Connectors to Find Your Most Expensive APIs.
API traffic metered, cache savings calculated, origin load measured, cost projections generated , optimize your API infrastructure costs with data
Works with every AI agent you already use
…and any MCP-compatible client
Waiting for input…
Gemini How It Works
Your AI agent reads Datadog: 4.2M API requests in the last 7 days. Top endpoint: `/api/products` at 1.8M requests (43% of total traffic).
Average response time 45ms, average response size 2.1KB. Second: `/api/search` at 890K requests, average 340ms, average 8.4KB response. The agent reads Cloudflare: zone-level cache hit ratio is 72%.
But per-endpoint analysis shows `/api/products` has a 91% cache hit ratio , only 162K of 1.8M requests reach the origin.
While `/api/search` has a 12% cache hit ratio , 783K requests hit the origin because search queries are unique. The agent calculates: `/api/products` cache saved 1.64M origin requests 2.1KB = 3.4GB of origin bandwidth.
At $0.09/GB, that is $306 saved this week. `/api/search` could save more if you implement query normalization and cache similar searches.
Estimated saving: $180/week. It writes to Google Sheets: endpoint-level traffic, cache ratios, origin load, bandwidth costs, and optimization recommendations. After 4 weeks, you see which optimizations had the biggest impact.
Connector Orchestration: 3 Connectors, one intelligent agent
Connect Datadog, Cloudflare and Google Sheets Connectors so your AI agent reads API request volumes and latency from Datadog, calculates bandwidth savings from Cloudflare cache, compares edge vs. origin load, and generates a weekly infrastructure cost optimization report in Google Sheets. Platform teams running high-traffic APIs behind Cloudflare get data-driven cost optimization recommendations. No guessing which endpoints cost the most. No manual bandwidth calculations. One prompt and your API costs are visible.
Datadog
triggerReads API request volumes, latency and error rates per endpoint
query_metrics search_logs list_monitors list_hosts Cloudflare
actionReads cache analytics, bandwidth savings and edge performance
get_zone_analytics list_zones get_worker_analytics list_workers Google Sheets
actionWrites weekly cost reports and tracks optimization trends
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 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.
- Datadog, Cloudflare & 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.
Platform teams running high-traffic APIs behind Cloudflare who need per-endpoint cost attribution and cache optimization data
Engineering managers presenting infrastructure cost reduction plans who need data-driven savings projections
FinOps teams tracking API infrastructure costs who need automated weekly reports without building custom dashboards
Startups approaching scale milestones who need to understand which endpoints will drive cost growth as traffic increases
Frequently Asked Questions About This Connector Orchestration
Which Connectors do I need for this workflow?
Three: Datadog, Cloudflare and Google Sheets. Connect all three to your AI client.
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.
How does the agent calculate costs?
The agent uses standard cloud bandwidth pricing ($0.09/GB) as a default. Customize in your prompt with your actual CDN and origin costs.
Can I use AWS CloudFront instead of Cloudflare?
This recipe uses the Cloudflare MCP. For CloudFront, you would need an AWS-specific Connector for cache analytics.
Does it account for compute costs, not just bandwidth?
The base workflow tracks bandwidth. Add compute cost estimates by specifying your origin server cost per request in the prompt.
How often should I run this?
Weekly for cost tracking, monthly for optimization recommendations. Traffic patterns need at least a week of data for meaningful analysis.
Get Instant Incident Alerts in Discord via MCP
Monitors fire, Discord gets the alert, the incident log updates itself , no human in the loop
MCP Recipe for Full-Stack Observability
Two monitoring tools, zero correlation , your Datadog alerts say 'high latency' and your Grafana dashboards say 'database connections maxed' but nobody connected the dots until the postmortem
MCP Recipe for Pre-Mortem System Analysis
Architecture red-teamed, failure modes quantified, monitoring alerts created , pre-mortem your system before production breaks it
Connectors for Cache Performance Monitoring
Your Redis cache has 47,000 keys but only 3,200 are ever accessed , the rest are ghosts from features you deleted 6 months ago, silently eating memory and money
Connectors for Monitored Deploy Orchestration
PR merged, deployment triggered, health check passed , and the deploy summary posted itself to the PR thread
Monitor Deployment Health Using Connectors
Deployments tracked, latency spikes caught, error rates compared, rollback decisions made , monitor every ship without watching dashboards
Connectors used in this workflow
Datadog
Datadog MCP. It lets you query performance metrics, search application logs, and manage active monitors directly from your AI agent. You can check system health, find specific error traces, and audit your alerting rules without ever leaving your chat or IDE. It turns your observability data into a conversational tool for faster troubleshooting and more efficient incident response across your entire cloud infrastructure.
Cloudflare
Cloudflare MCP gives your AI agents direct access to manage your edge infrastructure. You can deploy Workers, manage KV and D1 databases, handle R2 buckets, and monitor real-time analytics without leaving your chat window. It replaces manual CLI commands and dashboard navigation with natural language instructions for your entire Cloudflare stack.
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.