Scalyr announced the availability of a cloud-based log analytics platform capable of ingesting over 200 TB/day of data, and in real time.
As a result, Scalyr customers can upload, search, analyze, and retain data at enterprise scale, and at a cost that is orders-of-magnitude below current industry standards. This breakthrough innovation has allowed Scalyr’s pricing to rival even ‘do-it-yourself’ options using open source solutions, while providing a full service SaaS solution out of the box, with no set-up or maintenance required.
Scalyr believes that every company should be able to affordably collect, retain, and use all of its application and infrastructure event data to deliver product insights, reduce MTTR, improve SLOs, and optimize system performance. Companies that require massive scale for log and other real time event data have previously been forced to use on-premise solutions that are often expensive and slow. Achieving scale, affordably, has been an unsolved problem for companies of all sizes.
“We code-named this project ‘Sonic Boom’ because we aimed to break through the sound barriers of scale and costs that have prevented many companies from moving log management and analytics to the cloud,” said Christine Heckart, CEO at Scalyr. “The amount of event data that modern organizations must collect, store and analyze continues to grow exponentially, while budgets do not. Today’s announcement is Scalyr’s answer to this critical issue. This is a watershed moment that will fully transform observability tools into a baseline business utility for companies of all sizes and types.”
Scalyr’s revolutionary multi-tenant architecture is the first platform to simultaneously offer scale, affordability and performance to organizations of any size. Because there is a network-effect built into the architecture, the more data Scalyr ingests, the faster and the more affordable the system is for all customers. Organizations of any size now have access to the fastest, most scalable event data platform on the market to search and visualize performance data for a variety of devops, developer, and product use cases, including benefits such as:
- Full service SaaS platform for less than $5/GB at scale
- Integrated S3 storage that efficiently supports real-time ingest with the ability to cost effectively store data for years
- Intuitive UI and interactive performance so teams can work at the speed of thought – 96 percent of queries are processed in under one second
- Real-time visibility, dashboards, alerts, and search capabilities optimized for high-cardinality data sets
- Metrics and traces, free and on the fly when derived from log data, to complement or replace other metrics or tracing solutions
“Modern architectures generate staggering volumes of logs. Most people think that retaining this data is impossible; we believe it’s essential,” said Steve Newman, Founder and Chairman at Scalyr. “With the new architecture we’re announcing today, we’ve pushed back the scale / cost tradeoff by an order of magnitude, providing affordable enterprise-caliber visibility for engineering-driven businesses at scales of 200 TB/day and beyond.”
The Latest
In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...
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 ...