Grafana Labs

Open-source observability platform providing dashboards, metrics, logs, and traces, recognized as a Leader in the 2025 Gartner Magic Quadrant for Observability Platforms. Leader in Gartner MQ for Observability for second consecutive year (2025); Grafana Cloud integrates AI/ML monitoring; OpenTelemetry adoption at 50%+; approximately $200M+ ARR.

Grafana's composable, open-source architecture and AI integrations make it the observability backbone for teams deploying AI agents who need to monitor both traditional and LLM-based services.

grafana.com Observability and AI Monitoring

United Statesprivate

At a glance

Total raised

$1.1B

last round series e

Headcount

1k-5k

approximate · public range

Headquarters

New York

United States

Founded

2014

12 years active

Status

Private

Observability and AI Monitoring

Subsector

Observability and AI Monitoring

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Funding history

Total raised

$1.1B

Rounds tracked

Latest stage

Series E

Date	Stage	Amount	Valuation	Lead investors
Mar 12, 2026	Series E	$250M	—	Lightspeed Venture Partners, GIC
Aug 21, 2024	Growth	$270M	$6B	Lightspeed Venture Partners
Apr 6, 2022	Series D	$240M	$3B	GIC
Aug 24, 2021	Series C	$220M	$3B	Sequoia Capital, Coatue
Aug 17, 2020	Series B	$50M	—	Lightspeed Venture Partners, Lead Edge Capital
Oct 24, 2019	Series A	$24M	—	Lightspeed Venture Partners
Dec 1, 2018	Seed	$1.2M	—	—

Founder backgrounds

Anthony Woods

Co-founder

Prior to co-founding Grafana Labs, ran operations at Voxel, a cloud hosting company, in Singapore, and previously built critical tooling for Visa's payment infrastructure. Earlier in his career, he was a Senior Developer at Internap Network Services. He studied at Edith Cowan University in Western Australia. Alongside Torkel Ödegaard, he helped build Grafana from an open-source dashboarding project into a full-stack observability platform recognized as a Gartner Magic Quadrant Leader. Woods is based in Perth, Australia, and has publicly committed, with his wife, to giving away the majority of their personal wealth from the company's success, telling the *Australian Financial Review*: "We realise that we have been very fortunate and that's something that we want to share with the rest of the world."

Raj Dutt

Co-founder & CEO

Raj Dutt founded and led Voxel, a cloud and hosting company acquired by Internap in 2012, before co-founding Grafana Labs (originally named raintank) in 2014 alongside Torkel Ödegaard and Anthony Woods. He holds a degree from Rensselaer Polytechnic Institute. Grafana Labs grew out of the open-source Grafana project, and under Dutt the company has scaled to roughly 1,450 employees and upwards of $150M in ARR. A distinguishing trait is his **"big tent philosophy"**: Grafana commits to interoperating with any metrics, logs, or traces backend — including competitors' — rather than locking users into its own stack. This stance was codified early when the company bet on Prometheus as a core data source even when it was still a small project, and it remains central to Grafana Labs' positioning as a vendor-neutral observability layer.

Torkel Ödegaard

Co-founder & Chief Grafana Officer

Studied at Mälardalen University (2000–2005) before working as a platform engineer at eBay Sweden, where the operational pain of visualizing time-series data at scale led to the creation of Grafana. Released as open source in 2014, the project grew into Grafana Labs, co-founded with Raj Dutt and Anthony Woods. Prior to observability work, Ödegaard cut his teeth modding video games, an early tinkering phase that foreshadowed his builder approach. He remains deeply hands-on—still writing code daily as Chief Grafana Officer—and is known within the community for a relentlessly practical, engineer-first posture that prioritizes shipping working software over abstractions.

