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The system maintains a live cost ledger that updates as API calls complete, enabling immediate visibility into spending patterns and cost-per-feature attribution.","intents":["I need to see exactly how much my OpenAI API usage is costing me right now, not wait for monthly bills","I want to understand which features or endpoints are driving the highest costs so I can optimize them","I need to set up cost alerts before my OpenAI bill surprises me with unexpected charges","I want to calculate the true ROI of each AI-powered feature in my product"],"best_for":["Startups and small teams using OpenAI APIs who need cost visibility without enterprise APM overhead","Product managers trying to understand AI feature economics and profitability per user","Solo developers building LLM-powered applications who want to avoid surprise bills"],"limitations":["Limited to OpenAI ecosystem only — no support for Anthropic, Google Cloud Vertex AI, or other LLM providers","Requires valid OpenAI API key with billing access — cannot track usage from third-party integrations or cached requests","Real-time updates depend on OpenAI API availability and latency — may lag 30-60 seconds during high-volume periods","No token-level granularity — aggregates at request level, cannot break down costs by specific prompt vs completion tokens"],"requires":["Active OpenAI API account with billing enabled","Valid OpenAI API key with usage tracking permissions","Web browser with JavaScript enabled (webapp-based)","Network access to OpenAI's usage reporting endpoints"],"input_types":["OpenAI API key (credential)","API usage events (from OpenAI backend)"],"output_types":["structured cost data (JSON/CSV)","time-series cost metrics","aggregated usage statistics"],"categories":["data-processing-analysis","monitoring-observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_llm-report__cap_1","uri":"capability://data.processing.analysis.per.request.latency.and.performance.metrics.collection","name":"per-request latency and performance metrics collection","description":"Captures and visualizes API request latency, token throughput, and model response times by hooking into OpenAI API response metadata (time_created, finish_reason, usage fields). Aggregates latency data into percentile distributions and time-series graphs to identify performance bottlenecks and model-specific response time patterns without requiring application-level instrumentation.","intents":["I want to see if my OpenAI API calls are getting slower over time or if there are specific models that are consistently slow","I need to understand the p95 and p99 latency of my LLM calls to set realistic SLAs for my users","I want to correlate high latency with high costs to understand if I'm paying for slow responses"],"best_for":["Teams optimizing LLM response times for user-facing applications","Developers debugging performance regressions in AI features","Product teams balancing cost vs speed tradeoffs across different OpenAI models"],"limitations":["Latency metrics are end-to-end only — cannot break down network latency vs model inference time vs token generation time","No correlation with application-level metrics (user request latency, database queries) — isolated to OpenAI API layer","Shallow analytics compared to enterprise APM — no flame graphs, no distributed tracing across multiple services","Cannot track queued requests or rate-limit wait times — only measures actual API response latency"],"requires":["Active OpenAI API account","Valid OpenAI API key","Minimum 10-20 API requests to generate meaningful latency distributions"],"input_types":["OpenAI API response metadata"],"output_types":["latency percentiles (p50, p95, p99)","time-series latency graphs","model-specific performance comparisons"],"categories":["data-processing-analysis","monitoring-observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_llm-report__cap_2","uri":"capability://data.processing.analysis.usage.pattern.analysis.and.trend.detection","name":"usage pattern analysis and trend detection","description":"Analyzes historical API usage data to identify trends, peak usage times, and model adoption patterns through time-series aggregation and statistical comparison. Detects anomalies in usage volume or cost spikes by comparing current usage against rolling baselines, enabling teams to spot unexpected behavior or identify optimization opportunities.","intents":["I want to see which OpenAI models my team is actually using and if we're shifting away from expensive models","I need to understand if my API usage is growing linearly or exponentially to forecast future costs","I want to detect if a bug or runaway loop is causing unexpected API calls"],"best_for":["Engineering leads forecasting LLM infrastructure costs for budget planning","DevOps teams monitoring for cost anomalies and runaway API usage","Product teams understanding feature adoption