{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-ai-cost-meter","slug":"ai-cost-meter","name":"ai-cost-meter","type":"mcp","url":"https://www.npmjs.com/package/ai-cost-meter","page_url":"https://unfragile.ai/ai-cost-meter","categories":["mcp-servers"],"tags":["llm","ai","openai","anthropic","gemini","mistral","groq","deepseek","cost","token","tracker","billing","usage","pricing","middleware"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-ai-cost-meter__cap_0","uri":"capability://data.processing.analysis.multi.provider.llm.token.counting.with.standardized.interface","name":"multi-provider llm token counting with standardized interface","description":"Provides a unified API for counting tokens across 6+ LLM providers (OpenAI, Anthropic, Gemini, Mistral, Groq, DeepSeek) by wrapping each provider's native tokenization logic or implementing compatible algorithms. Uses provider-specific token encoders (e.g., tiktoken for OpenAI, claude-tokenizer for Anthropic) behind a normalized interface, allowing developers to swap providers without changing token-counting code. Handles model-specific tokenization differences (e.g., different BPE vocabularies, special token handling) transparently.","intents":["I need to count tokens for different LLM providers without learning each provider's API","I want to compare token usage across providers before committing to one","I need accurate token counts before sending requests to avoid exceeding context limits"],"best_for":["multi-provider LLM applications requiring provider-agnostic token accounting","teams evaluating cost-efficiency across OpenAI, Anthropic, and other providers","developers building LLM middleware or orchestration layers"],"limitations":["Token counts may drift slightly from actual API usage if provider tokenizers are updated without library updates","No support for custom fine-tuned models with non-standard tokenization","Tokenization accuracy depends on provider-specific encoder availability; some providers may require fallback estimation"],"requires":["Node.js 14+ or browser environment with ES6 support","No external API keys required for token counting (uses local tokenizers)"],"input_types":["text/string","structured messages (role/content pairs for chat models)"],"output_types":["integer (token count)","structured token breakdown (prompt tokens, completion tokens)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-ai-cost-meter__cap_1","uri":"capability://data.processing.analysis.real.time.llm.api.cost.calculation.with.per.request.granularity","name":"real-time llm api cost calculation with per-request granularity","description":"Automatically calculates monetary cost for each LLM API request by multiplying token counts (input + output) by provider-specific pricing rates. Maintains an internal pricing table for each provider and model, updated to reflect current pricing. Supports both streaming and non-streaming requests, calculating costs incrementally as tokens arrive. Returns cost breakdowns (prompt cost, completion cost, total) alongside token counts, enabling per-request cost visibility without manual billing API queries.","intents":["I need to know the exact cost of each LLM API call in real-time, not just at billing time","I want to track which models or features are most expensive in my application","I need to implement cost-aware routing (e.g., use cheaper models when possible)"],"best_for":["cost-conscious teams running high-volume LLM applications","developers building cost-optimization features (e.g., model selection based on budget)","startups needing granular cost tracking for unit economics"],"limitations":["Pricing table must be manually updated when providers change rates; no automatic price sync","Does not account for volume discounts, enterprise pricing, or custom contracts","Calculated costs are estimates; actual billed amounts may differ due to rounding, taxes, or provider-specific billing rules","No support for batch processing pricing (if providers offer discounts for batch APIs)"],"requires":["Node.js 14+ or browser environment","Knowledge of current pricing rates for target providers (library provides defaults)"],"input_types":["token counts (integer)","model identifier (string)","provider name (string)"],"output_types":["cost object with prompt_cost, completion_cost, total_cost (numbers in USD or other currency)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-ai-cost-meter__cap_2","uri":"capability://data.processing.analysis.streaming.response.cost.tracking.with.incremental.token.accounting","name":"streaming response cost tracking with incremental token accounting","description":"Tracks token usage and cost for streaming LLM responses by intercepting and counting tokens as they arrive in chunks, rather than waiting for the complete response. Maintains running totals of prompt tokens, completion tokens, and cost as the stream progresses. Works by wrapping streaming response handlers or middleware to parse token counts from provider-specific stream metadata (e.g., OpenAI's usage field in stream deltas). Enables cost visibility before streaming completes, supporting early termination or cost-aware stream handling.","intents":["I need to track costs for streaming responses in real-time, not after the stream ends","I want to stop a stream early if costs exceed a threshold","I need accurate token counts for partial/incomplete streaming responses"],"best_for":["applications with long-running or expensive streaming operations","cost-sensitive systems that need to implement spending limits mid-stream","real-time dashboards showing live cost accumulation during LLM operations"],"limitations":["Some providers (e.g., Anthropic) do not include token counts in stream deltas; final counts only available at stream end","Incremental cost tracking may be less accurate than final counts due to token boundary effects in streaming","Requires provider-specific stream parsing logic; not all streaming formats are supported equally"],"requires":["Node.js 14+ or browser with streaming API support","Provider that supports streaming responses (OpenAI, Anthropic, Gemini, etc.)"],"input_types":["streaming response object or event stream","provider identifier (string)"],"output_types":["incremental cost updates (number)","running token count (integer)","final cost and token summary (object)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-ai-cost-meter__cap_3","uri":"capability://tool.use.integration.provider.agnostic.middleware.integration.for.automatic.cost.tracking","name":"provider-agnostic middleware integration for automatic cost tracking","description":"Integrates with LLM client libraries (OpenAI SDK, Anthropic SDK, etc.) via middleware or wrapper patterns to automatically inject cost tracking into every API call without modifying application code. Intercepts requests before they're sent and responses after they're received, extracting token counts and calculating costs transparently. Supports both callback-based and promise-based middleware patterns, and works with async/await code. Accumulates costs across multiple requests, enabling application-level cost aggregation and reporting.","intents":["I want to track costs across my entire application without wrapping every LLM call manually","I need to add cost tracking to existing code with minimal refactoring","I want to aggregate costs across multiple requests and report totals"],"best_for":["teams adding cost tracking to existing LLM applications","developers building LLM frameworks or libraries that need built-in cost visibility","applications requiring application-level cost aggregation and reporting"],"limitations":["Middleware integration is provider-specific; requires separate middleware for each SDK","Does not intercept costs from non-SDK API calls (e.g., direct HTTP requests)","Middleware overhead adds latency (~5-10ms per request depending on implementation)","No built-in persistence; costs are tracked in-memory and lost on process restart"],"requires":["Node.js 14+","Compatible LLM client SDK (OpenAI, Anthropic, Gemini, etc.)","Application using async/await or promise-based patterns"],"input_types":["LLM client instance or configuration object"],"output_types":["cost tracking object with per-request and aggregate costs","structured cost report (total, by model, by provider)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-ai-cost-meter__cap_4","uri":"capability://data.processing.analysis.cost.aggregation.and.reporting.with.time.series.and.categorical.breakdowns","name":"cost aggregation and reporting with time-series and categorical breakdowns","description":"Aggregates costs across multiple requests and provides structured reports broken down by time period, model, provider, or custom categories. Maintains running totals and supports queries like 'total cost in last hour', 'cost by model', 'cost by provider'. Implements in-memory cost accumulation with optional export to JSON or CSV for external analysis. Supports custom tagging of requests (e.g., by user, feature, or endpoint) to enable cost attribution and chargeback scenarios.","intents":["I need to see total costs broken down by model or provider","I want to track costs per user or per feature for chargeback or cost allocation","I need to export cost data for billing, analytics, or financial reporting"],"best_for":["multi-tenant SaaS applications needing per-user cost tracking","teams analyzing cost efficiency across models or providers","applications requiring cost attribution for internal chargeback"],"limitations":["In-memory aggregation only; no built-in database persistence","No time-series database integration; custom implementation required for long-term historical analysis","Aggregation queries are synchronous and may be slow with large numbers of requests","No built-in support for cost forecasting or budget alerts"],"requires":["Node.js 14+","Cost tracking data from prior capabilities (token counts, per-request costs)"],"input_types":["cost tracking records (objects with timestamp, model, provider, cost, optional tags)","query parameters (time range, grouping dimension, filters)"],"output_types":["aggregated cost report (object with totals and breakdowns)","JSON or CSV export format"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-ai-cost-meter__cap_5","uri":"capability://data.processing.analysis.model.pricing.configuration.management.with.version.control","name":"model pricing configuration management with version control","description":"Manages a versioned pricing table for all supported models across all providers, allowing developers to update rates as providers change pricing. Supports both built-in default pricing (updated with library releases) and custom pricing overrides for specific models or providers. Implements a configuration API to set custom rates programmatically, and supports loading pricing from external sources (JSON files, environment variables, or APIs). Tracks pricing version to enable cost recalculation with historical rates if needed.","intents":["I need to update pricing rates when providers change their pricing","I want to use custom pricing rates for my organization (e.g., negotiated enterprise rates)","I need to track which pricing rates were used for historical cost calculations"],"best_for":["teams with custom or negotiated pricing agreements","applications requiring frequent pricing updates","organizations needing audit trails of pricing changes"],"limitations":["No automatic price sync from provider APIs; manual updates required","Pricing version tracking is basic; no full audit log of price changes","Custom pricing overrides are not persisted across process restarts without external storage","No support for time-based pricing changes (e.g., promotional rates)"],"requires":["Node.js 14+","Access to current pricing information from providers"],"input_types":["pricing configuration object (model -> rate mapping)","JSON file or environment variables with pricing data"],"output_types":["pricing configuration object","pricing version identifier (string or number)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-ai-cost-meter__cap_6","uri":"capability://automation.workflow.budget.enforcement.and.spending.limit.alerts","name":"budget