workflows



### Target users

- Enterprise IT operations and SRE teams at large global organizations
- DevSecOps engineers managing cloud-native and hybrid environments
- Platform engineering and Kubernetes operations teams
- Application owners and digital experience teams
- CIOs and CTOs seeking unified observability and business analytics
- Security operations teams requiring runtime vulnerability management



### Key use cases

- Enterprise cloud and multi-cloud infrastructure monitoring
- Application performance management and root-cause analysis
- Kubernetes and cloud-native microservices observability
- Digital experience and end-user journey monitoring
- Log management and analytics at scale
- Runtime application security and vulnerability management
- AIOps-driven incident detection and automated remediation
- GenAI and LLM application observability

Integrations ecosystem: Dynatrace supports 800+ technologies via its Dynatrace Hub, spanning major cloud providers (AWS, Microsoft Azure, Google Cloud Platform), container orchestration (Kubernetes, Red Hat OpenShift, Docker), CI/CD tooling (GitHub, GitLab), observability standards (OpenTelemetry, Prometheus, W3C Trace Context), ITSM platforms (ServiceNow), AI model providers (Anthropic), and enterprise stacks (SAP, Oracle, .NET, Java). OneAgent auto-instrumentation reduces manual configuration burden. The platform supports CNCF projects including Keptn and OpenFeature. Strategic partnerships include new collaboration agreements with AWS and early-access integrations with Google Cloud. OpenPipeline enables telemetry ingestion and routing from third-party sources.
Pricing summary: Dynatrace uses a consumption-based Dynatrace Platform Subscription (DPS) model requiring an annual spending commitment with no published minimum. Unit prices decrease at higher commitment tiers. Published rate card examples include Full-Stack Monitoring at approximately $0.08 per GiB-hour, Infrastructure Monitoring at $0.04 per host-hour, and Log Management ingestion at $0.20 per GiB. Synthetic monitoring starts at $0.001 per request. A newer 'Retain with Included Queries' log pricing option offers fixed-cost retention and querying for up to 35 days. There is no free tier; a 15-day free trial is available. Enterprise pricing is negotiated and contract-based. Users report minimum commitments can run ~$20,000+/year for even modest deployments.
Review summary: Users consistently praise Dynatrace's AI-driven root-cause analysis, OneAgent auto-discovery, and comprehensive full-stack visibility, describing it as a market-leading tool for reducing alert noise and accelerating incident resolution in complex enterprise environments. The Grail data lakehouse and Davis AI are frequently cited as differentiators. Critical reviews center on high cost (particularly for smaller deployments), a steep learning curve, complex configuration, and occasional onboarding support gaps. Despite pricing concerns, enterprise users generally view Dynatrace as essential infrastructure with strong ROI. Gartner named it a Customers' Choice in the 2024 Voice of the Customer for Observability Platforms.
Competitive positioning: Dynatrace occupies the top tier of the enterprise observability market, consistently ranked highest in Ability to Execute in the Gartner Magic Quadrant for Observability Platforms for 15 consecutive years. Its primary differentiation is the combination of causal AI (Davis®), a proprietary unified data lakehouse (Grail™), and OneAgent auto-instrumentation—enabling automatic topology mapping, root-cause analysis, and anomaly detection at scale without manual configuration. Dynatrace targets the largest global enterprises (focused on 15,000 accounts with $1B+ revenues), distinguishing itself from Datadog's broader SMB-to-enterprise motion and from open-source-centric vendors like Grafana Labs and Elastic. Its full-stack, single-agent deployment model and AIOps automation depth set a high bar vs. Splunk (acquired by Cisco), New Relic's usage-based simplicity, and cloud-native specialists like Chronosphere. The Dynatrace Platform Subscription (DPS) consumption model is positioned as a transparent, scalable alternative to rigid per-host SKUs.
Limitations: Dynatrace is widely noted as expensive, particularly for smaller deployments where minimum annual DPS commitments can significantly exceed actual consumption needs. Users report a steep learning curve and complex initial configuration, especially for advanced features beyond OneAgent defaults. Billing granularity (memory rounded to quarter-GiB increments, ephemeral resources billed in 15-minute intervals) can create cost surprises. Some users note difficulty with user permission validation at scale and limited guidance on best practices during onboarding. Certain integration scenarios (e.g., .NET 8 Azure Functions with OpenTelemetry in isolated workloads) have been flagged as poorly documented or unsupported. BizEvents are reportedly siloed from trace and log correlation. The platform's breadth can be overwhelming for teams that only need point-solution monitoring.


