DigitalOcean
Cloud infrastructure with a developer-first surface — Droplets, managed data, and GPU compute
DigitalOcean is a cloud platform built around simplicity: Droplets (Linux VMs), App Platform for managed deploys, Managed Databases (Postgres, MySQL, Redis, MongoDB, Kafka), Spaces object storage with a built-in CDN, Kubernetes (DOKS), and GPU Droplets plus GenAI GPU compute for model workloads. Its flat, predictable pricing and clean API make it well suited to lean marketing infrastructure and AI services. We treat it as a fast, cost-transparent home for the apps, data pipelines, and inference workloads that power growth programs.
We run EGGKNITE-built growth and AI services on DigitalOcean using App Platform for stateless web apps and APIs, DOKS for anything that needs orchestration, and Managed Postgres or Redis for state and caching. Spaces backs asset delivery, exports, and warehouse landing zones through an S3-compatible interface with CDN edge caching in front. We provision everything with the doctl CLI and Terraform against the DigitalOcean API, wire deploys into CI, and lean on VPCs, reserved IPs, and cloud firewalls to keep client environments isolated and auditable.
For AI work we stand up GPU Droplets and GenAI compute to host embeddings, fine-tuned models, and retrieval services close to the data they serve, then expose them behind our own gateways. Managed Databases handle vector-adjacent storage and analytics tables, load balancers and monitoring keep latency and uptime in check, and functions cover lightweight event-driven jobs. The result is infrastructure a small team can reason about, spin up per client, and tear down cleanly.
We deploy landing-page backends, tracking endpoints, and lifecycle webhooks on App Platform with Managed Postgres, giving clients a fast, predictable-cost stack that scales with campaign traffic.
Spaces object storage plus Managed Databases form a cost-transparent staging and warehouse layer where we land ad, CRM, and web event data before modeling it for attribution and reporting.
For clients with data-residency or budget constraints, we run embeddings and fine-tuned models on GPU Droplets, serving retrieval and generation behind gateways we control rather than per-token API bills.
For lean, well-scoped workloads DigitalOcean's flat pricing and smaller surface area cut both cost and operational overhead. We still use hyperscalers where a client needs their specific managed services, but many growth apps and pipelines run happily and more cheaply here.
Yes. We host embeddings, retrieval, and fine-tuned models on GPU Droplets and GenAI compute, backed by Managed Databases and Spaces, which keeps inference close to client data and costs predictable.
Everything is provisioned as code with Terraform and doctl, isolated per client with VPCs, cloud firewalls, and separate projects, so each environment is reproducible, auditable, and easy to tear down.
Let's grow.
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