Cloudprice

cloudprice-mcp

Ask your AI assistant what the cloud will cost — and get real list prices back.

cloudprice-mcp gives Claude, Cursor, GitHub Copilot and any other MCP client pricing and FinOps tools across AWS, Azure, GCP and OCI, plus LLM token prices for ~2,300 model/provider combinations. No cloud account, no API keys: it works from a catalogue refreshed every week from the providers' public pricing APIs.

• Four clouds in one answer, including OCI and its 10 TB free egress — which most tools leave out.
• Cloud and AI costs side by side: price a VM, a GPU and the LLM calls running on it in the same conversation.
• Price history: every weekly catalogue is archived, so you can ask what changed, not just what it costs today.
• Decisions, not just lookups: RI / Savings Plan break-even, 3-year TCO, migration payback, egress arbitrage, carbon (kg CO2e), FOCUS 1.3 export.

Quick start

Add this to your MCP client's config (Claude Desktop, Cursor, VS Code, …). uvx downloads and runs it — nothing to install first:

{

"mcpServers": { "cloudprice": { "command": "uvx", "args": ["cloudprice-mcp"] } } }

Claude Code: claude mcp add cloudprice -- uvx cloudprice-mcp

Or install with pip and let it configure every client it finds (asks before writing each one):

pip install cloudprice-mcp

cloudprice-mcp setup

Restart the client and the 39 tools appear. Problems? cloudprice-mcp doctor says what is wrong; INSTALL.md has per-client steps for Windows, macOS and Linux. Python 3.10+.

What's new in each release: [CHANGELOG.md](CHANGELOG.md).

Things to ask

*"I run 6× t3.2xlarge on AWS. On-demand vs 1-year Savings Plan vs 3-year RI —

what's the break-even month?"* → optimize_commitment

*"At 50 TB/month internet egress, which cloud is cheapest, and what would

moving save over 3 years?"* → compare_egress, find_egress_arbitrage

(OCI ≈ $340/mo vs ≈ $4,000/mo on the hyperscalers)

*"Size this 3-tier app on all four clouds: 8 web (4 vCPU/16 GB), 12 app

(8/32), 4 DB (16/64), 5 TB SSD, 10 TB/month egress, multi-AZ."* →

compare_workload, compare_total_cost_of_ownership

*"Cheapest way to run 50M input / 10M output tokens a month — and is Claude

Sonnet the same price on Bedrock and Vertex?"* → compare_token_pricing

*"Which H100 instance is cheapest per GPU-hour across the four clouds?"* →

compare_gpu_workload

*"We send 2B input / 500M output tokens a month to an API. Would renting

8×H100 and serving Llama ourselves be cheaper, and at what volume?"* →

compare_llm_vs_selfhost

*"My support agent averages 8 steps with a 2k system prompt and 3k of tool

definitions. What does 50,000 runs a month cost on Sonnet vs Haiku, and how

much does prompt caching save — and hosted on AgentCore or Lambda?"* →

estimate_agent_cost (tokens + AgentCore Runtime/Gateway/Memory vs Lambda)

*"We'll run this agent 50,000 times a month, 30 seconds a run, on Claude

Sonnet. AgentCore, Azure Foundry or Vertex Agent Engine, or just Lambda?"* →

compare_agent_platforms (hosting, tools, memory and tokens on each)

*"What changed in LLM pricing this week — anything cheaper on Bedrock or

Groq?"* → llm_price_changes (weekly archive of ~3,600 model/provider prices)

*"Which models on Bedrock and Azure OpenAI retire in the next six months,

and what replaces them?"* → model_retirements (also flagged automatically

by analyze_ai_spend and review_agentcore)

*"How has OCI E5.Flex pricing moved since May?"* → get_price_history

*"Here's our FOCUS export (~/Downloads/focus-2026-10.csv). Where is the money

going, what jumped last month, and how much of our usage do Savings Plans

cover?"* → analyze_bill — reads the file on your machine; nothing is sent

anywhere, and hosted deployments cannot call it.

*"Using the same export: are we under-committed, what spiked last week, and

how much of our spend is missing a team tag?"* → find_commitment_gaps,

detect_bill_anomalies, analyze_cost_allocation

*"What will this month's bill be, and what's our cost per customer? We had

41,000 customers in September and 44,500 in October."* → forecast_spend,

calculate_unit_cost

*"What would the compute on this bill cost on Azure, GCP or OCI?"* →

reprice_bill: the vCPU and memory you actually used, priced on all four

clouds, next to what you paid

*"What are we spending on Bedrock and Azure OpenAI, are we paying list

price, and would prompt caching help?"* → analyze_ai_spend

*"Review our AgentCore deployment: is it secure, what does the agent cost,

and what should we fix first?"* → review_agentcore on

terraform show -json output, plus the bill

Every answer carries the catalogue date and an honest_gaps list of what the tool does not model, so the AI can tell you where the number stops being exact.

Use it on your own bill and deployment

Nine tools read files on your machine: your cloud bill (a FOCUS export) and your AgentCore deployment (Terraform JSON). The file never leaves your machine; only the tool's summary goes back to your AI assistant. Hosted deployments of this server cannot call these tools at all.

1. Download your bill as a FOCUS CSV. Every major cloud exports it:

.csv and .csv.gz up to 500 MB are read.

2. Ask, with the file path in the question:

*"Here's our bill: ~/Downloads/focus-2026-10.csv. Where is the money going,

what would it cost on the other clouds, and what are we spending on AI?"*

3. Review an AgentCore deployment (Terraform):

terraform plan -out=tfplan

terraform show -json tfplan > plan.json

*"Review our AgentCore deployment in ~/plan.json. Our bill is

~/Downloads/focus.csv and the agent is tagged agent=support."*

You get findings ranked by severity: public network, unauthenticated gateways, model permissions, secrets in environment variables, idle-session cost, missing alarms and tags, and with the bill, what the agent costs and what caching would save. Each one names the resource, the Terraform fix and the AWS page behind the rule. To accept a finding on purpose, say so: *"skip S1 on aws_bedrockagentcore_agent_runtime.demo, it is a public demo"*. It is then listed as skipped, with your reason.

plan.json holds every value Terraform knows, secrets included. The review never repeats them, but keep the file out of git and delete it when you are done.

Tools

39 tools, grouped by job. Full descriptions and arguments: [docs/TOOLS.md](docs/TOOLS.md) (generated from the server, so it is always current).

FinOps Framework coverage

How the tools map to the FinOps Foundation capabilities. "Your bill" tools read a FOCUS export locally; the rest work from list prices and the weekly price archive.

Example: compare_workload input

{

"compute": [ { "name": "web", "vcpus": 4, "memory_gb": 16, "quantity": 8, "os_disk_gb": 100, "os_disk_type": "ssd" }, { "name": "app", "vcpus": 8, "memory_gb": 32, "quantity": 12, "os_disk_gb": 200, "os_disk_type": "ssd" }, { "name": "db", "vcpus": 16, "memory_gb": 64, "quantity": 4, "os_disk_gb": 500, "os_disk_type": "ssd" } ], "storage": [ { "name