### Source urls

- https://www.dynatrace.com/
- https://ir.dynatrace.com/news-events/press-releases/detail/379/dynatrace-reports-fourth-quarter-and-full-year-fiscal-2025-financial-results
- https://ir.dynatrace.com/sec-filings/all-sec-filings/content/0001773383-25-000065/dt-20250331.htm
- https://www.g2.com/sellers/dynatrace
- https://www.gartner.com/reviews/market/application-performance-monitoring-and-observability/vendor/dynatrace
- https://www.dynatrace.com/gartner-magic-quadrant-for-observability-platforms/
- https://en.wikipedia.org/wiki/Dynatrace
- https://ir.dynatrace.com/
- https://signoz.io/guides/dynatrace-pricing/
- https://www.cuspera.com/products/dynatrace-platform-x-13693/customer-story

Reviewed at: 2026-04-28T23:19:20.205+00:00


### Customer outcomes

| Customer | Summary | Metric |
| --- | --- | --- |
| TD Bank | Dynatrace helped TD Bank unify observability across its banking platform, consolidating monitoring tools and applying AI-driven AIOps to improve incident detection and resolution speed. | Transaction failure rate cut from 0.16% to 0.06%; monitoring costs reduced 45%; 75% AIOps efficiency savings; customer i |
| BNZ (Bank of New Zealand) | BNZ used Dynatrace to monitor 85 applications and 2,500+ services, driving major improvements in software release quality and service reliability over five years. | 58% increase in high-quality software releases; major service incidents down 94% over five years |
| WeLab Bank | Dynatrace's AI engine enabled early detection of potential system issues without false alarms, dramatically reducing root cause identification time. | Root cause identification time reduced from hours to minutes |



### Reviews breakdown

| Platform | Score | Score max | Review count | Url |
| --- | --- | --- | --- | --- |
| G2 | 4.5 | 5 | 1366 | https://www.g2.com/sellers/dynatrace |
| Gartner Peer Insights | 4.5 | 5 | 1512 | https://www.gartner.com/reviews/market/application-performance-monitoring-and-observability/vendor/dynatrace |



### Review themes



#### Praised

- AI-driven root cause analysis accuracy
- OneAgent zero-config auto-instrumentation
- Comprehensive full-stack visibility in a single platform
- Automatic topology mapping and dependency discovery
- Reduction in alert noise and false positives
- Strong customer and technical support
- Grail data lakehouse query power
- Kubernetes and cloud-native monitoring depth



#### Criticized

- High cost and opaque minimum annual commitments
- Steep learning curve for advanced features
- Complex initial configuration beyond defaults
- Billing surprises from memory rounding and ephemeral resource metering
- Limited guidance and best-practice documentation during onboarding
- BizEvents siloed from trace/log correlation
- Gaps in OpenTelemetry support for certain runtimes (e.g., .NET 8 Azure Functions)
- User permission validation complexity at enterprise scale




### Company facts

Founded year: 2005
Hq: Waltham, Massachusetts, USA


#### Founders

- Bernd Greifeneder
- Sok-Kheng Taing
- Hubert Gerstmayr

Employees range: 5000-6000
Total funding: $21.9M pre-IPO; IPO Aug 2019 (~$544M)
Valuation: ~$11B (market cap, Mar 2026)
Arr: ~$1.97B (Q3 FY2026, Dec 2025)
Customer count: ~4,100 (Mar 2025)
Status: Public (NYSE: DT)


Readiness: Not available


## Ranking

| Display name | Pair count | Total pairs | Presence percent | Avg position |
| --- | --- | --- | --- | --- |
| Datadog | 46 | 150 | 30.666666666666664 | 10.60576923076923 |
| New Relic | 45 | 150 | 30 | 10.10576923076923 |
| Grafana | 37 | 150 | 24.666666666666668 | 12.446601941747574 |
| Dynatrace | 23 | 150 | 15.333333333333332 | 24.176470588235293 |
| Elastic | 17 | 150 | 11.333333333333332 | 16.97222222222222 |
| Honeycomb | 17 | 150 | 11.333333333333332 | 18.05 |
| Splunk | 15 | 150 | 10 | 19.57894736842105 |
| Coralogix | 7 | 150 | 4.666666666666667 | 6.588235294117647 |
| Better Stack | 6 | 150 | 4 | 16 |
| Logz.io | 5 | 150 | 3.3333333333333335 | 11.666666666666666 |
| Sentry | 2 | 150 | 1.3333333333333335 | 3 |
| Chronosphere | 2 | 150 | 1.3333333333333335 | 22.333333333333332 |
| Axiom | 1 | 150 | 0.6666666666666667 | 74.66666666666667 |
| Mezmo | 1 | 150 | 0.6666666666666667 | 75 |



