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@savvysignalsllc/ai-spend

v0.1.1

Published

AI Spend CLI for token pricing, estimates, usage telemetry, and MCP workflows.

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Maintainers

savvysignalssavvysignals

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Readme

AI Spend CLI

AI Spend CLI connects local LLM builds to AI Spend pricing, estimate, snapshot, usage telemetry, and MCP tools.

Install

After the package is published to npm:

npm install -g @savvysignalsllc/ai-spend

You can also run it without a global install:

npx @savvysignalsllc/ai-spend prices

Login

Create a scoped API key in AI Spend, then point the CLI at the hosted API:

aispend login --token aisp_your_key --api-base https://aispendweb-production.up.railway.app/api/v1

Common Commands

aispend prices
aispend compare --input 1000000 --output 250000
aispend init --workspace demo-workspace --project demo-project
aispend estimate SPEC.md BLUEPRINT.md TODOS.md --json
aispend snapshot SPEC.md BLUEPRINT.md TODOS.md --local --json
aispend usage send --provider openai --model gpt-5.4-mini --input 100000 --output 25000 --feature build --environment local
aispend usage rankings --task coding
aispend usage forecast-actual --model gpt-5.4-mini --expected 12.50 --actual 10.80 --task coding
aispend usage recommendation-outcome --current gpt-5.4 --recommended gpt-5.4-mini --outcome accepted --task coding
aispend mcp start

Telemetry Boundary

aispend usage send sends structured usage telemetry: provider, model, token counts, request count, timestamp, environment, feature/team/cost-center labels, latency/retry status when supplied, and allowlisted metadata. Logged-in CLI and MCP commands also send small command-level telemetry events so AI Spend can improve forecast accuracy and model recommendations.

AI Spend does not collect prompts, completions, message bodies, secrets, personal identifiers, session IDs, trace IDs, IP addresses, or raw request/response bodies through the CLI telemetry flow. Set AISPEND_CLI_TELEMETRY=off or AISPEND_MCP_TELEMETRY=off to disable automatic command telemetry.