# Logz.io AI visibility in Observability & Monitoring

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

[Website](https://logz.io/)

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: 10
Total brands: 14
Measured responses: 150
Presence percent: 3.3333333333333335
Share of voice percent: 1.107011070110701
Average position: 11.666666666666666
Docs presence percent: 0
Blog presence percent: 2.666666666666667
Brand mention percent: 1.3333333333333335


## Profile

Overview: Logz.io is a Boston-based, privately held AI-powered observability platform founded in 2014 by Tomer Levy and Asaf Yigal. Its Open 360™ platform unifies log management (built on OpenSearch), infrastructure monitoring (Prometheus), distributed tracing (Jaeger/OpenTelemetry), and Cloud SIEM in a single managed SaaS experience. The platform is designed for DevOps and SRE teams that want the familiarity of open-source tooling without the operational burden of self-hosting. A core differentiator is its embedded AI Agent and OrionIQ agentic layer, which automate root cause analysis, surface anomalies, and reduce mean time to resolution. Logz.io also emphasizes cost efficiency via a Data Optimization Hub and consumption-based pricing. The company serves over 1,400 customers globally and has raised approximately $123M in total venture funding.
Product summary: Logz.io's Open 360™ platform is a fully managed, open-source-based observability suite that unifies logs, metrics, traces, and security monitoring. Its distinguishing feature is an AI Agent layer (OrionIQ) that automates root cause analysis, generates natural-language insights, and accelerates incident investigation. Built on OpenSearch, Prometheus, Jaeger, and OpenTelemetry, the platform serves as a managed upgrade path for teams running self-hosted ELK or Prometheus stacks, offering 300+ integrations, multi-tiered storage, a Data Optimization Hub for cost control, and Kubernetes 360 for container-native environments.


### Key capabilities

- AI-powered root cause analysis and agentic observability (OrionIQ / Open 360 AI)
- Log management on managed OpenSearch with live tail, pattern detection, and alerting
- Infrastructure monitoring based on Prometheus with 18-month metric retention
- Distributed tracing via OpenTelemetry and Jaeger with service map visualization
- Kubernetes 360 and App 360 unified views for container and microservice observability
- Cloud SIEM built on OpenSearch for security event monitoring and threat detection
- Data Optimization Hub for filtering noisy data and reducing ingestion costs
- Multi-tiered storage (Hot, Warm, Cold) with archive and restore capabilities
- Consumption-based and subscription pricing models with per-GB and per-metric billing
- Natural language querying and AI-generated visualizations via embedded AI Agent



### Target users

- DevOps and platform engineering teams managing cloud-native or Kubernetes-based infrastructure
- Site Reliability Engineers (SREs) responsible for incident response and MTTR reduction
- Engineering teams migrating from self-hosted ELK Stack or Prometheus
- Mid-market and enterprise software companies with distributed microservices architectures
- Security operations teams requiring cloud SIEM alongside observability
- Engineering leaders seeking to reduce observability tooling costs without sacrificing visibility



### Key use cases

- Replacing or augmenting self-hosted ELK/OpenSearch stacks with a managed SaaS alternative
- Reducing MTTR through AI-automated root cause analysis across logs, metrics, and traces
- Kubernetes and cloud-native application observability
- Controlling and optimizing observability data costs and storage spend
- Cloud infrastructure security monitoring via integrated SIEM
- Distributed microservices troubleshooting with correlated log-metric-trace data
- SRE and DevOps team productivity improvement through automation of manual investigation tasks
- Migrating from legacy on-prem monitoring tools to cloud-native observability