## Platform breakdown

| Platform | Prompt count | Presence rate |
| --- | --- | --- |
| chatgpt-search | 8 | 32 |
| perplexity | 6 | 24 |
| bing-copilot-search | 0 | 0 |
| google-ai-mode | 3 | 12 |
| google-ai | 0 | 0 |
| xai-search | 6 | 24 |



## Strengths

| Prompt text | Platform count | Avg position |
| --- | --- | --- |
| Which cloud observability platforms have the most reliable synthetic monitoring checks with the lowest false positive rates? | 1 | 1 |



## Gaps

| Prompt text | Competitor presence count |
| --- | --- |
| Which observability platforms make it easiest to correlate a user-reported error with the right trace and log lines in a distributed system? | 5 |
| What observability platforms offer the best tail-based sampling for high-throughput systems to control costs without losing important traces? | 4 |
| What are the best cloud-hosted observability platforms for migrating from a legacy self-hosted logging stack without losing historical data? | 4 |
| Which APM tools integrate best with cloud provider managed databases and serverless functions for end-to-end visibility? | 4 |
| What's the quickest distributed tracing platform to set up across a microservices architecture on a container orchestration platform? | 3 |



## Topic scores

| Topic name | Prompt count | Cited prompt count |
| --- | --- | --- |
| Capability | 5 | 4 |
| Developer Experience | 5 | 3 |
| Integrations & Ecosystem | 5 | 2 |
| Performance & Reliability | 5 | 2 |
| Setup & First Run | 5 | 1 |



## Prompt results

- Prompt text: What's the quickest distributed tracing platform to set up across a microservices architecture on a container orchestration platform?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available
Google-ai-mode: Not available
Google-ai: Not available
Xai-search: 10



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Grafana | 1 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Honeycomb | 1 |
| Grafana | 4 |



##### Bing-copilot-search





##### Google-ai-mode





##### Google-ai

| Display name | Position |
| --- | --- |
| Grafana | 2 |



##### Xai-search

| Display name | Position |
| --- | --- |
| Logz.io | 6 |
| Splunk | 9 |
| Dynatrace | 10 |
| New Relic | 18 |
| Honeycomb | 20 |


- Prompt text: Which observability platforms integrate with deployment pipelines to correlate performance regressions with specific code changes?


#### Brand position by platform

Chatgpt-search: 7
Perplexity: 5
Bing-copilot-search: Not available
Google-ai-mode: 3
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Datadog | 1 |
| New Relic | 4 |
| Dynatrace | 7 |



##### Perplexity

| Display name | Position |
| --- | --- |
| New Relic | 1 |
| Datadog | 3 |
| Dynatrace | 5 |



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Datadog | 1 |
| Dynatrace | 3 |



##### Google-ai





##### Xai-search




- Prompt text: Which monitoring platforms have the best anomaly detection — automatically surfacing regressions without manual threshold tuning?


#### Brand position by platform

Chatgpt-search: 2
Perplexity: 1
Bing-copilot-search: Not available
Google-ai-mode: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Honeycomb | 1 |
| Dynatrace | 2 |
| New Relic | 5 |
| Elastic | 8 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Dynatrace | 1 |
| Datadog | 5 |
| New Relic | 6 |



##### Bing-copilot-search





##### Google-ai-mode





##### Google-ai





##### Xai-search




- Prompt text: I'm evaluating observability platforms — which ones are best suited for a logs-first approach versus a traces-first approach?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available
Google-ai-mode: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Elastic | 1 |
| Splunk | 3 |
| Grafana | 6 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Honeycomb | 1 |



##### Bing-copilot-search





##### Google-ai-mode





##### Google-ai





##### Xai-search




- Prompt text: What observability platforms can a small engineering team realistically get to meaningful dashboards and alerting on quickly?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available
Google-ai-mode: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Grafana | 1 |
| Better Stack | 2 |
| New Relic | 3 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Grafana | 1 |
| Datadog | 3 |
| Better Stack | 5 |



