VictoriaMetrics announced VictoriaMetrics Cloud, a hosted monitoring platform and managed service for metrics that allows organizations to monitor and store large amounts of time-series data, without having to run the underlying infrastructure.
VictoriaMetrics Cloud delivers the power of the VictoriaMetrics open-source time series solution with enterprise features.
Cloud-based and managed round-the-clock by VictoriaMetrics engineers, VictoriaMetrics Cloud removes the burden of running and maintaining your monitoring infrastructure. This means enterprises don’t need to devote valuable internal resources on learning and maintaining specific monitoring tools. Instead, these teams can provide valuable insights and suggest improvements to IT operations and digital product development.
Feedback from early adopters proves, compared to other leading solutions on the market, VictoriaMetrics Cloud significantly reduces monitoring costs by up to 5x, freeing up budget for other projects.
Additionally, VictoriaMetrics uses a highly compressed time series format that significantly reduces storage requirements compared to other time-series databases. This allows customers to store more data on the same hardware. VictoriaMetrics can also ingest and query data with minimal CPU overhead, allowing you to run it on hardware with fewer cores, resulting in lower costs.
Managing VictoriaMetrics on-premises requires manual scaling, a time-consuming and error-prone process that involves adding new nodes or increasing resource allocation. In contrast, VictoriaMetrics Cloud simplifies scaling by offering tiered upgrades. This ensures a smoother, more reliable scaling experience.
Customers can choose a VictoriaMetrics Cloud tier based on their needs, from starter plans, for smaller deployments, to high-powered options for massive datasets. For organisations already tied into using Grafana Cloud, a popular hosted monitoring platform, VictoriaMetrics Cloud integrates seamlessly.
VictoriaMetrics Cloud is flexible and customizable, integrating seamlessly with existing monitoring tools and infrastructure, such as OpenTelemetry. IT teams can build custom monitoring solutions to meet their specific needs. Importantly, customers can, if needed, easily switch back to the open-source VictoriaMetrics.
“VictoriaMetrics Cloud is a game-changer for companies looking for a powerful and cost-effective monitoring solution,” explains Artem Navoiev, Co-Founder at VictoriaMetrics. “This service will help organisations scale to billions of time series while keeping costs predictable.”
In a significant step forward in its commitment to helping enterprises expand their current monitoring of applications into a more strategic ‘state of all systems’ enterprise-wide observability, VictoriaMetrics will soon announce General Availability of VictoriaLogs. This easy-to-use open-source log management solution is designed for efficient log storage and analysis combining a powerful query language, for easy log searching with minimal resource requirements.
The Latest
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.