Cut AI Model Costs Without Losing Quality via MCP.
Your GPT-4o bill is $4,200/month and 60% of those calls could run on Groq for $0.003 , your agent finds the waste
Works with every AI agent you already use
…and any MCP-compatible client
Waiting for input…
Gemini How It Works
Your AI agent pulls your LLM usage data from Helicone , every request from the last 30 days with model, token count, cost, latency, and the request pattern.
It categorizes each request type: classification (short input, boolean/enum output), summarization (long input, short output), generation (variable input, long output), structured extraction (variable input, JSON output).
For classification and extraction tasks, the agent checks Groq's model catalog: Llama 3.1 70B on Groq runs at 300 tokens/second and costs $0.59/M input tokens.
GPT-4o costs $2.50/M input tokens. For a classification pipeline making 10,000 calls/day with 500 tokens average, that is $12.50/day on GPT-4o versus $2.95/day on Groq.
The agent writes the full analysis to Google Sheets: pipeline name, current model, current cost, recommended model, projected cost, savings, and risk assessment.
Tab two shows the 30-day projection: $4,200 current $1,680 optimized. $2,520/month in savings by routing the right calls to the right model.
MCP Server Orchestration: 3 MCP Servers, one intelligent agent
Connect Helicone, Groq and Google Sheets MCP servers so your AI agent analyzes your LLM request logs from Helicone, identifies calls that can be routed to Groq's fast inference for 10-50x cost reduction, and builds a cost optimization report in Google Sheets. Teams spending $3,000-10,000/month on OpenAI who have never audited which calls actually need GPT-4o and which are classification tasks that Llama 3 handles fine get the answer in a spreadsheet.
Helicone Llm Observability
triggerAnalyzes LLM request patterns , model, tokens, cost, latency per call
query_requests query_costs query_latency query_prompts Groq
enrichmentProvides Groq model pricing and latency benchmarks for comparison
list_models get_model chat_completion Google Sheets
actionBuilds the cost optimization report with savings projections
append_sheet_values update_sheet_values get_spreadsheet create_spreadsheet Run This Automation Today
Connect Claude, ChatGPT, Cursor, or any AI agent to the Vinkius catalog and run this automation in minutes.
Build Your Own MCP
Turn any internal API into an MCP server. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Connect & Automate
The 3 servers this recipe uses are ready in the catalog. Connect them once, paste a prompt, and your AI runs the full workflow.
- Helicone Llm Observability, Groq & Google Sheets ready in the catalog right now
- Add more from 4,700+ servers whenever you need
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers and recipes added every week
Superpowers you didn't know your AI had
The Vinkius catalog gives your agent access to 4,700+ MCP servers and the intelligence to combine them. Imagine never logging into another dashboard. Your AI handles the work across every tool, in one conversation. That's what this infrastructure was built for.
Cross-Platform Intelligence
Your agent doesn't just connect to tools. It understands the relationships between them. Data flows where it needs to go, automatically, with full context preserved across every platform.
Contextual Reasoning
Every decision your agent makes considers the full picture. It reads CRM data, checks calendars, reviews conversation history, and acts on everything at once. Not step by step. All at once.
Productivity at Scale
What used to take 45 minutes across five different dashboards now takes one sentence. Your agent runs the entire workflow end to end while you focus on decisions that actually matter.
Zero-Config Reliability
No API keys to paste. No webhooks to configure. No YAML to debug. Connect your MCP servers once, and your agent handles the rest. Every time, without intervention.
Made for
exactly this
Your AI agent taps into the entire Vinkius MCP catalog to handle these for you. You describe what you need. It does the rest.
AI engineering teams spending $3,000-10,000/month on OpenAI who have never audited which calls actually need a frontier model
CTOs who need a monthly LLM cost report with actionable optimization recommendations for the board
Platform teams evaluating Groq as a cost-reduction strategy for high-volume, low-complexity LLM workloads
Startups approaching their OpenAI spending cap who need to reduce costs without degrading product quality
Frequently Asked Questions About This MCP Server Orchestration
Which MCP servers do I need for this workflow?
Three: Helicone, Groq and Google Sheets. Connect all three to your AI client before running any prompt from this page.
Does this work with Claude Desktop, Cursor or Windsurf?
Yes. Any AI client that supports the Model Context Protocol works , Claude Desktop, Cursor, Windsurf, Cline and others. Connect the MCP servers and paste a prompt.
Do I need to already use Groq?
No. The agent uses Groq's model catalog and pricing for comparison. You do not need to route traffic through Groq until you decide to migrate. The report shows what you would save.
What if I use Anthropic instead of OpenAI?
Helicone tracks any LLM provider. The cost comparison works the same , the agent compares your current per-token cost with Groq's pricing regardless of which provider you use today.
Is my usage data secure?
MCP servers authenticate through API keys. Helicone usage data stays in your account. The Google Sheet is in your Drive. Vinkius does not store your LLM usage data.
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MCP servers used in this workflow
Helicone (LLM Observability)
Helicone (LLM Observability) tracks your AI usage in real-time. Monitor requests, analyze costs per model or user, and measure latency across all LLM providers. You can also track multi-turn session graphs, manage prompt versions, and log user feedback directly through your agent. It gives you full visibility into your AI spend and performance.
Groq
Groq MCP Server. Get blazing-fast LLM inference by connecting your AI agent to Groq's LPU-accelerated endpoints. Run chat completions using Llama 3 or Mixtral, transcribe audio files, translate non-English audio to English text, and enforce structured JSON output—all with minimal latency.
Google Sheets
Google Sheets MCP Server lets your AI client read, write, and manage data directly in Google Sheets. Use conversational commands to pull data from specific ranges, append new rows, or structure entire spreadsheets. It acts as an analyst, letting you manipulate complex data without opening the GUI or writing formulas.