cloudprice-mcp

The FinOps MCP server. Gives Claude, GitHub Copilot, Cursor, Windsurf, Cline, Continue, Zed — or any MCP-compatible AI — structured pricing data and analysis primitives across AWS, Azure, GCP, and OCI. AI clients use cloudprice-mcp to compute Reserved Instance break-even, multi-cloud workload TCO, exit-cost migration analyses, snapshot cost modeling, and egress arbitrage — the kind of FinOps decisions that normally live in three browser tabs and a half-built spreadsheet.

21 tools covering compute, block storage, object storage, managed Postgres, egress (internet + inter-region with OCI's 10 TB free tier surfaced explicitly), Multi-AZ workloads, snapshots with realistic incremental modeling, Reserved Instance / Savings Plan discounts, FinOps decision suite (migration, commitment, TCO, egress arbitrage), multi-cloud spot pricing with eviction tradeoffs, multi-cloud price history (the only public weekly-refreshed dataset of its kind), a stateless cost drift sentinel for scheduled agents, multi-cloud carbon footprint ($ AND kg CO2e on the same query), multi-cloud GPU pricing (T4 / A10 / L4 / L40S / V100 / A100 / H100 across all 4 clouds), and cross-provider LLM token pricing (Claude / GPT / Gemini / Llama / Mistral / DeepSeek across Anthropic / OpenAI / Bedrock / Vertex / Azure OpenAI). OCI Always Free tier (4 OCPU compute, 20 GB object storage, 10 TB egress) surfaced as $0 line items where it applies.

One-line install configures every AI client you have: pip install cloudprice-mcp && cloudprice-mcp setup — auto-detects Claude Desktop, GitHub Copilot Agent Mode, Cursor, Windsurf, Cline, Continue.dev, and Zed, then asks Y/N before writing each config.

What does FinOps look like with cloudprice-mcp?

Real questions teams actually ask. Paste any of these into Claude / Copilot / Cursor with cloudprice-mcp loaded:

"I have 6× t3.2xlarge running on AWS. Compare the 3-year total cost on-demand vs 1-year Savings Plan vs 3-year RI partial upfront. What's the break-even month?" → AI calls compare_workload, pulls list-price baseline, layers AWS's published RI rates, returns dollar break-even. ~7-month payback typical.

"I'm thinking about offloading 5 TB of cold-tier object storage from AWS S3 to a cheaper provider. Compare archive-tier cost across all 4 clouds, factor in AWS exit egress, and tell me the payback period." → AI calls compare_object_storage + compare_egress, computes one-time exit cost vs ongoing savings. Often surfaces "don't move — AWS Glacier Deep Archive is already tied for cheapest".

"At 50 TB/month internet egress, where am I cheapest? Show the 3-year savings of moving." → compare_egress → OCI ~$340/mo, AWS/Azure/GCP ~$4,000/mo. The 12× difference is OCI's 10 TB free tier — a real moat for content/CDN workloads.

"Size a 3-tier SaaS workload: 8 web (4/16), 12 app (8/32), 4 DB (16/64), 5 TB shared SSD, 50 TB HDD bulk, 10 TB/month egress. Compare full-stack monthly cost across all 4 clouds with multi-AZ and 1-year commitment." → AI chains compare_workload + compare_egress, applies multi-AZ multiplier (×2 compute) + commitment discount.

What you get back: dollar numbers traceable to a public catalog, AI-explained tradeoffs, payback periods, and the kind of "don't do that" recommendation that kills bad migrations before they happen. No console-clicking. No tab-switching between three pricing calculators. No FinOps spreadsheet that goes stale the moment a new SKU drops.

Install

Recommended (auto-config):

pip install cloudprice-mcp
cloudprice-mcp setup     # auto-configures every detected MCP client, asks Y/N before writing

Then fully restart whichever clients were configured. 10 tools appear in each. Done.

Trust spectrum:

If something doesn't work, run:

cloudprice-mcp doctor

It tells you exactly what's broken (Python version, install path, config location, tool registration, command path validity).

Python 3.10+ required.

For step-by-step manual install (Windows / macOS / Linux), see INSTALL.md.

Tools exposed

Single-spec lookups (v0.1)

Bulk + workload compare (v0.2)

Object storage + managed Postgres (v0.3)

FinOps decision suite (v0.6, NEW)

Four named tools that turn cross-cloud pricing into FinOps decisions in one call instead of letting the AI chain three+ tools. All four consume a structured workload inventory (compute / storage / object_storage / databases / egress) plus tool-specific options.

