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Grafana Labs Releases Loki

Grafana Labs announced that Loki version 1.0 is generally available for production use.

Unlike other logging systems, Loki allows users to instantaneously switch between metrics and logs, preserving context and reducing MTTR. Loki is inspired by Prometheus — the de facto standard monitoring system for the cloud native ecosystem — and gives developers an easy-to-use, highly efficient and cost-effective approach to log aggregation.

“Grafana Labs is proud to have created Loki and fostered the development of the project, building first-class support for Loki into Grafana and ensuring customers receive the support and features they need,” said Tom Wilkie, VP of Product at Grafana Labs. “We are committed to delivering an open and composable observability platform, of which Loki is a key component, and continue to rely on the power of open source and our community to enhance observability into application and infrastructure.”

Loki is a horizontally scalable, highly available, multi-tenant log aggregation system inspired by Prometheus, the open source monitoring solution. Loki does not index the content of logs, but rather a set of labels for each log stream. By storing compressed, unstructured logs and only indexing metadata, Loki is cost-effective and simple to operate by design. Loki offers a Prometheus-like query language called LogQL to further integrate with the cloud native ecosystem.

“We’ve built Loki to be as close to the experience for logs as Prometheus is for metrics,” continued Wilkie. “And we adopted best practices for cloud native by making it containerized and Kubernetes-native, using cloud storage, and designing it to run at massive scale in the cloud.”

The Loki project was started at Grafana Labs and introduced in 2018. The project has since attracted more than 1,000 contributions from 137 contributors and nearly 8,000 stars on GitHub. Optimized for Grafana, Kubernetes and Prometheus, Loki is released under the Apache 2.0 license.

Loki version 1.0 is available immediately. Grafana Loki is a set of components that can be composed into a fully featured logging stack. Grafana Cloud offers a high-performance, hosted Loki service that allows users to store all logs together in a single place with usage-based pricing.

Grafana Labs also offers enterprise services and support for Loki, including:

- Support and training from Loki maintainers and experts

- 24 x 7 x 365 coverage from the geographically distributed Grafana team

- Per-node pricing that scales with deployment

Grafana Labs offers Grafana Enterprise, with key features and support for large organizations, and Grafana Cloud, a fully managed open and composable observability platform that offers Prometheus, Graphite, and Loki as a managed service for companies operating at scale or looking to offload administrative work to focus on their core competency.

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Grafana Labs Releases Loki

Grafana Labs announced that Loki version 1.0 is generally available for production use.

Unlike other logging systems, Loki allows users to instantaneously switch between metrics and logs, preserving context and reducing MTTR. Loki is inspired by Prometheus — the de facto standard monitoring system for the cloud native ecosystem — and gives developers an easy-to-use, highly efficient and cost-effective approach to log aggregation.

“Grafana Labs is proud to have created Loki and fostered the development of the project, building first-class support for Loki into Grafana and ensuring customers receive the support and features they need,” said Tom Wilkie, VP of Product at Grafana Labs. “We are committed to delivering an open and composable observability platform, of which Loki is a key component, and continue to rely on the power of open source and our community to enhance observability into application and infrastructure.”

Loki is a horizontally scalable, highly available, multi-tenant log aggregation system inspired by Prometheus, the open source monitoring solution. Loki does not index the content of logs, but rather a set of labels for each log stream. By storing compressed, unstructured logs and only indexing metadata, Loki is cost-effective and simple to operate by design. Loki offers a Prometheus-like query language called LogQL to further integrate with the cloud native ecosystem.

“We’ve built Loki to be as close to the experience for logs as Prometheus is for metrics,” continued Wilkie. “And we adopted best practices for cloud native by making it containerized and Kubernetes-native, using cloud storage, and designing it to run at massive scale in the cloud.”

The Loki project was started at Grafana Labs and introduced in 2018. The project has since attracted more than 1,000 contributions from 137 contributors and nearly 8,000 stars on GitHub. Optimized for Grafana, Kubernetes and Prometheus, Loki is released under the Apache 2.0 license.

Loki version 1.0 is available immediately. Grafana Loki is a set of components that can be composed into a fully featured logging stack. Grafana Cloud offers a high-performance, hosted Loki service that allows users to store all logs together in a single place with usage-based pricing.

Grafana Labs also offers enterprise services and support for Loki, including:

- Support and training from Loki maintainers and experts

- 24 x 7 x 365 coverage from the geographically distributed Grafana team

- Per-node pricing that scales with deployment

Grafana Labs offers Grafana Enterprise, with key features and support for large organizations, and Grafana Cloud, a fully managed open and composable observability platform that offers Prometheus, Graphite, and Loki as a managed service for companies operating at scale or looking to offload administrative work to focus on their core competency.

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In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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