and model preference shifts"],"limitations":["Trend detection requires minimum 7-14 days of historical data — cannot detect anomalies in new accounts","No causal analysis — cannot explain WHY usage changed, only that it did","Baseline calculation is simple rolling average — does not account for seasonality or planned usage changes","Limited to OpenAI usage patterns — cannot correlate with application metrics or business events"],"requires":["Active OpenAI API account with 7+ days of usage history","Valid OpenAI API key"],"input_types":["historical OpenAI API usage data"],"output_types":["trend graphs","usage forecasts","anomaly alerts","model adoption statistics"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_llm-report__cap_3","uri":"capability://data.processing.analysis.cost.attribution.by.application.feature.or.endpoint","name":"cost attribution by application feature or endpoint","description":"Maps OpenAI API calls to specific application features or endpoints by correlating API request metadata with application context passed through custom headers or request parameters. Aggregates costs at the feature level to enable ROI calculation and cost optimization decisions per feature without requiring application code changes.","intents":["I want to know how much my 'AI chat assistant' feature costs vs my 'content generation' feature","I need to calculate the unit economics of each AI-powered feature to decide which ones are profitable","I want to identify which features are driving the highest costs so I can optimize or deprecate them"],"best_for":["Product managers calculating feature-level profitability and ROI","Engineering teams optimizing cost-per-feature and deciding on feature deprecation","Startups understanding which AI features are worth investing in"],"limitations":["Requires application to pass feature context in API requests — cannot retroactively attribute costs without code changes","Attribution is coarse-grained (feature-level) — cannot drill down to individual user or request-level costs","No automatic feature detection — requires manual configuration of feature-to-endpoint mappings","Cannot track costs across multiple API calls that comprise a single feature (e.g., multi-step chains)"],"requires":["Application code modified to pass feature context in OpenAI API request headers or metadata","Valid OpenAI API key","Feature mapping configuration in llm.report dashboard"],"input_types":["OpenAI API calls with feature context metadata","feature-to-endpoint mapping configuration"],"output_types":["cost breakdown by feature","feature-level ROI metrics","cost-per-feature trends"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_llm-report__cap_4","uri":"capability://automation.workflow.cost.alert.and.threshold.configuration","name":"cost alert and threshold configuration","description":"Allows users to define custom cost thresholds and alert rules (daily spend limit, weekly budget, cost-per-feature ceiling) that trigger notifications when spending exceeds configured limits. Implements threshold monitoring by continuously comparing real-time cost aggregates against user-defined rules and dispatching alerts via email or webhook integrations.","intents":["I want to get an email alert if my daily OpenAI spending exceeds $100","I need to set a hard budget cap to prevent runaway costs from bugs or attacks","I want to be notified if a specific feature's costs spike unexpectedly"],"best_for":["Startups and small teams without dedicated DevOps infrastructure for cost monitoring","Solo developers who need simple cost guardrails without complex alerting systems","Teams with variable or unpredictable LLM usage patterns"],"limitations":["Alerts are notifications only — do not automatically throttle or disable API calls (no hard cost enforcement)","Threshold logic is simple rule-based — cannot express complex conditions (e.g., 'alert if cost increases 20% week-over-week')","Alert delivery depends on email/webhook reliability — may miss alerts during outages","No alert deduplication or rate limiting — may spam users with repeated alerts during sustained high usage"],"requires":["Valid OpenAI API key","Email address or webhook URL for alert delivery","Access to llm.report dashboard to configure thresholds"],"input_types":["threshold configuration (numeric limits, conditions)","alert destination (email, webhook URL)"],"output_types":["alert notifications (email, webhook POST)","alert history and logs"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_llm-report__cap_5","uri":"capability://safety.moderation.openai.api.key.management.and.secure.credential.storage","name":"openai api key management and secure credential storage","description":"Securely stores OpenAI API keys in encrypted form and manages credential lifecycle (rotation, revocation, expiration) through a credential vault. Implements zero-knowledge architecture where keys are encrypted client-side before transmission and