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Clockwork Systems

Advanced clock synchronization for distributed systems

Overview

Clockwork Systems provides specialized clock synchronization technology for mission-critical distributed systems operating across cloud and on-premises environments. Its offerings combine software licensing, subscriptions, and professional services to ensure accurate timekeeping and coordination across distributed applications, with tools like Latency Sensei to monitor cloud network latency. The company differentiates itself through deep timing expertise tailored to time-sensitive industries and end-to-end deployment across diverse environments, supported by Stanford-origin research and Series A funding. Its goal is to help customers achieve reliable, precise synchronization to reduce timing-related issues and boost system performance.

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About Clockwork Systems

Simplify's Rating
Why Clockwork Systems is rated
B-
Rated B on Competitive Edge
Rated B on Growth Potential
Rated C on Differentiation

Industries

Data & AnalyticsEnterprise SoftwareFinancial ServicesGaming

Company Size

51-200

Company Stage

Late Stage VC

Total Funding

$72.6M

Headquarters

Palo Alto, CA

Founded

2018

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Simplify's take

What believers are saying

  • Clockwork raised $31M on October 5, 2026, bringing total funding to $73M.
  • Uber, Nebius, DCAI, and WhiteFiber publicly validated FleetIQ in 2025-2026.
  • Revelio counted 247 employees in June 2026, up 34.5% from 2023.

What critics are saying

  • NVIDIA, Broadcom, and hyperscaler-native tooling can bundle comparable fabric controls by 2027.
  • FleetIQ’s value proposition depends on AI spending continuing; one budget freeze crushes expansion.
  • If utilization gains disappoint after pilots, Clockwork becomes a niche monitoring vendor, not infrastructure standard.

What makes Clockwork Systems unique

  • Clockwork’s FleetIQ, launched September 2025, targets GPU communication, not generic observability.
  • Its hardware-agnostic fabric spans NVIDIA, AMD, InfiniBand, RoCE, cloud, and on-prem.
  • Stanford-originated clock sync plus dynamic traffic control underpins sub-microsecond visibility and recovery.

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Funding

Total Funding

$72.6M

Above

Industry Average

Funded Over

3 Rounds

Late VC funding comparison data is currently unavailable. We're working to provide this information soon!
Late VC Funding Comparison Coming Soon

Benefits

  • Competitive Salary

Growth & Insights and Company News

6 month growth

57%

1 year growth

-4%

2 year growth

6%
Associated Press
Mar 11th, 2026
Clockwork.io launches TorchPass to eliminate GPU failure waste in AI training, saving $6M per 2,048-GPU cluster

Clockwork.io has launched TorchPass Workload Fault Tolerance, a software solution that eliminates costly GPU training failures through Live GPU Migration technology. The system allows AI training workloads to continue running through hardware failures, network disruptions and node crashes without requiring checkpoint restarts. The company claims TorchPass can save over $6 million annually in a typical 2,048-GPU deployment by reducing wasted training progress by 95%. In large clusters, it cuts lost time from approximately three hours per day to under ten minutes. Independent testing by SemiAnalysis found TorchPass delivered faster fault-tolerant performance than standard checkpoint-restart approaches and higher Model FLOPs Utilisation than leading open-source alternatives. The solution typically completes recovery in approximately three minutes whilst training continues uninterrupted. TorchPass is now available as part of Clockwork.io's FleetIQ platform.

The SaaS News
Sep 11th, 2025
Clockwork Raises $20.57 Million in Funding | The SaaS News

Clockwork Raises $20.57 Million in Funding

TechStartups
Sep 10th, 2025
Clockwork Systems Raises $20.6M for FleetIQ

Stanford spinout Clockwork has raised $20.6 million, led by NEA with participation from notable investors, to address AI's GPU inefficiency. The funding coincides with the launch of FleetIQ, a software solution aimed at enhancing GPU performance by improving communication between GPUs, clusters, and clouds. This innovation seeks to reduce crashes, shorten restarts, and increase utilization rates, making AI infrastructure more efficient and sustainable.

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