Track LLM Cost vs Quality Using Connectors.
Your OpenAI bill grew from $200 to $2,400 in 2 months and you have no idea which feature caused it , because you track API spend at the account level, not at the prompt level
Works with every AI agent you already use
…and any MCP-compatible client
Waiting for input…
Gemini How It Works
Your AI agent pulls the last 7 days of LLM traces from Langfuse: every prompt chain, every intermediate step, every quality score, every error.
It crosses this data with Helicone's cost analytics: cost per request, cost per user, cost per feature, token consumption by model.
The result goes to Google Sheets as a multi-tab dashboard. Tab 1 , Cost Attribution: 'Feature X costs $847/month (42% of total).
It uses GPT-4 for a classification task that GPT-3.5-turbo handles at 94% accuracy for $31/month.' Tab 2 , Quality Trends: 'The summarization prompt scored 4.2/5 average last week, down from 4.6 two weeks ago.
The June 1 prompt update degraded quality. Roll back to v3.' Tab 3 , Latency Analysis: 'P95 latency for the chat chain is 8.2 seconds.
Step 3 (RAG retrieval) takes 5.1 seconds , it is the bottleneck, not the LLM call.' Tab 4 , Anomalies: 'User X triggered 340 requests in 1 hour , abuse or legitimate use? Cost impact: $127.' The dashboard turns invisible LLM operations into decisions: which model to downgrade, which prompt to roll back, which feature to optimize.
Connector Orchestration: 3 Connectors, one intelligent agent
Connect Langfuse, Helicone and Google Sheets Connectors so your AI agent pulls LLM trace data from Langfuse , latency, token usage, error rates and quality scores per prompt chain , crosses it with cost and usage analytics from Helicone, and builds a unified observability dashboard in Google Sheets that shows exactly which prompts cost the most, which chains are slowest, and where quality is degrading before your users complain. AI engineers, indie hackers and startup teams running LLM-powered products who notice their API costs climbing but cannot attribute spend to specific features, cannot identify which prompt changes improved or degraded quality, and are flying blind on production LLM performance because 'it works in the playground' is their entire monitoring strategy.
Langfuse Llm Tracing Evals
triggerProvides detailed LLM trace data , latency per step, token counts, quality scores, error chains, and prompt version tracking
list_traces get_trace list_observations get_observation list_scores get_daily_metrics Helicone Llm Observability
enrichmentAdds cost attribution, user-level analytics, request volume patterns, and latency percentiles across all LLM providers
query_requests query_costs query_latency query_users query_sessions list_properties Google Sheets
actionBuilds the unified LLM observability dashboard with cost breakdown, quality trends, and anomaly alerts
create_spreadsheet update_sheet_values append_sheet_values get_sheet_values 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 Connector
Convert any internal API into a Connector. 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 each 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.
- Langfuse Llm Tracing Evals, Helicone Llm Observability & Google Sheets ready in the catalog right now
- Add more from 5,800+ servers whenever you need
- Connections are secured and compliant by default
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers and recipes added weekly
Superpowers you didn't know your AI had
The Vinkius catalog gives your agent access to 5,800+ Connectors and the intelligence to combine them. Imagine never logging into another dashboard. Your AI handles the work across all tools, in one conversation. That's what this connectivity layer 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 all platforms.
Contextual Reasoning
Each 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 Connectors once, and your agent handles the rest. Each time, without intervention.
Made for
exactly this
Your AI agent taps into the entire Vinkius AI Connectors to handle these for you. You describe what you need. It does the rest.
AI engineers tracking which prompts and chains cost the most and where to optimize model selection for 80% cost reduction
Indie hackers monitoring their LLM bills to find the $800 GPT-4 classification that GPT-3.5-turbo handles at 94% accuracy
Startup CTOs building production LLM observability dashboards that connect cost, quality and latency in one view
AI enthusiasts who run multiple LLM-powered tools and want to understand where their money goes and where quality degrades
Frequently Asked Questions About This Connector Orchestration
Which Connectors do I need for this workflow?
Three: Langfuse, Helicone 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 supporting the Model Context Protocol works , Claude Desktop, Cursor, Windsurf, Cline and others.
Do I need both Langfuse and Helicone?
Both provide unique data. Langfuse excels at trace-level quality and chain analysis. Helicone excels at cost attribution and usage patterns. Together, they give complete observability.
Is my LLM data secure?
Connectors authenticate through API keys. Trace data stays in your Langfuse and Helicone accounts. Google Sheets stores aggregated analytics only. Vinkius does not store your LLM data.
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Your GPT-4 API takes 4 seconds per response , Groq returns the same quality answer in 180 milliseconds, Langfuse traces every call, and Sheets shows the latency-cost comparison that makes your product feel instant
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Connectors used in this workflow
Langfuse (LLM Tracing & Evals)
Langfuse (LLM Tracing & Evals) lets you monitor your AI apps in real-time. It connects your AI client to your Langfuse project so you can track traces, manage prompt versions, and audit evaluation scores without jumping between tabs.
Helicone (LLM Observability)
Helicone MCP lets you monitor LLM usage, track costs, and manage prompts directly through your AI agent. It connects your Helicone account to your agent so you can see real-time data on request latency, spend, and user feedback without switching tabs. It's built for teams who need to see exactly what's happening with their AI infrastructure.
Google Sheets
Google Sheets MCP lets you read, write, and manage spreadsheet data through your AI agent. Stop wasting time on manual data entry or complex formulas. Just tell your agent to pull specific ranges, add new rows, or create entire new sheets on the fly. It handles the tedious work of keeping your data organized so you can focus on making decisions.