Pico introduced ultra-low latency venue connectivity.
Pico’s ultra-low latency solution supports clients with latency-sensitive trading strategies requiring highly accurate views of the market and faster execution times. Utilizing high- performance Layer 1 switching technology, Pico can now achieve one-way market data latency of 5-87ns and round-trip latency of 140ns for order entry (an 80 percent reduction from standard Layer 3 access). This new Layer 1 offering is supported by Pico’s global engineering expertise and instrumented with its best-in-class Corvil Analytics1, assuring operational performance, transparency, and visibility. Corvil’s real-time analytics continuously monitors Layer 1 switch transit latency, with immediate alerts generated if nanosecond thresholds are violated, providing maximum operational transparency for latency-sensitive trading. End-of- day reports on details including message rates, microbursts, and gap detection give clients important service assurance visibility.
Layer 1 connectivity is available across venues in North America, Europe and Asia-Pacific (APAC), with Pico’s global service delivery team providing the required support for this turnkey solution to ensure rapid time to market.
“Layer 1 access is an important component for many trading strategies and Pico’s advances in optimizing exchange connectivity latency set a new benchmark which gives clients the ability to gain a significant competitive edge,” said Roland Hamann, CTO & Head of APAC at Pico. “Pico is committed to delivering a differentiated client experience and we continue to invest in our next generation network to remain ahead in performance, security, scalability and transparency. This launch further strengthens our comprehensive range of network products that meet the full spectrum of electronic trading requirements.”
Pico has built a cloud infrastructure for financial markets participants with mission critical exchange connectivity spanning 47 data centers across key global market centers in the Americas, Europe and Asia. Its resilient proprietary network, PicoNet™, is a globally comprehensive, low-latency and fully redundant network connecting major financial data centers with access to major public cloud providers. This allows Pico the technical capability to offer financial market data anywhere to its clients for true borderless trading. The combination of Pico’s global infrastructure and data services with its analytics and machine intelligence solution, Corvil Analytics, contributes to clients being equipped with cutting-edge solutions to meet ever-changing market conditions.
On August 4, 2021, Pico announced that it had entered into a definitive agreement for a business combination with FTAC Athena Acquisition Corp., a special purpose acquisition company. Upon closing of the transaction, the combined company will operate as Pico. The transaction reflects a pro forma enterprise value for the combined company of approximately $1.4 billion. The business combination, which has been unanimously approved by the boards of directors of both Pico and FTAC Athena, is targeted to close in late 2021, subject to stockholder approvals and other customary closing conditions.
The Latest
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.
The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...
The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...