"Equipment or items used in windsurfing" matching MCP servers:

Enables users to describe their LLM fine-tuning job once and get the cheapest, fastest, and most balanced GPU options across a dozen cloud providers in seconds.

Enables users to describe an LLM fine-tuning workload and receive ranked, cost- and runtime-optimized GPU recommendations across multiple cloud providers by estimating VRAM and FLOPs.

Enables users to describe an LLM fine-tuning job once and receive the cheapest, fastest, or most balanced GPU hardware options across a dozen cloud providers by calculating required VRAM/FLOPs and aggregating live pricing.

Enables users to describe an LLM fine-tuning job and receive ranked, task-aware GPU recommendations with estimated VRAM/FLOPs, cost, and runtime across more than ten cloud providers.

Enables users to describe an LLM fine-tuning workload once and get task-aware VRAM/FLOPs estimates plus ranked cheapest, fastest, and balanced GPU recommendations across multiple cloud providers.

Enables users to describe an LLM fine-tuning job and receive the cheapest, fastest, or most balanced GPU options across multiple cloud providers by calculating required VRAM and FLOPs.

Enables users to describe an LLM fine-tuning job and receive ranked GPU recommendations across a dozen cloud providers based on estimated VRAM/FLOPs, live pricing, cost, and speed. It returns cheapest, fastest, and balanced hardware options without manual comparison.

Enables users to describe an LLM fine-tuning workload once and get ranked, cost- and speed-optimized GPU recommendations across multiple cloud providers by estimating VRAM/FLOPs requirements.

Enables users to describe an LLM fine-tuning job once and get ranked GPU hardware recommendations with estimated VRAM/FLOPs, cost, and runtime across a dozen cloud providers.

Enables users to describe an LLM fine-tuning job once and receive VRAM/FLOPs-aware recommendations for the cheapest, fastest, or most balanced GPU options across a dozen cloud providers.

It enables users to describe an LLM fine-tuning workload once and receive task-aware VRAM/FLOPs estimates plus ranked cheapest, fastest, or balanced GPU recommendations across multiple cloud providers.

It enables users to describe an LLM fine-tuning workload once and automatically estimates required VRAM/FLOPs, compares live GPU pricing across cloud providers, and returns ranked cheapest, fastest, or balanced hardware recommendations.

Enables users to describe an LLM fine-tuning workload and receive ranked, task-aware GPU recommendations by estimating required VRAM/FLOPs and comparing live pricing across a dozen cloud providers.

Enables users to describe an LLM fine-tuning job once and receive ranked GPU recommendations across many cloud providers based on estimated VRAM/FLOPs, cost, and runtime.

Enables users to describe a fine-tuning workload and receive ranked GPU cloud recommendations based on required VRAM/FLOPs, cost, and runtime across multiple providers.

Enables users to describe a fine-tuning workload once and receive task-aware VRAM/FLOP estimates plus ranked cheapest, fastest, and balanced GPU options across multiple cloud providers. It can be used from Claude Code/Desktop or CLI/API to identify viable hardware options without manually comparing provider pricing.

Enables users to describe an LLM fine-tuning job in natural language and receive ranked GPU recommendations from a dozen cloud providers, based on estimated VRAM and FLOPs requirements. It surfaces the cheapest, fastest, and most balanced hardware options while disclosing the source and freshness of each price.

Enables users to describe an LLM fine-tuning workload once and receive ranked cloud GPU recommendations with estimated VRAM/FLOPs requirements, cost, runtime, and data-source transparency.

Enables users to describe an LLM fine-tuning workload and receive VRAM/FLOPs estimates plus ranked GPU recommendations across multiple cloud providers, optimizing for cost, speed, or balance.

Enables users to describe an LLM fine-tuning workload once and receive ranked, task-aware GPU recommendations across a dozen cloud providers based on required VRAM/FLOPs, cost, and runtime. It surfaces the cheapest, fastest, and balanced options through MCP so coding assistants can plan training hardware without manual price comparison.

