Terafab builds compute. ComputeFlow routes it.
Paste a workload. ComputeFlow tells you where it should run — across cloud GPUs, private clusters, sovereign data centers, edge nodes, and future orbital and Terafab-scale supply — and exactly what trade-offs you are making.
Two halves of the same future. Different bets.
The hard part of the next decade isn't only building compute. It's deciding where each workload should run. Terafab is going to win the first half. ComputeFlow is going to win the second.
The factory bet
- Builds chips
- Owns infrastructure
- Vertical integration
- Capital-heavy
- Physical scale
- Years to ramp
- One massive supply-side bet
The routing bet
- Routes compute
- Coordinates infrastructure
- Software-defined
- Asset-light
- Network scale
- Works now
- Benefits from every new compute source online
“Owning compute is step one. Routing compute is step two.”
Owning compute is hard. Using it well is harder.
Every team running serious AI infrastructure is making the same routing decisions in slack threads, spreadsheets, and tribal knowledge. That doesn't scale.
Hyperscalers, neoclouds, sovereign clusters, private DCs, edge mesh, and tomorrow's orbital + GW-scale supply. No team can shop across all of it manually.
Cost, latency, energy, sovereignty, and risk move together — but most teams pick a vendor on price, then discover the trade-offs later.
Customer data, regulators, and procurement teams are all rewriting where workloads are allowed to run.
Quota changes, outages, new supply coming online — your routing plan should change with them. Spreadsheets can't.
The neutral control plane for global compute
ComputeFlow makes every compute source — cloud, private, sovereign, edge, orbital, future GW-scale — feel like one programmable surface.
Cloud GPUs, private clusters, sovereign DCs, edge, and orbital — all routed from one place, with one mental model.
Cost, latency, energy, sovereignty, risk, and availability — scored locally, deterministically, every time.
Never bet on one supplier. ComputeFlow recommends a weighted split across the top three pools.
When new compute comes online — anywhere — ComputeFlow turns it into usable capacity in the same UI.
From workload to routing plan in under a minute
ComputeFlow is the routing layer. Every workload runs through the same loop: define, score, split, explain.
Name it, pick a workload type, set budget, latency, energy, sovereignty, reliability, region, and urgency in plain UI.
Local scoring engine evaluates 7 compute pools across cost, latency, energy, sovereignty, risk, and availability.
ComputeFlow returns a weighted split across the top three routes so you don't depend on a single supplier.
You get a recommendation, why it won, what trade-offs you made, and the exact operational next step.
Try the routing engine. Right here.
No signup. No paid APIs. Adjust the workload, run the engine, and see how the routing plan changes in real time.
Workload definition
Scoring runs locally. No paid APIs, no telemetry, no surprises.
Route Frontier-7B finetune to Private AI Datacenter
For a training workload with a $80,000 monthly envelope, Private AI Datacenter is the best primary route. We split 35% to Private AI Datacenter, 33% to Sovereign EU Cluster, and 32% to Research Supercomputing so you do not depend on a single supplier.
Private AI Datacenter won on cost efficiency, operational reliability.
Provision a pilot allocation on Private AI Datacenter this week, mirror to the #2 pool for failover, then expand the split once steady-state cost and latency are confirmed.
- Strong unit economics at ~$1.90/GPU-hr.
- Median latency near 22ms suits this workload.
- Operationally mature with 95% reliability history.
- Pool is purpose-fit for training workloads.
- Median latency near 48ms suits this workload.
- 88% clean energy mix.
- Meets restricted data requirements with margin.
- Operationally mature with 93% reliability history.
- Strong unit economics at ~$1.40/GPU-hr.
- Meets restricted data requirements with margin.
- Operationally mature with 90% reliability history.
- Pool is purpose-fit for training workloads.
Seven compute pools. One programmable surface.
ComputeFlow already models the full stack of compute supply — from elastic cloud GPUs to orbital nodes and future GW-scale fab capacity.
Elastic H100 / B200 capacity across major regions.
Reserved GPU pods. Predictable cost, dedicated tenants.
Data never leaves the EU. Audited supply chain.
Millisecond inference next to the machines that need it.
