Funding

California-Based Clockwork.io Secures $31M in Funding

Oct 6, 2026 | By Devin Jacobs

California-Based Clockwork.io Secures $31M in Funding

SUMMARY

  • Clockwork.io raised $31M in funding.
  • Funds will support growth and enterprise adoption.
  • Clockwork.io builds software for reliable AI workloads.

Clockwork.io, a Palo Alto, CA-based provider of fault-tolerance software for AI training, reinforcement learning, and inference workloads, has secured $31 million in a funding round, bringing the total amount to $73 million.

The round was co-led by Premji Invest, Wing Venture Capital, and Seligman Ventures, with participation from NEA and e& Capital.

The company will use the funding to expand enterprise adoption and scale delivery through cloud partners.

What is Clockwork.io?

Clockwork.io develops software that helps large AI workloads continue running when hardware or network failures occur.

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Its fault-tolerance platform works between the hardware and AI workloads. LinkPass can reroute traffic around failed network links, while TorchPass can move work from a failing GPU to a healthy one so training can continue without restarting.

The company has also introduced two new TorchPass capabilities. Multi-node platform snapshots save the state of a distributed AI job across every node, while fast application checkpoints capture updated model weights in the background during reinforcement learning.

These tools are designed to reduce downtime, repeated computation, and idle GPU capacity when failures occur.

About Clockwork.io

How Much Funding Has Clockwork.io Raised?

Clockwork.io has raised $31 million in its latest funding round, bringing its total funding to $73 million.

The source does not provide details about the company's previous funding rounds.

Who Invested in Clockwork.io’s Funding Round?

Premji Invest, Wing Venture Capital, and Seligman Ventures co-led the latest funding round.

NEA and e& Capital also participated.

Clockwork.io funding

Why the Funding Matters

Clockwork.io will use the new funding to expand enterprise adoption and scale delivery through cloud partners.

The company is also expanding TorchPass with capabilities designed to improve recovery from failures in large distributed AI workloads. Platform snapshots can preserve the state of an entire running job across multiple nodes, while fast asynchronous checkpoints can reduce the amount of work that needs to be repeated.

Who founded Clockwork.io?

Clockwork.io was founded by Balaji Prabhakar, Deepak Merugu, and Yilong Geng.

The company is headquartered in Palo Alto, California.

"Failures are inevitable at AI scale. Losing hours of useful work to them should not be," said Suresh Vasudevan, CEO of Clockwork.io. "Fault tolerance is a goodput multiplier: it keeps GPUs doing useful work instead of waiting for recovery or repeating work already done.

"At AI infrastructure scale, a single network issue should never sideline healthy GPUs or interrupt running workloads. Before Clockwork.io, one InfiniBand NIC flap could remove an eight-GPU server from service, while a switch port flap could drain a second server, doubling the impact to 16 GPUs," said Raghu Hiremagalur, SVP, CTO Infrastructure, LinkedIn.

Market and Business Growth

Large AI workloads can run across thousands of GPUs, where a single failed GPU, network link, or server can interrupt an entire job.

Clockwork.io's LinkPass and TorchPass solutions are already in production. The company says enterprises operating their own GPU fleets, hyperscalers, and neoclouds are adopting its technology to increase the amount of GPU time spent on useful work.

Together AI is also bringing TorchPass to market as a service on its GPU Clusters. The companies plan to demonstrate a multi-node training job continuing through injected network and GPU failures without restarting.

Clockwork.io Market and Business Growth

What Happens Next?

Clockwork.io plans to expand enterprise adoption and increase delivery through cloud partners.

The company will continue developing its fault-tolerance technology for distributed AI workloads, including training, reinforcement learning, and inference.

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