Hyperbolic

Hyperbolic MCP brings on-demand GPU compute for AI inference directly into your unified MCP server, letting any AI agent request open-source model runs without you having to provision or manage infrastructure. Instead of standing up your own GPU cluster or juggling separate vendor dashboards, you connect Hyperbolic once through BusinessMCP's hosted MCP endpoint at /api/mcp, and every agent in your stack — Claude, GPT, Gemini, or a custom orchestration — can call it the same way it calls your CRM, ad platform, or database tools. This is especially useful for teams that need pay-per-use GPU access for inference workloads (image generation, LLM completions, embeddings, fine-tuned model serving) but don't want to manage a separate integration surface for every model provider.

Because Hyperbolic MCP sits inside BusinessMCP's business-intelligence layer, every inference request, GPU-hour, and model call is logged alongside your other tool usage, giving you a single pane of glass to see how much compute your agents are consuming and which workflows are driving cost. Rather than digging through a GPU vendor's separate billing console, you get inference activity correlated with the rest of your operational data — CRM updates, ad spend, support tickets — so you can answer questions like which customer segments or campaigns are triggering the most model inference, or whether a spike in GPU usage correlates with a product launch. That context is what turns raw GPU access into an actual business-intelligence signal.

Typical use cases include running open-source LLMs for internal chat assistants, generating images or embeddings at scale for content pipelines, batch-processing inference jobs triggered by other MCP-connected tools (e.g., a new record in Postgres kicks off a summarization job), and prototyping with different open-source models without committing to long-term GPU reservations. Because the server is model-agnostic, you can swap which open-source model an agent targets without changing how your agents call the tool — the MCP interface stays constant even as the underlying model changes.

Setup follows the same pattern as every other server in the BusinessMCP catalog: authenticate with your API keys, connect Hyperbolic once, and it becomes available across your entire growth-suite app and to any agent hitting your Bearer-token-protected /api/mcp endpoint. There's no per-agent reconfiguration and no cookie-based tracking to worry about, which keeps the setup GDPR-friendly and portable across whichever AI model or vendor you're currently using. For teams already piping data through vector stores, filesystems, or sequential-thinking tools, adding Hyperbolic MCP means your agents can now reason, retrieve, and also run the actual inference — all through one consistent hosted MCP layer instead of a patchwork of SDKs and dashboards.

If your agents need occasional bursts of GPU-backed inference rather than a dedicated always-on cluster, Hyperbolic MCP's pay-per-use model fits naturally into an agent-driven workflow: request compute only when a task requires it, get results back through the same MCP call structure, and let BusinessMCP's dashboard track the resulting spend and usage patterns over time.