Enables users to describe an LLM fine-tuning job once and get the cheapest, fastest, or most balanced GPU options across multiple cloud providers by estimating required VRAM and FLOPs.

Enables users to describe an LLM fine-tuning workload once and get task-aware VRAM/FLOPs calculations with cheapest, fastest, and balanced GPU recommendations across a dozen cloud providers. It supports CLI, REST API, and natural-language MCP integration without manual price comparison.

Enables users to describe an LLM fine-tuning workload and automatically estimate VRAM/FLOPs to recommend the cheapest, fastest, and balanced GPU options across multiple cloud providers. It supports natural-language requests through MCP, returning structured ranked hardware recommendations without manual price comparison.

It enables users to describe an LLM fine-tuning workload once and receive ranked GPU recommendations for cheapest, fastest, or balanced options across many cloud providers by estimating VRAM/FLOPs and aggregating live pricing.

Enables state-aware Terraform/OpenTofu plan evaluation against live AWS topology and CloudWatch telemetry, detecting reliability blast radius and FinOps waste before merge.

Enables users to describe an LLM fine-tuning job in natural language so the server estimates required VRAM/FLOPs and searches live GPU pricing across a dozen cloud providers. Returns ranked cheapest, fastest, and balanced hardware recommendations with each result's data source clearly identified.

Turns a plain-language description of an LLM fine-tuning job into ranked hardware picks by estimating the required VRAM and FLOPs, then comparing live pricing across a dozen GPU clouds. Returns the cheapest, fastest, and most balanced configurations for a given budget or time limit, with each result labeled by its data source.

Enables users to describe an LLM fine-tuning job once—in natural language or via YAML—and receive VRAM/FLOPs estimates plus ranked, Pareto-optimal GPU configurations (cheapest, fastest, and balanced) aggregated from live pricing across a dozen cloud providers. It integrates with Claude Code and Claude Desktop as well as a CLI and REST API, disclosing the source and freshness of every recommendation.

Enables users to describe an LLM fine-tuning job once and receive ranked GPU recommendations across a dozen cloud providers, calculated from the workload's required VRAM and FLOPs. It surfaces the cheapest, fastest, and most balanced hardware options with clear data provenance, callable from Claude Code, Claude Desktop, a CLI, or a REST API.

An unofficial MCP server for Percona Everest that enables AI-powered interaction with database clusters, including listing, retrieving details, credentials, and components.

AI infrastructure design agent. Describe your app in plain English; Riley designs, prices, and deploys AWS or GCP infrastructure with generated Terraform.

Enables querying live AWS spot price arbitrage signals and suppression logs directly in Claude Code or any MCP-compatible AI tool.

Deploys and manages projects on Swigs Cloud from any MCP client, turning folders or git repositories into live HTTPS sites in about two minutes.

Enables coding agents to answer from what is actually true in the current Terraform working directory: the provider schemas pinned in the lock file, saved plans, redacted state, and local or installed module interfaces. It also reports what a pinned provider upgrade would break, all read-only and without ever running init in the root module.

Gives AI agents strictly read-only query access to PostgreSQL, Oracle, Cosmos DB, Blob Storage, Log Analytics, and Redis, returning each query as a paged or downloadable result set in Parquet or NDJSON rather than inline rows.

Azure PIM role activation MCP server allowing role activation through natural language, with support for saving frequently-used roles as favorites and activating them with a single command.

MCP server that lets Claude (or any MCP-compatible client) compare on-demand compute + storage pricing across AWS, Azure, and GCP in real time.

Enables querying and editing configuration items in Apollo Config Center using portal login, with support for multiple environments, clusters, and namespaces.

Enables large language models to query and operate on Apollo Configuration Center. Supports managing apps, clusters, namespaces, config items, and releases.

An MCP server that enables AI assistants to interact with Azure DevOps projects, work items, repositories, pipelines, wikis, and more via 21 tools.