All four tools accept a WorkloadInventory shape that mirrors a 4-section sizing sheet (compute / storage / object_storage / databases / egress) plus optional commitment, multi_az, and one_time.data_to_migrate_gb fields. Output includes honest_gaps — explicit list of what each tool does NOT model — to prevent over-trust.

Egress + Multi-AZ + better snapshots (v0.5, NEW)

Example: compare_workload input shape

{
  "compute": [
    { "name": "web", "tier": "Web", "vcpus": 4, "memory_gb": 16, "quantity": 8,  "os_disk_gb": 100, "os_disk_type": "ssd" },
    { "name": "app", "tier": "App", "vcpus": 8, "memory_gb": 32, "quantity": 12, "os_disk_gb": 200, "os_disk_type": "ssd" },
    { "name": "db",  "tier": "DB",  "vcpus": 16, "memory_gb": 64, "quantity": 4, "os_disk_gb": 500, "os_disk_type": "ssd" }
  ],
  "storage": [
    { "name": "shared-fast", "tier": "DB",  "capacity_gb": 5000,  "disk_type": "ssd" },
    { "name": "shared-bulk", "tier": "App", "capacity_gb": 50000, "disk_type": "hdd" }
  ]
}

Snapshots (v0.2.1)

snapshot_count on storage rows and os_disk_snapshot_count on compute rows are now priced. Snapshot rates per cloud per disk type are bundled (~$0.05/GB-mo for AWS/Azure, ~$0.026/GB-mo for GCP).

Caveat — upper-bound estimate: snapshots are priced as snapshot_per_gb_month × full_capacity × quantity × snapshot_count. Real-world snapshots are incremental (only changed blocks), so actual cost is typically 20-50% of this model's number. If snapshots dominate your total, ask the cloud's calculator for a tighter estimate.

iops and throughput_mbs on storage rows are still accepted as metadata only — not used for SKU matching in this release.

Reserved Instance / Savings Plan estimator (v0.2.1)

compare_workload accepts an optional commitment parameter:

Storage and snapshots are not discounted (most clouds don't offer meaningful storage commitments). Discount tiers are conservative averages — your actual rate depends on instance family, payment option, and region.

Pricing data

Prices are bundled as a curated dataset of common SKUs across 4 clouds:

• Compute (~50 VM SKUs across AWS / Azure / GCP / OCI, including OCI A1 Always Free + A2 Arm Ampere + E5 Flex)

• Block storage (SSD + HDD per cloud)

• Object storage (Hot / Cool / Archive tiers per cloud, including OCI Always Free 20 GB)

• Managed PostgreSQL (RDS / Azure DB / Cloud SQL / OCI Database with PostgreSQL)

Auto-refreshed weekly (v0.7+)

The bundled catalog is refreshed every Sunday by a GitHub Action that hits each cloud's public pricing API:

• AWS — Pricing API (via boto3, OIDC-authenticated)

• Azure — Retail Prices API (public, no auth)

• OCI — Public pricing API (public, no auth)

• GCP — Cloud Billing Catalog API (via API key — GCP_API_KEY env var). Added in v0.8.0

Each refresh writes a dated snapshot to src/cloudprice_mcp/data/prices/YYYY-MM-DD.json — every JSON ever published lives in the repo. The history archive is MIT-licensed and grows with every release.

Every tool result includes the catalog's as_of field so you know exactly which prices were used.

Public price history dataset (v0.7.1+)

cloudprice-mcp is the only FinOps tool we know of that preserves every weekly snapshot. You can query "what did m5.xlarge cost in May?" — neither AWS Calculator nor GCP Estimator can answer that because their pages always show today.

Query the history from the CLI:

cloudprice-mcp history --cloud oci --sku VM.Standard.E5.Flex.4OCPU
# oci/VM.Standard.E5.Flex.4OCPU (us-ashburn-1) — 2 data point(s)
#
# AS_OF          HOURLY USD
# --------------------------
# 2026-04-26   $    0.67600
# 2026-05-12   $    0.18400
#
# Change: -72.78% ($-0.49200/h)

The -72.78% drop is the v0.7.0 auto-refresh fixing a hand-curated inaccuracy in the prior OCI catalog — proof that the auto-refresh story works.

Query the history from AI assistants via two new MCP tools:

• get_price_history(cloud, sku, since?) — full timeseries + change stats

• list_tracked_skus(cloud?, since?) — every (cloud, sku) pair we have history for

Real questions this unlocks:

"Has AWS m5.xlarge changed price in the last quarter?" → AI calls get_price_history, returns timeseries with start/end prices and % change.

"Show me every multi-cloud price mover since January." → AI calls list_tracked_skus(since="2026-01-01"), returns every SKU + its latest price + change.