##### Bing-copilot-search





##### Google-ai-mode





##### Google-ai





##### Xai-search




- Prompt text: What observability platforms support unified metrics, traces, and logs instrumentation for Node.js and Python polyglot applications?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available
Google-ai-mode: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search





##### Perplexity

| Display name | Position |
| --- | --- |
| Datadog | 1 |
| New Relic | 4 |



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Elastic | 5 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Elastic | 9 |



##### Xai-search




- Prompt text: Which enterprise observability platforms handle multi-tenant environments with isolated views per team or service best?


#### Brand position by platform

Chatgpt-search: 5
Perplexity: 1
Bing-copilot-search: Not available
Google-ai-mode: Not available
Google-ai: Not available
Xai-search: 26



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Grafana | 1 |
| Datadog | 4 |
| Dynatrace | 5 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Dynatrace | 1 |
| Grafana | 3 |



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Grafana | 1 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Grafana | 1 |



##### Xai-search

| Display name | Position |
| --- | --- |
| Logz.io | 5 |
| Datadog | 17 |
| Dynatrace | 26 |
| New Relic | 36 |
| Splunk | 46 |
| Grafana | 56 |
| Elastic | 66 |


- Prompt text: Which observability platforms make it easiest to correlate a user-reported error with the right trace and log lines in a distributed system?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available
Google-ai-mode: Not available
Google-ai: Not available
Xai-search: 9



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Datadog | 1 |
| Grafana | 2 |
| New Relic | 10 |



##### Perplexity

| Display name | Position |
| --- | --- |
| New Relic | 1 |
| Datadog | 3 |
| Honeycomb | 5 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| New Relic | 2 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Sentry | 2 |
| Honeycomb | 3 |
| Datadog | 5 |



##### Google-ai





##### Xai-search

| Display name | Position |
| --- | --- |
| New Relic | 1 |
| Dynatrace | 9 |
| Honeycomb | 18 |
| Splunk | 33 |
| Datadog | 55 |


- Prompt text: Which APM tools have the best day-one onboarding to get immediate value without drowning in noise?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available
Google-ai-mode: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| New Relic | 1 |
| Datadog | 3 |



##### Perplexity

| Display name | Position |
| --- | --- |
| New Relic | 1 |
| Sentry | 3 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| New Relic | 1 |



##### Google-ai-mode





##### Google-ai





##### Xai-search




- Prompt text: Which observability backends support receiving OpenTelemetry data simultaneously to avoid vendor lock-in?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available
Google-ai-mode: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Grafana | 1 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Grafana | 1 |
| New Relic | 3 |
| Datadog | 4 |
| Honeycomb | 8 |



##### Bing-copilot-search





##### Google-ai-mode





##### Google-ai





##### Xai-search

| Display name | Position |
| --- | --- |
| Datadog | 7 |
| Logz.io | 12 |
| Grafana | 14 |
| Honeycomb | 16 |
| Splunk | 17 |
| Elastic | 19 |


- Prompt text: What observability platforms offer the best tail-based sampling for high-throughput systems to control costs without losing important traces?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available
Google-ai-mode: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Honeycomb | 1 |
| Grafana | 3 |
| New Relic | 5 |
| Coralogix | 9 |
| Datadog | 11 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Honeycomb | 1 |
| Grafana | 3 |
| Datadog | 8 |



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Datadog | 3 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Datadog | 2 |
| Grafana | 3 |



##### Xai-search




- Prompt text: What are the best cloud-hosted observability platforms for migrating from a legacy self-hosted logging stack without losing historical data?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available
Google-ai-mode: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Datadog | 1 |
| Elastic | 4 |
| New Relic | 6 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Elastic | 1 |
| Datadog | 3 |
| New Relic | 6 |
| Grafana | 8 |



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Datadog | 1 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Datadog | 1 |



##### Xai-search




- Prompt text: Which observability platforms handle data retention and query performance best as log volume grows into terabytes per day?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available
Google-ai-mode: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Coralogix | 1 |
| Datadog | 11 |
| Elastic | 17 |
| Grafana | 19 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Grafana | 1 |



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Chronosphere | 4 |
| Coralogix | 6 |



##### Google-ai





##### Xai-search




- Prompt text: Which observability platforms support business-level metrics like conversion funnels alongside infrastructure and application telemetry?