AI bull / bear

Bull case

Grafana Labs is positioned to become the definitive observability layer for the AI-native enterprise. Its second consecutive Gartner Magic Quadrant Leader placement in 2025 signals that the market has consolidated around the Grafana stack as the standard, not merely an option. The key accelerant: AI/ML monitoring natively integrated into Grafana Cloud. As enterprises deploy LLM-based agents and retrieval-augmented generation pipelines, the surface area of what must be observed explodes — token usage, hallucination rates, latency across model calls, and prompt effectiveness. Grafana’s composable architecture maps perfectly onto this fragmented landscape, ingesting signals from vector databases, model endpoints, and traditional microservices in a single pane. OpenTelemetry adoption crossing 50% further entrenches Grafana’s position. When instrumentation is standardized, the value shifts to the visualization and query layer — exactly where Grafana dominates. The $250 million Series E led by Lightspeed and GIC in March 2026 gives the company ample runway to invest in AI-specific product features and enterprise go-to-market without near-term liquidity pressure. With approximately $200 million in ARR, built on an open-source engine that enjoys near-universal developer affinity, Grafana benefits from a conversion funnel that proprietary competitors cannot replicate.

Bear case

Grafana’s open-source heritage cuts both ways. The core dashboard and visualization products are free and widely self-hosted, which limits the addressable pool of paying customers and forces the company to monetize through managed cloud services and enterprise plugins. Hyperscalers — AWS with CloudWatch and Amazon Managed Grafana, Microsoft with Azure Monitor and managed Grafana — already package and resell Grafana, capturing margin that Grafana Labs itself cannot access. Datadog, meanwhile, outspends on sales and marketing while offering a tightly integrated proprietary suite that appeals to buyers who prioritize simplicity over composability. The observability market is also absorbing AI monitoring at a pace that may favor incumbents with existing enterprise contracts. If AI observability becomes a feature rather than a category — absorbed into existing APM and infrastructure monitoring suites — Grafana’s AI-specific investments may not yield the differentiation required to convert new logos at scale. There is also a structural tension between the bottom-up, developer-led adoption model and the top-down enterprise procurement motion needed to sustain $200 million-plus ARR growth. As the organization scales past $1.1 billion in total funding, expectations around exit multiples intensify — and the path to an IPO robust enough to clear that bar is not yet proven.

Generated by deepseek-v4-pro · May 2, 2026Refreshes quarterly · see disclosures

In the news

The New Stack5/10/2026
Why Prometheus couldn’t see Cilium metrics at 2 a.m.
Grafana metrics visibility gap illustrates "integration tax" cost of coordinating multiple CNCF projects in production Kubernetes platforms.

Watch

Leadership

Raj Dutt

Co-founder & CEO

Torkel Ödegaard

Co-founder & Chief Grafana Officer

𝕏

Anthony Woods

Co-founder

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Products

Grafana
The open-source observability and data visualization platform that enables users to query, visualize, alert on, and understand their metrics, logs, and traces no matter where they are stored. It connects to hundreds of data sources including Prometheus, InfluxDB, and PostgreSQL, making it the industry-standard dashboarding tool for SREs, DevOps engineers, and analysts who need unified real-time visibility into their infrastructure and applications.
Grafana Cloud
A fully managed, composable observability platform that brings together Prometheus metrics, logs, traces, and visualizations in a single stack. It includes built-in AI-powered assistance, Adaptive Telemetry to reduce data costs by up to 80%, and turnkey solutions for Kubernetes monitoring, incident response, and load testing. Designed for teams that want a scalable, no-ops observability solution without managing infrastructure.
Grafana Loki
A horizontally scalable, highly available, multi-tenant log aggregation system inspired by Prometheus. Loki indexes metadata rather than the full log content, making it dramatically cheaper and more efficient than traditional log management solutions. It pairs natively with Grafana and is designed for developers and operators who need cost-effective, cloud-native log storage and querying at scale.
Grafana Tempo
A high-volume, distributed tracing backend that stores traces in object storage and integrates seamlessly with Grafana. Tempo supports OpenTelemetry-native ingestion and works with any tracing protocol, requiring only cheap object storage (S3, GCS, Azure Blob) rather than proprietary databases. It enables SREs and platform engineers to debug performance issues across microservices architectures without the high cost of traditional tracing solutions.
Grafana Mimir
A horizontally scalable, highly available, multi-tenant time series database designed as a long-term storage backend for Prometheus. Mimir provides massive scale for metrics with features like continuous query optimization, recording rules, and native histograms. It allows platform teams to consolidate their Prometheus infrastructure into a single, cost-effective metrics store that can handle billions of active time series.

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