B2B SaaS Provider · vs · B2B SaaS Provider
Elastic vs Grafana Labs
Strukturierter Technologie- und Marktvergleich · Stand 2026
Direkte Merkmalsgegenüberstellung
Elastic · vs · Grafana LabsEnterprise Search, Observability und Security-Software basierend auf Elasticsearch.
Führende Open-Core-Observability-Plattform zur zentralen Visualisierung und Analyse komplexer Telemetriedaten.
Vergleichsanalyse & Key Insights
Was ist der Hauptunterschied zwischen Elastic und Grafana Labs?
Beim Vergleich von Elastic und Grafana Labs agieren beide Plattformen im Bereich Cloud Data Warehouse / Data Lake, B2B SaaS Provider und Analytics & Messplattform. Elastic ist positioniert als Enterprise Search, Observability und Security-Software basierend auf Elasticsearch, während Grafana Labs den Schwerpunkt auf Führende Open-Core-Observability-Plattform zur zentralen Visualisierung und Analyse komplexer Telemetriedaten legt. Beide Anbieter stellen komplementäre wie auch konkurrierende Kernfähigkeiten für den Markt bereit.
Welche Alternativen gibt es zu Elastic und Grafana Labs?
Bei der Evaluierung von Elastic und Grafana Labs prüfen Enterprise-Entscheider häufig auch weitere Plattformen im Bereich Cloud Data Warehouse / Data Lake, B2B SaaS Provider und Analytics & Messplattform. Die erweiterte Wettbewerbslandschaft und detaillierte Marktprofile findest du direkt auf Polaris7.
Echtzeit-Beobachtung
Aktuelle Marktsignale & News: Elastic vs Grafana Labs
Öffentlich erfasste Marktbewegungen, Partnerschaften, Produkt-Updates und strategische Ankündigungen aus dem Knowledge-Graphen.
Elastic
Letzte Aktivitäten
- ·SEC APIfinancials
10-Q Financial Filing Analysis for Elastic (2026-08-28)
Elastic N.V. hat die Finanzergebnisse für das zum 31. Juli 2026 beendete erste Quartal des Geschäftsjahres 2027 veröffentlicht und einen Gesamtumsatz von 478,11 Millionen USD erzielt, was einem Anstieg von 15 % im Jahresvergleich entspricht. Haupttreiber war Elastic Cloud mit einem Wachstum von 20 % im Jahresvergleich auf 235,21 Millionen USD, was 49 % des Gesamtumsatzes ausmacht. Parallel leitete Elastic am 24. Juni 2026 einen Restrukturierungsplan zur operativen Optimierung ein, der einen Stellenabbau von rund 7 % und Restrukturierungskosten von 19,92 Millionen USD umfasste. Zudem akquirierte Elastic nach Quartalsende am 21. August 2026 die KI-Ermittlungsplattform Deductive AI, Inc. für ca. 70 Millionen USD in bar.
- Der Gesamtumsatz im ersten Quartal des GJ 2027 stieg im Jahresvergleich um 15 % auf 478,11 Mio. USD, getrieben von Elastic Cloud mit einem Plus von 20 % auf 235,21 Mio. USD (49 % des Gesamtumsatzes).
- Ein am 24. Juni 2026 initiierter Restrukturierungsplan führte zu einem Stellenabbau von 7 % und Belastungen in Höhe von 19,92 Mio. USD.
- Nach Quartalsende wurde am 21. August 2026 die Barübernahme von Deductive AI, Inc. für rund 70 Mio. USD abgeschlossen.
- ·https://martechseries.com/feed/Hiring
Elastic Nominates Julia Liuson to Board
Elastic announced the nomination of Julia Liuson to its Board of Directors. Liuson is a veteran technology executive who most recently served as President of Microsoft’s Developer Division and played a leadership role in integrating AI into developer tools including work with GitHub. Her nomination is subject to shareholder approval at Elastic’s annual general meeting in October 2026; if elected she will join the company’s Compensation Committee. The release also notes that Caryn Marooney will not stand for re-appointment when her term expires in October 2026. Elastic positioned the nomination as adding AI and developer-platform expertise as the company pursues opportunities connecting AI applications and agents to enterprise data for observability and security use cases.
- Elastic nominated Julia Liuson to its Board of Directors.
- Julia Liuson most recently served as President of Microsoft’s Developer Division and worked on GitHub integrations such as GitHub Copilot.
- Liuson’s nomination is subject to a shareholder vote at Elastic’s 2026 annual general meeting in October 2026.