Cheap FLOPs for long-horizon training and physical simulation.
Solar-only batch compute above the weather and the grid.
When new GW-scale supply ships, ComputeFlow routes onto it.
Built for everyone running serious AI infrastructure
ComputeFlow is the routing layer, so it sits underneath whatever you're already building. It doesn't replace your stack. It coordinates it.
Split training across private + research pools, pre-route inference to clouds, reserve forward capacity.
Centralize cost, sovereignty, and risk policy across product teams without a procurement bottleneck.
Stitch national HPC, sovereign clusters, and cloud bursts together for long-horizon simulation.
Route real-time inference to edge mesh, batch training to private DCs, keep policies portable.
Forward-route batch workloads to orbital nodes when the energy and latency math works.
Hard data-residency policy at the routing layer — workloads never plan against forbidden pools.
The window is opening. The routing layer is unowned.
Compute supply is exploding. Sovereignty is hardening. AI is the workload. Five years from now, the routing layer is one of the most valuable software companies in infrastructure.
Companies like Terafab are racing to bring massive new compute online. That supply needs routing.
Regulators are forcing data residency. Routing has to become a first-class control.
Off-grid compute is no longer a thought experiment. It needs a control plane the day it ships.
Single-vendor lock-in is no longer acceptable. Routing across vendors is the new default.
Training and inference are the new center of gravity. Routing AI compute is the highest-leverage layer.
Outages, quotas, geopolitics. CIOs need a layer that adapts instead of failing over after the fact.
Start free. Scale as capacity scales.
ComputeFlow is asset-light, so pricing is asset-light too. Pay for the routing intelligence, not the iron.
- 5 routing simulations / month
- All 7 compute pools
- Cost, latency, energy, sovereignty scores
- Single-user, no saved scenarios
- Unlimited routing simulations
- Saved scenarios + scenario diffs
- Deeper scoring + risk explanations
- Cost forecast across 12 months
- Email + slack delivery of reports
- Everything in Pro
- Shared workload queues
- Org budgets + spend alerts
- Workload routing policies
- Collaboration + audit trail
- Private connectors to your clusters
- SOC 2, ISO, residency controls
- Custom routing policy engine
- Procurement, SLAs, support pod
- Onboard new compute as it appears
From routing UI to programmable control plane
We build in phases. Each phase is shippable on its own. Together they become the operating system for global compute.
- Six-axis scoring across 7 compute pools
- Recommended split allocation
- Plain-English explanations and warnings
- Command Center dashboard with simulated state
- Real-time pricing + quota feeds for public clouds
- Connectors to private clusters and sovereign DCs
- Org budgets, spend alerts, cost forecast
- Routing policy engine (compliance + procurement)
- SDK + API for workload orchestration
- Automatic re-routing on capacity / risk events
- Marketplace for new compute supply
- Forward-routing for orbital and GW-scale capacity
Honest answers about the routing bet
No. Terafab is building compute. We make compute usable. Routing benefits from every new supply source coming online — including Terafab's. Different layer, friendly rivalry.
You enter a workload, pick a type, set six constraints, and the local scoring engine returns a ranked routing plan with a recommended split, explanations, and an operational next step.
The MVP runs a deterministic local model over seven simulated pools. Phase 2 adds live pricing, quota, and capacity feeds. No paid APIs are required for the MVP.
No. ComputeFlow is software-defined and asset-light. You can run the routing layer side-by-side with your existing orchestration, then connect feeds in phase 2.
Free for exploration, Pro for individual operators, Team for platform teams, Enterprise for sovereign and procurement programs. We price the routing intelligence, not the iron.
Because the value is in the routing decision, not in owning the GPUs. Every new compute source that comes online — cloud, sovereign, edge, orbital, fab-scale — makes the routing layer more valuable, not less.
Don't bet on one factory. Route across all of them.
Whenever a new GPU lights up, a new cluster comes online, or a new compute source goes live, ComputeFlow turns it into usable capacity for your team.
“The future is not only who owns compute. It is who controls where compute flows.”
Owning compute is step one. Routing compute is step two. ComputeFlow is step two.