#### Brand position by platform

Chatgpt-search: 8
Perplexity: 3
Bing-copilot-search: Not available
Google-ai-mode: 1
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Datadog | 1 |
| New Relic | 4 |
| Dynatrace | 8 |
| Splunk | 12 |
| Elastic | 16 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Datadog | 1 |
| Dynatrace | 3 |
| New Relic | 6 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Coralogix | 9 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Dynatrace | 1 |
| New Relic | 2 |



##### Google-ai





##### Xai-search




- Prompt text: Which distributed tracing platforms add the least overhead to latency-sensitive APIs — safe to run in production at full sampling?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available
Google-ai-mode: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Grafana | 2 |
| Datadog | 6 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Honeycomb | 4 |



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| New Relic | 4 |



##### Google-ai





##### Xai-search




- Prompt text: Which observability platforms integrate best with incident management and on-call scheduling tools for a seamless response workflow?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available
Google-ai-mode: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Datadog | 3 |
| Grafana | 6 |
| Splunk | 10 |
| New Relic | 11 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Datadog | 1 |
| New Relic | 5 |
| Grafana | 6 |



##### Bing-copilot-search





##### Google-ai-mode





##### Google-ai





##### Xai-search




- Prompt text: Which observability platforms support real user monitoring alongside backend APM for correlating frontend and backend performance?


#### Brand position by platform

Chatgpt-search: 3
Perplexity: 3
Bing-copilot-search: Not available
Google-ai-mode: 1
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Datadog | 1 |
| Dynatrace | 3 |
| New Relic | 5 |
| Splunk | 7 |
| Elastic | 9 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Datadog | 1 |
| New Relic | 2 |
| Dynatrace | 3 |
| Elastic | 5 |
| Splunk | 6 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Datadog | 1 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Dynatrace | 1 |
| Grafana | 2 |
| Datadog | 3 |
| New Relic | 6 |
| Elastic | 9 |



##### Google-ai





##### Xai-search




- Prompt text: Which APM tools integrate best with cloud provider managed databases and serverless functions for end-to-end visibility?


#### Brand position by platform

Chatgpt-search: 8
Perplexity: 5
Bing-copilot-search: Not available
Google-ai-mode: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Datadog | 1 |
| New Relic | 3 |
| Dynatrace | 8 |



##### Perplexity

| Display name | Position |
| --- | --- |
| New Relic | 1 |
| Dynatrace | 5 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| New Relic | 3 |
| Coralogix | 4 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| New Relic | 1 |
| Datadog | 3 |



##### Google-ai





##### Xai-search




- Prompt text: Which monitoring platforms offer the best on-call experience — from alert firing through to root cause identification?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available
Google-ai-mode: Not available
Google-ai: Not available
Xai-search: 70



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Better Stack | 2 |
| New Relic | 3 |
| Datadog | 4 |
| Grafana | 8 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Datadog | 1 |
| Grafana | 5 |



##### Bing-copilot-search





##### Google-ai-mode





##### Google-ai





##### Xai-search

| Display name | Position |
| --- | --- |
| Better Stack | 16 |
| New Relic | 17 |
| Datadog | 51 |
| Dynatrace | 70 |


- Prompt text: Which cloud observability platforms have the most reliable synthetic monitoring checks with the lowest false positive rates?


#### Brand position by platform

Chatgpt-search: 1
Perplexity: Not available
Bing-copilot-search: Not available
Google-ai-mode: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Dynatrace | 1 |
| Datadog | 3 |
| New Relic | 7 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Datadog | 1 |
| New Relic | 2 |
| Splunk | 3 |



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| New Relic | 1 |



##### Google-ai





##### Xai-search




- Prompt text: Which SaaS monitoring platforms have the lowest ingestion lag during high-volume log bursts so alerting stays fast?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available
Google-ai-mode: Not available
Google-ai: Not available
Xai-search: 59



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Splunk | 1 |
| Grafana | 3 |
| New Relic | 5 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Splunk | 1 |
| New Relic | 3 |



##### Bing-copilot-search





##### Google-ai-mode





##### Google-ai





##### Xai-search

| Display name | Position |
| --- | --- |
| Logz.io | 9 |
| Elastic | 10 |
| Splunk | 16 |
| New Relic | 35 |
| Better Stack | 51 |
| Dynatrace | 59 |
| Datadog | 61 |
| Axiom | 71 |
| Mezmo | 75 |