Cross-provider LLM token pricing (v0.12.0+)

Token costs are the fastest-growing FinOps line item in 2026 — and nobody compares them cross-provider openly. The same model is often available on multiple providers at different prices (Claude on Anthropic / Bedrock / Vertex; GPT on OpenAI / Azure OpenAI; Llama on Bedrock).

from cloudprice_mcp.finops.tokens import compare_token_pricing

# Cheapest model overall for a 50M-in / 10M-out monthly workload
r = compare_token_pricing(
    monthly_input_tokens=50_000_000,
    monthly_output_tokens=10_000_000,
)
# gemini-1.5-flash on google is cheapest at $6.75/mo for 50M in / 10M out tokens.
#   gemini-1.5-flash       on google        $   6.75/mo
#   gemini-1.5-flash       on vertex        $   6.75/mo
#   gemini-2.0-flash       on google        $   9.00/mo
#   llama-3.1-8b           on bedrock       $  13.20/mo
#   gpt-4o-mini            on openai        $  13.50/mo
#   deepseek-v3            on deepseek      $  24.50/mo

# Same model across all hosts — proves Claude 4 Sonnet provider parity
# (and surfaces that only Anthropic API publishes the 90%-off cache_read rate)
r = compare_token_pricing(model_id="claude-4-sonnet")
#   anthropic   in=$3/1M  out=$15/1M  cache_read=$0.30/1M  cache_write=$3.75/1M
#   bedrock     in=$3/1M  out=$15/1M
#   vertex      in=$3/1M  out=$15/1M

Covers 19 models across 8 providers: Claude (4 Opus / 4 Sonnet / 3.5 Haiku / 3 Haiku), GPT (5, 5 mini, 4o, 4o-mini, o1), Gemini (2.0 Flash, 1.5 Pro/Flash), Llama (3.1 8B/70B/405B, 3.3 70B), Mistral Large 2, DeepSeek V3/R1.

Real questions this unlocks:

"Cheapest model that handles 200K context for output-heavy chat at 10M/mo output volume?" → AI calls compare_token_pricing with the volume + an optional model_family filter, returns ranked monthly cost across every viable model+provider combo.

"Should I use Anthropic API or Bedrock for Claude?" → compare_token_pricing(model_id="claude-4-sonnet") shows price parity on per-token rates, but Anthropic API exposes a 10x cheaper cache_read rate that Bedrock doesn't publish. For caching-heavy workloads, Anthropic wins.

Multi-cloud GPU pricing (v0.11.0+)

The fastest-growing cloud cost category — and nobody compares it cross-cloud openly.

from cloudprice_mcp.finops.gpu import compare_gpu_workload
from cloudprice_mcp.pricing import load_catalog

r = compare_gpu_workload(load_catalog(), gpu_type="H100", gpu_count=8)
# OCI BM.GPU.H100.8 is cheapest at $80.0000/h for 8x H100.
#   oci    BM.GPU.H100.8                  $ 80.0000/h  $10.0000/GPU/h
#   gcp    a3-highgpu-8g                  $ 84.4000/h  $10.5500/GPU/h
#   aws    p5.48xlarge                    $ 98.3200/h  $12.2900/GPU/h
#   azure  ND96isr_H100_v5                $ 98.3200/h  $12.2900/GPU/h

Covers NVIDIA T4, A10, A10G, L4, L40S, V100, A100, H100 across all 4 clouds. Returns:

• Absolute hourly winner — the cheapest matching SKU per cloud

• Per-GPU efficiency winner — sometimes a different cloud (e.g., OCI's BM.GPU4.8 is cheapest per GPU but only sold as 8x, so for gpu_count=1 Azure/GCP win the absolute ranking)

• Over-provisioning flags — when the only matching SKU bundles more GPUs than asked for

• GPU memory — differentiates A100 40GB vs 80GB (same gpu_type field)

The OCI H100 finding is real: at 8x H100 it's ~19% cheaper than AWS/Azure for identical hardware.

Cost Drift Sentinel (v0.9.0+)

The shift from query tool to agent capability. Most FinOps tools answer "what does this cost?" — this one answers "is this still what it cost when I signed off on it?"

from cloudprice_mcp.finops.sentinel import watch_workload
from cloudprice_mcp.inventory import parse_dict
from cloudprice_mcp.pricing import load_catalog

# First call — captures a baseline. Persist the returned baseline JSON.
result = watch_workload(load_catalog(), parse_dict(workload_spec))
save(result["baseline"])

# Later — pass the baseline back to detect drift.
report = watch_workload(load_catalog(), parse_dict(workload_spec), baseline=load_baseline())
if report["alert_triggered"]:
    notify_humans(report["headline"])

Stateless by design — no server, no database. The baseline JSON lives wherever you want: a file in your IaC repo, S3, Slack DM, anywhere. Each call is a pure function of (catalog, workload, baseline).

