Wafer DigitalOcean B2B2C Integration

This setup turns Wafer into an intelligence layer inside someone else's cloud storefront, which is a much lighter way to scale than selling raw GPU capacity itself. Wafer does the hard technical work, tuning kernels, runtimes, and serving paths so models run faster or cheaper, while DigitalOcean wraps that into a product with one endpoint, usage based billing, account ownership, and support.

• DigitalOcean already presents inference as a single managed surface, with a model catalog, OpenAI compatible endpoints, serverless and dedicated deployments, and routing controls. That makes Wafer easiest to slot in below the API layer, where the customer sees DigitalOcean's product, not a separate vendor workflow.
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• The technical value Wafer contributes is concrete. In DigitalOcean's published benchmark work, Wafer's optimization system improved throughput on frontier models running on AMD GPUs, including a reported 11.33x speedup in one case. That is the kind of gain a cloud partner can package into better economics or better latency for its own customers.
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• This model also fits fragmented enterprise adoption. Regulated buyers often want dedicated endpoints, residency controls, SLAs, and one accountable infrastructure vendor. Wafer already appears to be operating in that direction, including healthcare deployment context through Neon Health, which makes partner led distribution more credible than a pure self serve model.
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The next step is broadening from one cloud relationship into a repeatable channel across clouds, gateways, and managed platforms. If Wafer becomes the hidden optimization layer under multiple inference products, it can compound distribution through partners, reach regulated enterprise workloads faster, and grow without carrying the balance sheet burden of owning every customer contract and GPU cluster.