- ·https://martechseries.com/feed/M&A
Elastic Completes Deductive AI Acquisition
Elastic announced it has completed the acquisition of Deductive AI, an AI-powered investigation platform that automates production-incident investigation and root-cause analysis. Elastic says the acquisition advances Elastic Observability by adding a reinforcement-learning-powered investigation engine and an AI SRE agent that gathers evidence, forms and tests hypotheses across code, telemetry, and organizational knowledge. Elastic CEO Ash Kulkarni and Deductive AI cofounder and former CEO Rakesh Kothari are quoted on the strategic fit and expected acceleration of AI-powered investigation capabilities. Existing Deductive AI customers will continue to receive support while integration plans and additional roadmap details are developed and shared in the coming months.
- Elastic completed the acquisition of Deductive AI (announced on August 25, 2026).
- Deductive AI is an AI-powered investigation platform that provides an AI SRE agent to gather evidence, form and test hypotheses, and determine root cause across code, telemetry, and organizational knowledge.
- Elastic said the acquisition advances Elastic Observability by adding a reinforcement-learning harness for root cause analysis.
Grafana Labs
Letzte Aktivitäten
- ·DEV CommunityApplication Performance Monitoring (APM)
Observability Stack: Prometheus, Node Exporter, Grafana
A technical how-to explaining the three-piece observability stack: Prometheus (time-series database that scrapes metrics), Node Exporter (exposes OS-level metrics at a /metrics HTTP endpoint), and Grafana (visualizes Prometheus data as dashboards). The article describes the pull-based model Prometheus uses, the role of Node Exporter as a translator of OS stats, how Grafana queries Prometheus, default ports (Prometheus 9090, Node Exporter 9100, Grafana 3000), basic install commands, a sample prometheus.yml with scrape_interval and job_name, and next steps such as adding scrape targets, writing PromQL queries, and adding Alertmanager for notifications.
- Prometheus is a time-series database and monitoring system that scrapes metrics from HTTP endpoints and exposes its own metrics on port 9090.
- Node Exporter exposes hardware and OS-level metrics at a /metrics endpoint (default port 9100) and is typically installed one-per-machine.
- Grafana queries Prometheus (e.g., via PromQL) to render dashboards and runs by default on port 3000; it stores no metrics itself.
- ·DEV CommunityInternal Developer Platform (IDP)
Building an Internal Developer Platform on Azure AKS
This technical article explains how to create an Internal Developer Platform (IDP) using Azure Kubernetes Service (AKS). It outlines core components including AKS as the managed Kubernetes backbone, a service mesh (e.g., Istio or Linkerd) for microservice communication, CI/CD pipelines (Azure DevOps, GitHub Actions, Jenkins) for automated build and deployment, monitoring and logging tools (Azure Monitor, Prometheus, Grafana, Azure Log Analytics), and security/compliance controls (RBAC, Pod Security Policies, Azure Policy, Azure Security Center). The piece describes an example developer workflow from code push to production and summarizes benefits such as increased efficiency, scalability, security, and consistency.
- An Internal Developer Platform (IDP) is a set of tools, processes, and automations that simplifies development, testing, and deployment for developers.
- Azure Kubernetes Service (AKS) is presented as the central managed Kubernetes environment where containerized applications run.
- Service meshes such as Istio or Linkerd are recommended to manage microservice communication, including load balancing, traffic management, and security policies.
- ·DEV CommunityInfrastructure
Read-Only SRE: Using AI in Production Safely
The author argues for a conservative, observation-first role for AI in production SRE workflows: grant AI read-only access to telemetry (logs, dashboards, events, commits, deployment history, IaC plans) so it can synthesize incident timelines, summarize recent activity, and surface anomalies — but keep production write actions (restarts, scaling, Terraform changes, firewall edits) under human control. The piece frames AI as a fast, always-available “SRE intern” that helps engineers think faster without taking ownership of risky changes. The author acknowledges AI may earn broader operational responsibilities in the future but recommends an onboarding approach that mirrors human engineers: observe, learn, and prove understanding before receiving write permissions. Published on dev.to on 2026-07-10.
- Author recommends giving AI read-only access to production telemetry (logs, events, monitoring dashboards, deployment history, Terraform plans, Git commits) to help with incident troubleshooting.
- Author explicitly advises against allowing AI to perform production writes (restarts, scaling, deleting resources, changing Terraform, updating firewall rules) due to accountability and business-context gaps.
- Author frames AI as a fast 'SRE intern' useful for preparing incident timelines, summarizing dashboards, highlighting anomalies and suggesting possible causes without making changes.
Exakte Ökosystem-Überschneidungen vergleichen
Erkunde alle tiefen Marktbeziehungen in Polaris7. Entdecke gemeinsame Kunden, integrierte Technologien, SDK-Schnittstellen und überlappende Partner von Elastic und Grafana Labs im Markt-Ökosystem.