Key properties:

• Workload-hash protected — if you change the workload spec, the hash mismatches and you get a fresh baseline rather than a misleading drift report

• SKU-level attribution — the drift report consults the price-history dataset and surfaces which SKUs moved the most between baseline and now

• Configurable threshold — default 5%; pass alert_threshold_pct=N to tune

Plug-and-play GitHub Action template at examples/cloudprice-watch.yml — drop it in any IaC repo with a workload.json, get auto-opened GitHub issues when costs drift. Baseline is committed to your repo so the history is auditable.

Carbon-aware FinOps (v0.10.0+) — kg CO2e per workload, alongside USD

The only FinOps MCP tool that returns both cost AND carbon footprint on the same query. AWS / Azure / GCP each publish their own customer dashboards (Customer Carbon Footprint Tool, Emissions Impact Dashboard, Carbon Footprint) — but none compare across providers. cloudprice does.

from cloudprice_mcp.finops.carbon import compare_carbon_footprint
from cloudprice_mcp.pricing import load_catalog

result = compare_carbon_footprint(load_catalog(), vcpus=8, memory_gb=32, quantity=6)
# Returns per-cloud SKU + cost + power class (x86/ARM) + monthly kWh +
# grid-based kg CO2e + market-based residual kg CO2e (after renewable matching),
# ranked cheapest-carbon-first.

What's modeled (and disclosed in every response):

• PUE per cloud from each provider's public sustainability report

• Grid carbon intensity per region from public emissions data (EPA eGRID for US, similar baselines elsewhere)

• Renewable matching per provider (AWS/Azure 100%, GCP ~64% CFE, OCI unmatched outside EU)

• ARM vs x86 power class — ARM SKUs (Graviton, Ampere, Axion) modeled at ~30% lower per-vCPU power

• Two carbon numbers per cloud: location-based (grid) AND market-based (residual after renewable matching) — both surfaced so auditors can see both perspectives

What's NOT modeled (always disclosed via honest_gaps[]):

• Embodied carbon (server manufacturing) — operational only

• GPU / network / storage power — compute + memory only

• Time-of-use grid variation — annual averages only

• Real-time 24/7 CFE matching — GCP publishes annual CFE %; cloudprice uses that

Real questions this unlocks:

"What's the lowest-carbon cloud for 4 vCPU / 16 GB at 6 instances?" → AI calls compare_carbon_footprint, returns per-cloud kg CO2e/mo ranked.

"How much carbon do I save running on ARM vs x86?" → AI calls it twice with the same shape but different target SKUs.

What's NOT modeled (real-world TCO killers)

• ✅ Egress / data transfer — modeled in v0.5 (compare_egress)

• ✅ Multi-AZ / HA replicas — modeled in v0.5 (multi_az: true on compare_workload)

• ✅ Snapshots upper-bound only — fixed in v0.5 (snapshot_incremental_factor)

• Reserved/Savings Plan SKU detail (we apply a flat tier discount, not per-region/per-family detail) — roadmap

• Multi-region pricing (currently us-east only; us-west / eu-west planned for v0.5.1) — roadmap

• IOPS-based storage matching (capacity-only) — roadmap

• Backup storage charges (some clouds free, others billed) — roadmap

• Request costs (PUT/GET pricing for object storage) — roadmap

• Retrieval costs for archive tiers (Glacier-style retrieval can be 10× the storage cost) — roadmap

• VPC peering / interconnect costs — roadmap

These are tracked roadmap items. Use cloudprice-mcp for the on-demand list-price baseline; do final TCO analysis with each cloud's own calculator before relying on numbers for big decisions.

Live runtime pricing (not just weekly refresh) is being considered for v0.8 — would fetch prices directly at MCP tool invocation time instead of from the bundled catalog. Trade-offs: slower (network call per tool use), adds GCP auth requirement, breaks offline mode. The v0.7 weekly auto-refresh covers ~95% of the credibility win at zero runtime cost; live mode is opt-in territory.

Develop locally

git clone https://github.com/Albaker-Group/cloudprice-mcp.git
cd cloudprice-mcp
pip install -e ".[dev]"
pytest

To point Claude Desktop at your dev copy, swap the command in the config:

{
  "mcpServers": {
    "cloudprice": {
      "command": "python",
      "args": ["-m", "cloudprice_mcp.server"]
    }
  }
}

License

MIT — see LICENSE.

Credits

Built by Ali Albaker, multi-cloud architect — runs a live three-cloud portfolio at ~$1.80/month across AWS, Azure, and GCP, with OCI joining as the 4th cloud in 2026.