enforcement and spending limit alerts","description":"Implements budget tracking and enforcement by monitoring cumulative costs against user-defined spending limits. Supports per-request budget checks (reject requests that would exceed budget), per-session limits, and per-time-period limits (e.g., daily, monthly). Provides callback hooks or event emitters to trigger alerts when costs approach or exceed thresholds. Integrates with cost tracking to enable real-time budget enforcement without external services.","intents":["I need to prevent my application from exceeding a monthly or daily spending limit","I want to alert users when they're approaching their cost budget","I need to implement cost-aware request routing (e.g., reject expensive requests when budget is low)"],"best_for":["cost-sensitive applications with strict budgets","multi-tenant systems implementing per-user spending limits","development environments needing cost caps to prevent runaway expenses"],"limitations":["Budget enforcement is in-memory only; no persistence across process restarts","No built-in integration with payment systems or billing platforms","Budget checks add latency (~1-5ms per request)","No support for complex budget rules (e.g., different limits for different user tiers)"],"requires":["Node.js 14+","Cost tracking data from prior capabilities"],"input_types":["budget limit (number in USD or other currency)","time period (string: 'request', 'session', 'daily', 'monthly')","optional alert threshold (percentage of budget)"],"output_types":["boolean (budget exceeded or not)","alert event (object with current cost, limit, remaining budget)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-ai-cost-meter__cap_7","uri":"capability://data.processing.analysis.cost.comparison.and.model.recommendation.based.on.efficiency.metrics","name":"cost comparison and model recommendation based on efficiency metrics","description":"Analyzes historical cost and token usage data to recommend the most cost-efficient models for specific use cases. Calculates efficiency metrics (cost per token, cost per request, tokens per dollar) for each model and provides rankings. Supports filtering by quality constraints (e.g., 'recommend models with >90% quality score') or latency constraints. Enables A/B testing by comparing costs across models for the same prompts or use cases.","intents":["I want to find the cheapest model that meets my quality requirements","I need to compare costs across models for the same task","I want to optimize my model selection based on historical cost data"],"best_for":["teams evaluating multiple models for cost optimization","applications implementing dynamic model selection","cost-conscious organizations needing data-driven model choices"],"limitations":["Recommendations are based on historical data only; no predictive modeling","Requires sufficient historical data for accurate recommendations (small sample sizes may be unreliable)","Does not account for quality differences between models (requires external quality metrics)","No support for multi-objective optimization (e.g., balancing cost, latency, and quality)"],"requires":["Node.js 14+","Historical cost and token usage data from multiple models","Optional: external quality metrics for filtering"],"input_types":["cost and token usage records (array of objects)","optional quality metrics (object with model -> score mapping)","optional constraints (latency, quality thresholds)"],"output_types":["ranked model recommendations (array with efficiency scores)","cost comparison report (object with per-model metrics)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":54,"verified":false,"data_access_risk":"high","permissions":["Node.js 14+ or browser environment with ES6 support","No external API keys required for token counting (uses local tokenizers)","Node.js 14+ or browser environment","Knowledge of current pricing rates for target providers (library provides defaults)","Node.js 14+ or browser with streaming API support","Provider that supports streaming responses (OpenAI, Anthropic, Gemini, etc.)","Node.js 14+","Compatible LLM client SDK (OpenAI, Anthropic, Gemini, etc.)","Application using async/await or promise-based patterns","Cost tracking data from prior capabilities (token counts, per-request costs)"],"failure_modes":["Token counts may drift slightly from actual API usage if provider tokenizers are updated without library updates","No support for custom fine-tuned models with non-standard tokenization","Tokenization accuracy depends on provider-specific encoder availability; some providers may require fallback estimation","Pricing table must be manually updated when providers change rates; no automatic price sync","Does not account for volume discounts, enterprise pricing, or custom contracts","Calculated costs are estimates; actual billed amounts may differ due to rounding, taxes, or provider-specific billing rules","No support for batch processing pricing (if providers offer discounts for batch APIs)","Some providers (e.g., Anthropic) do not include token counts in stream deltas; final counts only available at stream end","Incremental cost tracking may be less accurate than final counts due to token boundary effects in streaming","Requires provider-specific stream parsing logic; not all streaming formats are supported equally","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8654334378740609,"quality":0.41,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.328Z","last_scraped_at":"2026-05-03T14:04:47.473Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":1062031,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=ai-cost-meter","compare_url":"https://unfragile.ai/compare?artifact=ai-cost-meter"}},"signature":"bZtlmKClyI4Xh/yfHXT4oKgljtpdvwqINIy/WGrzMLzDNC0871/EvGtr9MSSmiWruBDfPylAgsBHWKzCoFycAQ==","signedAt":"2026-06-21T03:10:06.850Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ai-cost-meter","artifact":"https://unfragile.ai/ai-cost-meter","verify":"https://unfragile.ai/api/v1/verify?slug=ai-cost-meter","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}