Integrations ecosystem: Logz.io offers 300+ integrations spanning major cloud providers (AWS, Microsoft Azure, Google Cloud), container and orchestration platforms (Kubernetes, Docker, AWS EKS, Azure Kubernetes Service), observability agents and collectors (OpenTelemetry, Prometheus, Fluent Bit, Filebeat, Logstash, Jaeger), databases and middleware (MongoDB, PostgreSQL, Redis, Kafka, RabbitMQ, Cassandra), web servers (Nginx, Apache), CDNs (Akamai), CI/CD tools (Jenkins), and programming language runtimes (Python, Node.js). It holds AWS Advanced Technology Partner status with competency designations in DevOps, Security, Data & Analytics, Generative AI, and SMB, and is available on the AWS Marketplace. A strategic partnership with Microsoft Azure enables ELK-on-Azure deployments. An MCP Server integration enables AI-native access to logs, metrics, and telemetry from LLM toolchains.
Pricing summary: Logz.io offers both consumption-based and subscription (Pro/Enterprise) pricing models. Consumption pricing: Log Management at $0.92/ingested GB/day (retention tiers: 3–30 days); Infrastructure Monitoring at $0.40/1,000 time-series metrics/day (18-month retention); Distributed Tracing at $0.16/1M spans/day (10-day retention); Agentic Observability (AI Agent) at $10/1M tokens or per invocation. Subscription plans (Pro and Enterprise) include unlimited daily data volume and users; overage is billed at 1.4× the subscription rate. Monthly billing is 1.2× the annual rate. A 14-day free trial is available with no credit card required. Volume discounts are available for high-usage customers via direct negotiation. The platform is also purchasable through the AWS Marketplace.
Review summary: Logz.io earns a 4.5/5 on G2 (171 reviews) and 4.3/5 on Gartner Peer Insights (49 reviews, Observability Platforms market). Users consistently praise the user-friendly interface, fast and knowledgeable customer support, seamless integration with cloud-native stacks, and the quality of AI-powered log insights. The platform is particularly valued by teams migrating from self-hosted ELK for its drop-in compatibility and managed reliability. Critical feedback centers on pricing that can escalate with data volume growth, a complex initial configuration experience, and some limitations in dashboard customization and UI density. Overall sentiment is positive, with 96% of G2 reviewers rating the product 4 or 5 stars.
Competitive positioning: Logz.io positions itself as a fully managed, AI-first observability platform built on open-source foundations (OpenSearch/ELK, Prometheus, Jaeger, OpenTelemetry), targeting teams that want the familiarity of open-source tools without the operational overhead of self-hosting. Its primary differentiation angles are: (1) an agentic AI layer (OrionIQ / Open 360 AI) that automates root cause analysis and reduces manual troubleshooting; (2) consumption-based pricing designed to lower total cost of ownership versus volume-tier incumbents like Splunk and Datadog; (3) a purpose-built data optimization hub that lets customers actively remove noisy data and reduce spend. Against pure open-source players like Elastic and Grafana Labs, Logz.io competes on managed SaaS simplicity and integrated AI. Against cost-optimized rivals like Coralogix and Mezmo, it competes on open-source pedigree and the breadth of its unified logs-metrics-traces-SIEM platform.
Limitations: Logz.io is a fully managed SaaS platform only—no self-hosted or on-premises deployment option is available, which may exclude highly regulated or air-gapped environments. User reviews on G2 note that pricing can escalate significantly at high log volumes. Some reviewers report a complex initial setup, difficult dashboard customization, and a UI that can feel cluttered. Distributed tracing retention is fixed at 10 days by default, which may be limiting for compliance or long-horizon debugging use cases. The platform is primarily AWS-hosted; multi-cloud or region availability carries additional cost and configuration complexity. As a mid-market SaaS vendor, Logz.io has a smaller ecosystem and fewer enterprise integrations compared to incumbents like Splunk or Datadog.


### Source urls

- https://logz.io/
- https://logz.io/about-us/
- https://logz.io/pricing/
- https://logz.io/case-studies/
- https://www.g2.com/products/logz-io/reviews
- https://www.gartner.com/reviews/market/observability-platforms/vendor/logz-io/product/open-360-platform
- https://logz.io/news-posts/logz-io-raises-52-million-in-series-d-funding-led-by-general-catalyst/
- https://logz.io/news-posts/logz-io-announces-23-million-funding-round/
- https://pitchbook.com/profiles/company/108291-34
- https://tracxn.com/d/companies/logzio/__N6_JS8N0Eut5QEwsxT48AFBuIbQc7i_5KMVNe6Si5hg/funding-and-investors
- https://logz.io/case-studies/how-ziprecruiter-boosted-sre-productivity-with-logz-io/

Reviewed at: 2026-04-28T23:21:01.757+00:00


### Customer outcomes

| Customer | Summary | Metric |
| --- | --- | --- |
| Dish Network | Dish Network used Logz.io's data optimization capabilities to filter out unneeded observability data, significantly cutting both data volume and associated costs. | 62% reduction in observability data and costs |
| ZipRecruiter | ZipRecruiter migrated from a self-hosted ELK stack that suffered 3–4 Elasticsearch incidents per month; after switching to Logz.io, query performance improved and SRE team productivity was significantly boosted. | 3x improvement in query performance |
| The Economist | The Economist's Head of DevOps reported that prior to Logz.io, over 20% of the team's time was spent managing their self-hosted logging platform; Logz.io eliminated that overhead. | >20% of engineering time reclaimed from platform management |
| Tidemark | Tidemark improved issue response time significantly after adopting Logz.io for log management and observability. | 3x improvement in issue response time |



