MetaOps: DevOps, Shaped for Silicon
DevOps practice reworked around the SoC development flow, on infrastructure that stays inside your own network.
What MetaOps Is
DevOps practice was built for software teams, and it does not transfer to silicon unchanged. Simulation runs take hours or days rather than minutes. Design data is measured in terabytes. Tool licenses are finite and expensive. MetaOps is that practice reworked around those constraints: infrastructure, automation, and the ongoing tuning that keeps both useful as a project moves through its phases.
It is a product in the sense that there is something to install and operate, and a service in the sense that it does not stand still. A configuration that suits early block-level work may no longer fit during SoC regression, so the system is reviewed and retuned as the flow changes.
Where the Time Goes
A Typical Environment
- Workstations assigned per engineer and idle half the time
- More machines added, yet throughput barely moves
- Regression status is something you ask someone for
- Open-source tooling abandoned because the network is closed
With MetaOps
- Idle capacity returned to the pool and redirected
- Storage and bandwidth tuned so compute is actually fed
- A dashboard you look at instead of a person you interrupt
- External packages available inside the network, under control
Your Data Never Leaves Your Network
MetaOps runs on on-premises hyper-converged infrastructure. Design data, PDKs, and simulation results stay inside your own network, under your own access controls.
There is no cloud tenancy to review, no data residency question to answer, and nothing to explain to your customers or your foundry partner about where their IP is held. In many situations that is not a preference but a requirement.
What It Changes
Capacity from Utilization, Not Procurement
Each engineer gets a dedicated virtual machine, so nobody waits for a shared workstation to free up. Your administrators set the reclamation policy and enforcement is automatic: machines that sit idle are returned to the pool and redirected to where compute is actually needed. Peak demand is met by moving capacity rather than buying it.
Parallel Execution Across a Worker Pool
Simulations are dispatched to a pool of worker nodes and run in parallel, so a large batch finishes in the time the pool can absorb it rather than the time one machine would take. Reclaimed capacity feeds the same pool, which grows and shrinks with demand instead of sitting idle between peaks.
Storage and Bandwidth, Tuned for the Flow
Putting design data on shared storage is the easy part. Keeping it fast when the whole flow runs at once is not: synthesis reading libraries, simulation writing waveforms, and implementation moving large databases all compete for the same disks and the same links. MetaOps tunes storage and network bandwidth around the SoC development flow, so throughput holds up under load instead of leaving jobs queued behind an I/O bottleneck that never shows up in a CPU graph. Capacity is only useful if the data can reach it.
Regression, Scheduled or On Demand
Regressions run on a schedule or on request, whichever the situation calls for. A dashboard shows what is running, what passed, and where coverage stands, so status is something you look up rather than something you ask someone for.
Visibility and Version Control
Git-based source management keeps design and verification code under review and rollback. Performance monitoring shows how the system is actually being utilized, so slowdowns get diagnosed rather than guessed at.
Open-Source Tooling Inside a Closed Network
An air-gapped environment usually means engineers give up on external libraries: no package index, no dependency resolution, and whatever ends up installed differs from machine to machine. MetaOps proxies package indexes through a controlled internal endpoint, so Python and Rust ecosystems stay available and consistent, vetted before they enter the network rather than carried in by hand.
Multi-Vendor EDA Support
MetaOps works with the tools your team already uses. Flows are built around multiple EDA vendors rather than assuming a single toolchain, so adopting the infrastructure does not mean rebuilding the design environment around it.
It Does Not Stop at Handover
Reclamation thresholds, bandwidth allocation, and pool sizing are not settings you get right once. What suits early block-level work may not fit during SoC regression, and a system tuned six months ago can quietly drift away from the work in front of it.
Regular review and retuning are part of MetaOps rather than an add-on. We built this infrastructure to run our own projects before we offered it to anyone else, and our work still runs on it, so the tuning comes from operating it daily rather than from a manual. Teams that would rather not run the infrastructure at all can hand operation to us entirely and keep their engineers on design work.
7+
Customer companies
150+
Engineers using MetaOps
On-Prem HCI · VM Pooling · Worker Pool Parallelism · Storage & Bandwidth Tuning · Regression Orchestration & Dashboard · Git-Based Source Management · Performance Monitoring · Internal Package Proxy · Multi-Vendor EDA Support
Ready to Streamline Your Flow?
Tell us how your team runs today and where the bottlenecks are. We will come back with a concrete view of what we would change first.