Where Should You Host MCP Servers? One Isolated Environment Per Server, Not a Shared Box
Blog post from Qovery
MCP servers should be deployed as separate isolated workloads rather than combined on shared virtual machines, containers, processes, or credential files, because shared hosting expands credential blast radius, weakens auditability, and allows one workload to consume resources needed by others. Each server should have a dedicated identity with least-privilege permissions, default-deny network egress, CPU and memory limits, short-lived credentials where possible, and structured tool-call logs sent to an append-only external sink that can be correlated with cloud audit logs. Hosting should be selected according to the server’s trust level: stateless public tools may suit edge runtimes such as Cloudflare Workers, while servers accessing private data or production systems should run in an organization’s own cloud account or Kubernetes environment, with managed services such as Fargate, Cloud Run, Azure Container Apps, or Lambda fitting different operational needs. Servers with write, destructive, or model-generated-code capabilities require stronger safeguards including approval gates, sandboxing, resource quotas, and tightly scoped credentials, since prompt-based policies alone are vulnerable to prompt injection and tool poisoning. The discussion also distinguishes hosting platforms from complementary gateway, policy, and secret-management layers, and presents Qovery as an internal developer platform intended to automate isolated deployments, RBAC, previews, and operational workflows within a customer-controlled cloud environment.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| MCP | 137 | 415 | 24 | 14 | -96% |
| Kubernetes | 36 | 243 | 12 | 5 | -95% |
| Secrets Management | 10 | 61 | 14 | 9 | -98% |
| Serverless | 5 | 12 | 4 | 2 | -99% |
| Platform Engineering | 3 | 73 | 12 | 4 | -96% |
| AI Coding Assistant | 2 | 31 | 7 | 6 | -98% |
| LLM | 2 | 83 | 19 | 14 | -99% |
| AI Agents | 1 | 64 | 25 | 14 | -99% |
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