### Reviews breakdown

| Platform | Score | Score max | Review count | Url |
| --- | --- | --- | --- | --- |
| G2 | 4.5 | 5 | 171 | https://www.g2.com/products/logz-io/reviews |
| Gartner Peer Insights | 4.3 | 5 | 49 | https://www.gartner.com/reviews/market/observability-platforms/vendor/logz-io/product/open-360-platform |



### Review themes



#### Praised

- User-friendly interface and intuitive dashboards
- Fast, knowledgeable, and responsive customer support
- Seamless integration with existing ELK-based infrastructure
- AI-powered insights and root cause analysis features
- Real-time log streaming and live tail capabilities
- Cost optimization and data filtering features
- Broad integrations with cloud platforms and DevOps tooling
- Ease of migration from self-hosted ELK stacks



#### Criticized

- Pricing escalates significantly at high log ingestion volumes
- Complex initial setup and configuration
- Dashboard customization can be difficult or limited
- UI can feel cluttered for new users
- Short default distributed tracing retention (10 days)
- Some features require Enterprise tier or additional negotiation




### Company facts

Founded year: 2014
Hq: Boston, MA, USA


#### Founders

- Tomer Levy
- Asaf Yigal

Employees range: 200-500
Total funding: ~$123M
Valuation: Not available
Arr: ~$48M
Customer count: 1,400+
Status: Private


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 | 0 | 0 |
| perplexity | 0 | 0 |
| bing-copilot-search | 0 | 0 |
| google-ai-mode | 0 | 0 |
| google-ai | 0 | 0 |
| xai-search | 5 | 20 |



## Strengths

| Prompt text | Platform count | Avg position |
| --- | --- | --- |
| Which enterprise observability platforms handle multi-tenant environments with isolated views per team or service best? | 1 | 5 |



## 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 observability platforms support real user monitoring alongside backend APM for correlating frontend and backend performance? | 4 |
| Which APM tools integrate best with cloud provider managed databases and serverless functions for end-to-end visibility? | 4 |



## Topic scores

| Topic name | Prompt count | Cited prompt count |
| --- | --- | --- |
| Capability | 5 | 1 |
| Developer Experience | 5 | 0 |
| Integrations & Ecosystem | 5 | 2 |
| Performance & Reliability | 5 | 1 |
| 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: 6



#### 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: 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 |
| 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: 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 |
| 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: Not available
Perplexity: Not available
Bing-copilot-search: Not available
Google-ai-mode: Not available
Google-ai: Not available
Xai-search: 5



#### 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: Not available



#### 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: 12



#### 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: 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 |
| 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: 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 |
| 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: 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 |
| 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: Not available



#### 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: 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 |
| --- | --- |
| 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: 9



#### 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: 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 |
| 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: 10



#### 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://logz.io/blog/ebpf-integration/ | Zero-Code Distributed Tracing for Kubernetes with Logz.io & eBPF | logz.io | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/4f638bff-15d9-4202-be76-d55ee1111439/92653ebd39377e4194275a4c9da0c5337e258ace.png | commercial | blog_post | 3 | 3 |
| https://logz.io/blog/jaeger-kubernetes-best-practices/ | Jaeger Essentials: Jaeger, Kubernetes & Best Practices | logz.io | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/4f638bff-15d9-4202-be76-d55ee1111439/92653ebd39377e4194275a4c9da0c5337e258ace.png | commercial | blog | 1 | 0 |
| https://logz.io/blog/top-open-source-log-management-tools/ | Top 5 open source Log Management tools \| Logz.io | logz.io | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/4f638bff-15d9-4202-be76-d55ee1111439/92653ebd39377e4194275a4c9da0c5337e258ace.png | commercial | blog_post | 1 | 0 |
| https://logz.io/glossary/enterprise-observability/ | What is Enterprise Observability? Components & Benefits - Logz.io | logz.io | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/4f638bff-15d9-4202-be76-d55ee1111439/92653ebd39377e4194275a4c9da0c5337e258ace.png | commercial | home | 1 | 0 |
| https://logz.io/blog/opentelemetry-observability/ | OpenTelemetry Observability: Top Features and Best Practices | logz.io | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/4f638bff-15d9-4202-be76-d55ee1111439/92653ebd39377e4194275a4c9da0c5337e258ace.png | commercial | blog_post | 1 | 0 |
| https://logz.io/blog/a-practical-guide-to-kubernetes-logging/ | A Practical Guide to Kubernetes Logging | logz.io | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/4f638bff-15d9-4202-be76-d55ee1111439/92653ebd39377e4194275a4c9da0c5337e258ace.png | commercial | blog | 1 | 0 |



## Response excerpts





## 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: 0
Avg position delta: Not available
Citation count delta: 0