Skip to main content

Grafana Labs Releases Grafana 12

Grafana Labs released Grafana 12, the latest version of the company's flagship open source data visualization platform, introducing a comprehensive approach to observability as code that enables a more consistent and stable experience.

"We reimagined what it means to provision dashboards and how APIs and schema are structured," said Torkel Ödegaard, Co-Founder, Grafana Labs. "These are fundamental changes that have become the basis for a range of improvements we've made to how users can interact with Grafana through code. With Grafana 12, we focused on providing everything users need to more easily and efficiently create and manage dashboards."

Key features of Grafana 12 include:

  • App Platform: The backbone of the observability as code strategy, providing consistent, versioned APIs for managing Grafana resources like dashboards, plus a set of tools for building custom applications on top of Grafana.
  • New Dashboard Schema: A new JSON structure that decouples general settings from content, enhancing readability when rendered as code and making it easier to generate dashboards.
  • Dynamic Dashboards: Powered by the new dashboard schema, this feature allows for more flexible dashboard creation with improved customization options.
  • Git Sync: Users can automatically synchronize Grafana dashboards to a GitHub repository and review changes using pull requests, for higher-quality and more portable dashboards.
  • New As Code Tools: A set of new products that can be integrated into pipelines or GitHub Actions, supporting customers who already have observability as code setups in place. These include improvements to the Terraform provider and a new CLI tool, GrafanaCTL.
  • 15 New Data Sources: Explore product analytics data, DB data, and developer tools with new data sources like DynamoDB, CosmosDB, Cloudflare, Atlassian Statuspage, and Pagerduty.
  • SQL Expressions: Use SQL to combine and transform data from different sources and merge disparate data sets in real time.
     

The Latest

FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action? ...

Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk ...

While IT leaders are becoming more comfortable and adept at balancing workloads across on-premises, colocation data centers and the public cloud, there's a key component missing: connectivity, according to the 2025 State of the Data Center Report from CoreSite ...

A perfect storm is brewing in cybersecurity — certificate lifespans shrinking to just 47 days while quantum computing threatens today's encryption. Organizations must embrace ephemeral trust and crypto-agility to survive this dual challenge ...

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Grafana Labs Releases Grafana 12

Grafana Labs released Grafana 12, the latest version of the company's flagship open source data visualization platform, introducing a comprehensive approach to observability as code that enables a more consistent and stable experience.

"We reimagined what it means to provision dashboards and how APIs and schema are structured," said Torkel Ödegaard, Co-Founder, Grafana Labs. "These are fundamental changes that have become the basis for a range of improvements we've made to how users can interact with Grafana through code. With Grafana 12, we focused on providing everything users need to more easily and efficiently create and manage dashboards."

Key features of Grafana 12 include:

  • App Platform: The backbone of the observability as code strategy, providing consistent, versioned APIs for managing Grafana resources like dashboards, plus a set of tools for building custom applications on top of Grafana.
  • New Dashboard Schema: A new JSON structure that decouples general settings from content, enhancing readability when rendered as code and making it easier to generate dashboards.
  • Dynamic Dashboards: Powered by the new dashboard schema, this feature allows for more flexible dashboard creation with improved customization options.
  • Git Sync: Users can automatically synchronize Grafana dashboards to a GitHub repository and review changes using pull requests, for higher-quality and more portable dashboards.
  • New As Code Tools: A set of new products that can be integrated into pipelines or GitHub Actions, supporting customers who already have observability as code setups in place. These include improvements to the Terraform provider and a new CLI tool, GrafanaCTL.
  • 15 New Data Sources: Explore product analytics data, DB data, and developer tools with new data sources like DynamoDB, CosmosDB, Cloudflare, Atlassian Statuspage, and Pagerduty.
  • SQL Expressions: Use SQL to combine and transform data from different sources and merge disparate data sets in real time.
     

The Latest

FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action? ...

Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk ...

While IT leaders are becoming more comfortable and adept at balancing workloads across on-premises, colocation data centers and the public cloud, there's a key component missing: connectivity, according to the 2025 State of the Data Center Report from CoreSite ...

A perfect storm is brewing in cybersecurity — certificate lifespans shrinking to just 47 days while quantum computing threatens today's encryption. Organizations must embrace ephemeral trust and crypto-agility to survive this dual challenge ...

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...