- Prompt text: Which observability platforms make it easiest for developers new to OpenTelemetry to adopt a trace-first workflow?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available
Google-ai-mode: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Honeycomb | 6 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Honeycomb | 1 |
| New Relic | 4 |
| Datadog | 6 |
| Grafana | 7 |



##### Bing-copilot-search





##### Google-ai-mode





##### Google-ai





##### Xai-search

| Display name | Position |
| --- | --- |
| New Relic | 7 |
| Coralogix | 13 |
| Honeycomb | 23 |
| Grafana | 65 |


- Prompt text: Which observability platforms have the best alert management features to help teams reduce alert fatigue through smart routing and thresholds?


#### Brand position by platform

Chatgpt-search: 3
Perplexity: Not available
Bing-copilot-search: Not available
Google-ai-mode: Not available
Google-ai: Not available
Xai-search: 72



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Datadog | 1 |
| Grafana | 2 |
| Dynatrace | 3 |
| New Relic | 5 |
| Splunk | 7 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Grafana | 1 |
| New Relic | 2 |



##### Bing-copilot-search





##### Google-ai-mode





##### Google-ai





##### Xai-search

| Display name | Position |
| --- | --- |
| Splunk | 7 |
| New Relic | 9 |
| Grafana | 23 |
| Datadog | 35 |
| Dynatrace | 72 |


- Prompt text: What log shipping tools work best for getting structured logs from containerized applications to an observability platform without code changes?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available
Google-ai-mode: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Grafana | 7 |
| Elastic | 8 |



##### Perplexity





##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Coralogix | 6 |



##### Google-ai-mode





##### Google-ai





##### Xai-search

| Display name | Position |
| --- | --- |
| Logz.io | 10 |
| Datadog | 16 |
| Chronosphere | 27 |
| Elastic | 32 |
| Grafana | 39 |


- Prompt text: Which observability platforms have the best ad-hoc query experience for high-cardinality log data during an active incident?


#### Brand position by platform

Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available
Google-ai-mode: Not available
Google-ai: Not available
Xai-search: Not available



#### Platform rows



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Better Stack | 1 |
| Datadog | 2 |
| Elastic | 3 |
| Grafana | 12 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Honeycomb | 1 |
| Grafana | 6 |



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Honeycomb | 3 |



##### Google-ai





##### Xai-search







## Top sources

| Url | Title | Domain | Logo url | Source vertical | Content type | Citation count | Last30d count |
| --- | --- | --- | --- | --- | --- | --- | --- |
| https://docs.dynatrace.com/docs/dynatrace-intelligence/anomaly-detection | Anomaly detection — Dynatrace Docs | docs.dynatrace.com | Not available | commercial | documentation | 6 | 6 |
| https://docs.dynatrace.com/docs/observe/digital-experience/synthetic/synthetic-app/create-configure-browser-monitors | Create and configure a browser monitor | docs.dynatrace.com | Not available | commercial | documentation | 5 | 5 |
| https://docs.dynatrace.com/docs/manage/identity-access-management/permission-management/management-zones | Management zones — Dynatrace Docs | docs.dynatrace.com | Not available | commercial | documentation | 5 | 5 |
| https://docs.dynatrace.com/docs/observe/digital-experience/rum/analyze-and-alert/analyze-frontend-to-backend | Analyze performance from frontend to backend | docs.dynatrace.com | Not available | commercial | documentation | 4 | 4 |
| https://docs.dynatrace.com/docs/analyze-explore-automate/alerting-and-notifications | Alerting and notifications | docs.dynatrace.com | Not available | commercial | documentation | 4 | 4 |
| https://www.dynatrace.com/platform/application-observability/ | Application Observability | dynatrace.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/4f638bff-15d9-4202-be76-d55ee1111439/2da23314472ce4ef9ade4f51324f87ab929f961e.png | commercial | product_page | 3 | 3 |
| https://docs.dynatrace.com/docs/discover-dynatrace/get-started/serverless-monitoring | Serverless monitoring | docs.dynatrace.com | Not available | commercial | documentation | 3 | 3 |
| https://docs.dynatrace.com/docs/ingest-from/technology-support/serverless-compute-services | Serverless compute support matrix - Dynatrace Documentation | docs.dynatrace.com | Not available | commercial | documentation | 3 | 3 |



