Model training needs GPUs. But most production AI applications — an agent that calls the Anthropic or OpenAI API, a RAG pipeline querying a vector database, a webhook that classifies incoming support tickets — are I/O-bound, not GPU-bound. They spend most of their time waiting on network calls, not doing local matrix math.
For that class of workload, what matters is CPU headroom for concurrent requests, fast NVMe storage for a vector database like Qdrant or pgvector, a stable dedicated IP for webhook callbacks, and full root access to install Python, Node.js, Docker or whatever your agent framework depends on.
Install Python, Node.js, Docker, or any runtime your agent or API framework requires.
Fast read/write for vector databases and embeddings, especially under concurrent queries.
A stable address for webhook callbacks, API endpoints and outbound integrations.
Run containerized agent frameworks, orchestration tools and inference wrappers with root access.
Handle concurrent API requests and background jobs without a single core becoming a bottleneck.
Upgrade CPU, RAM and storage as your request volume and dataset size increase.
Our current lineup is CPU-based VPS; for local model training or GPU inference, a dedicated GPU provider is a better fit. Contact us to discuss your specific requirements.
Yes, tools like Qdrant, pgvector or Chroma run well with full root access and NVMe storage on our Advanced or Pro plans.
Yes, Python or Node.js-based agent frameworks calling external model APIs run comfortably on any of our Linux VPS plans.
Standard or Advanced suits most low-to-moderate traffic AI applications; Pro is recommended for higher concurrency or larger vector datasets.
A general-purpose root-access environment for building and testing AI tooling.
Learn More →Full root access, NVMe storage and a dedicated IPv4 — starting at $9.95/month.
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