CoreWeave Direct-to-Expert: Work With the Engineers Who Built CoreWeave Cloud

CoreWeave Direct-to-Expert | AI Infrastructure Support

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Standing up a training cluster and keeping an inference fleet alive under real traffic are two different jobs, and most teams are expected to do both, often without in-house expertise on either. A failed job tells you something broke but doesn't tell you where. Any investigation must cross storage , networking , and the scheduler, while the fleet sits idle and you burn cash. It’s not a job for the inexperienced. When we built CoreWeave Cloud , we made a decision about how we'd work with customers: no non-technical layer between you and the person who can fix your issue. That's how we've worked with the frontier labs since the first frontier models were trained, and it's how we work with enterprises now. CoreWeave Direct-to-Expert connects you to engineers cross-trained across the stack, which is why the first person who responds is usually the one who resolves the problem. CoreWeave Direct-to-Expert runs in a shared channel with your team. You post a problem there. A human responds in minutes and pulls in the engineer who owns the layer in question, and that engineer stays on it through resolution. Nobody asks your team to re-explain the environment to someone seeing it for the first time.

We train frontier models on CoreWeave, and at that scale the hard problems show up mid-run, when every restart costs us real time. What changed is who we talk to when that happens. We aren't filing a ticket and waiting for it to climb a queue. The CoreWeave engineer is already in the thread with us, knows our environment, and stays on the problem until we have the actual reason. That has saved us many hours of debugging and investigation on runs we couldn't afford to lose. It's less like vendor support and more like working with a dedicated teammate. Sam Braun, Member of Technical Staff at Cohere. The work starts before production CoreWeave Direct-to-Expert begins before your first incident. Most teams work with our engineers long before anything is running in production: scoping the workload, sizing the cluster, and testing it on the hardware they'll actually deploy on. CoreWeave ARENA is where that happens. It's a structured lab where you run your real models and pipelines on production AI infrastructure, with our engineers alongside your team. We've run hundreds of tests. Some end with us telling a team its workload doesn't need what they came in asking for. IBM's sandbox workloads weren't a fit for a standard capacity plan. It is bursty, unpredictable, and nothing like the steady-state training runs we'd sized for before. CoreWeave engineers sat down with us early to shape the design around our requirements: how sandboxes would scale, what we thought storage and networking needed to look like, and how to absorb concurrent bursts. They matched the design to our existing network and access setup instead of asking us to adapt to theirs. As we increased the volume and variety of traffic across our local hardware and burst into additional CoreWeave Sandboxes capacity, we worked together to make the customer experience as seamless as possible, allowing us to utilize our own hardware efficiently and burst into additional capacity as needed. Brian Belgodere, Senior Technical Staff Member at IBM The right engineer, right away Every cloud provider will probably put the right expert on a problem, eventually. What’s changed in the era of AI is how fast a resolution is required, how deep the expertise has to run, and the cost of waiting. Signing for capacity and running a production pipeline on it are separated by a lot of unglamorous work: scheduler configuration, storage layout, checkpointing strategy, and tuning for the specific shape of your workload. Some clouds hand you documentation at this point and leave you to solve the problem. CoreWeave is with you every step of the way. The engineer who sized your cluster in CoreWeave ARENA runs that onboarding alongside your team, plans your migration off legacy infrastructure, and stays with you through cutover. If a job fails months into production, that's still who you reach out to in your channel. Most AI problems don't respect org boundaries. A storage bottleneck shows up as a NCCL timeout. Our teams don't work them separately: the engineers who own storage, networking, and scheduling are in the same channel as the customer, at the same time. Peter Salanki, CTO at CoreWeave Experience writes the playbook The first generation of frontier models were trained on infrastructure we built and ran. Our engineers were running those clusters at a scale nobody had reached before, tackling failures nobody had seen. Today, 9 of the 10 leading foundation model providers rely on CoreWeave. Your team works with the same engineers. Enterprises face their own domain challenges: an existing data estate, a regulated environment, a security review that has to pass before anything moves. Our engineers have worked on these problems too. And as AI evolves, there will be new problems to solve. Nobody has a reference architecture for agentic systems. How they behave under real traffic is still being worked out in production. That's the work our engineers are in: rolling up their sleeves, working with you, and building one in real time. Keep your specialists on the science In physical AI , robotics, and simulation-heavy work, the people closest to the problem are domain experts. They know the vehicle dynamics, the material properties, the failure modes of the product being modeled, and they don't want to spend a day tuning a scheduler in the middle of a validation run. CoreWeave Direct-to-Expert bridges that gap two ways: Our infrastructure engineers hold the scheduler, storage, and networking layer so your specialists can stay on the simulation, the training run, or the model, Through Physical AI Field Engineering , engineers with the same domain backgrounds—automotive, aerospace, or mechanical—embed directly with your team to build that model with you, not just keep the infrastructure under it running.

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