## Response excerpts

| Prompt text | Platform | Excerpt |
| --- | --- | --- |
| Which observability platforms integrate with deployment pipelines to correlate performance regressions with specific code changes? | google-ai-mode | ...Changes Detection](https://docs.datadoghq.com/continuous_delivery/features/code_changes_detection/)Root cause analysis — Dynatrace Docs[Enhanced Root Cause Analysis using Even... |
| Which monitoring platforms have the best anomaly detection — automatically surfacing regressions without manual threshold tuning? | google-ai-mode | The best AI-powered observability platforms in 2026 Here is how the top contenders stack up for zero-tuning, automated regression detection: * * Dynatrace (Davis AI) : Widely considered the gold standard for causal, automated anomaly detection. |
| I'm evaluating observability platforms — which ones are best suited for a logs-first approach versus a traces-first approach? | google-ai-mode | Dynatrace : Relies on an automated, AI-driven topology mapping engine (Smartscape) that automatically discovers service dependencies and traces transactions end-to-end with deep contextual precision. |



## Competitor excerpts

| Platform | Competitor name | Excerpt |
| --- | --- | --- |
| chatgpt-search | Datadog | If your goal is to take a user report like “Checkout failed at 14:03, and I got a 500 error” and quickly find the exact distributed trace, the relevant log lines, and the failing service, I’d shortlist Datadog, Sentry, Honeycomb, New Relic, and Grafana Cloud. |
| chatgpt-search | Grafana | If your goal is to take a user report like “Checkout failed at 14:03, and I got a 500 error” and quickly find the exact distributed trace, the relevant log lines, and the failing service, I’d shortlist Datadog, Sentry, Honeycomb, New Relic, and Grafana Cloud. |
| perplexity | New Relic | The strongest fits are New Relic and Datadog; Honeycomb is a good option when your team is comfortable investigating structured telemetry. |
| perplexity | Datadog | The strongest fits are New Relic and Datadog; Honeycomb is a good option when your team is comfortable investigating structured telemetry. |
| bing-copilot-search | New Relic | The strongest options today are OpenObserve, New Relic, Datadog, Honeycomb, and OpenTelemetry-based stacks. ## 🔑 Key Considerations When choosing an observability platform for correlating user-reported errors: * Unified telemetry: Does the p... |
| google-ai-mode | Sentry | ...SREs](https://openobserve.ai/blog/logs-traces-metrics-correlation/)Errors, traces, logs, metrics: when to reach for what - Sentry Blog The observability platforms that make thi... |
| google-ai-mode | Honeycomb | ...lation the easiest—largely by natively bridging user context with backend telemetry—stand out in distinct ways: * * _Honeycomb_ — Best for High-Cardinality Root Cause Analysis * Why it shines: Honeycomb treats high-cardinality data (lik... |
| chatgpt-search | Honeycomb | My shortlist would start with Grafana Tempo + Grafana Alloy, Honeycomb, Grafana Cloud, and Datadog, with Jaeger/OpenTelemetry Collector and Axiom also worth evaluating depending on how much infrastructure you want to manage yourself. |
| chatgpt-search | Grafana | My shortlist would start with Grafana Tempo + Grafana Alloy, Honeycomb, Grafana Cloud, and Datadog, with Jaeger/OpenTelemetry Collector and Axiom also worth evaluating depending on how much infrastructure you want to manage yourself. |
| perplexity | Honeycomb | \| Platform \| Best fit \| Why it stands out \| Main trade-off \| \|---\|---\|---\|---\| \| Honeycomb Refinery \| Flexible, trace-aware sampling with explicit throughput control \| Refinery makes decisions on whole traces and supports rules-based, dynamic, dete... |
| perplexity | Grafana | \| \| Grafana Cloud Adaptive Traces / Grafana Alloy \| Managed tail sampling, or an open pipeline you can run yourself \| Adaptive Traces offers managed policy-based sampling, including errors, latency, and attributes. |
| google-ai-mode | Datadog | Honeycomb, New Relic (Infinite Tracing), and Datadog/Splunk (via OpenTelemetry Collector tail-based sampling) offer the most robust approaches to tail-based sampling for high-throughput environments. |



## Trend

Visibility delta: -5.399999999999999
Avg position delta: 2.2662337662337664
Citation count delta: 16