stored in encrypted form server-side, preventing llm.report from ever accessing plaintext keys.","intents":["I want to connect my OpenAI account to llm.report without exposing my API key to the service","I need to rotate my OpenAI API key without losing historical cost data","I want to revoke access to llm.report without affecting my other OpenAI integrations"],"best_for":["Security-conscious teams that require zero-knowledge credential handling","Organizations with strict API key rotation policies","Teams using multiple OpenAI accounts and needing per-account credential isolation"],"limitations":["Zero-knowledge architecture means llm.report cannot recover lost API keys — users must manage key rotation independently","Credential rotation requires manual re-entry of new keys — no automated key refresh mechanism","Cannot support OAuth-based authentication with OpenAI (OpenAI does not provide OAuth) — requires direct API key access","Key expiration is not enforced by llm.report — relies on OpenAI's key management policies"],"requires":["Valid OpenAI API key with billing access","HTTPS connection to llm.report (no HTTP support)","Browser support for client-side encryption (modern browsers only)"],"input_types":["OpenAI API key (plaintext, entered by user)"],"output_types":["encrypted credential storage confirmation","credential metadata (key ID, creation date, last used)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_llm-report__cap_6","uri":"capability://data.processing.analysis.dashboard.visualization.and.cost.reporting","name":"dashboard visualization and cost reporting","description":"Renders interactive dashboards displaying cost trends, usage patterns, and performance metrics through web-based charting libraries (likely Chart.js or similar). Provides multiple visualization types (line charts for trends, bar charts for model comparison, pie charts for cost breakdown) and allows users to customize time ranges, filters, and metrics displayed.","intents":["I want to see a visual breakdown of my OpenAI costs by model type","I need to generate a cost report for my manager showing spending trends over the last month","I want to compare my API costs across different time periods to understand growth"],"best_for":["Non-technical stakeholders (managers, finance) who need visual cost reports","Teams presenting LLM cost analysis to executives or investors","Developers who prefer visual dashboards over raw data exports"],"limitations":["Visualizations are static snapshots — do not update in real-time as new API calls complete","Limited customization — cannot create arbitrary custom dashboards or drill-down views","Export options likely limited to PDF or CSV — no integration with BI tools like Tableau or Looker","Dashboard performance may degrade with large datasets (months of historical data)"],"requires":["Web browser with JavaScript and Canvas support","Valid OpenAI API key connected to llm.report","Minimum 1-2 days of usage history for meaningful visualizations"],"input_types":["cost and usage data from OpenAI API"],"output_types":["interactive web dashboards","PDF reports","CSV exports"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_llm-report__cap_7","uri":"capability://automation.workflow.free.tier.usage.and.quota.management","name":"free tier usage and quota management","description":"Provides a free tier with limited analytics features and usage quotas (e.g., 100 API calls tracked per month, 30-day data retention) to enable startups and small teams to evaluate LLM cost tracking without upfront payment. Implements quota enforcement by tracking API call counts and data retention windows, with clear upgrade paths to paid tiers for higher limits.","intents":["I want to try llm.report without paying to see if it's useful for my team","I need basic cost tracking for my small startup without enterprise pricing","I want to understand my OpenAI spending before committing to a paid analytics tool"],"best_for":["Startups and small teams with limited budgets for analytics infrastructure","Solo developers evaluating LLM cost tracking tools","Teams with low OpenAI API usage (< 1000 calls/month)"],"limitations":["Free tier has limited data retention (30 days) — cannot perform long-term trend analysis","Quota limits (100 tracked calls/month) may be insufficient for teams with moderate API usage","Feature limitations on free tier — may lack advanced analytics or custom alerts","Upgrade path may be expensive relative to free tier value — creates friction for scaling"],"requires":["Valid OpenAI API key","Email address for account creation","No credit card required for free tier"],"input_types":["OpenAI API key"],"output_types":["basic cost and usage metrics","limited historical data"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Active OpenAI API account with billing enabled","Valid OpenAI API key with usage tracking permissions","Web browser with JavaScript enabled (webapp-based)","Network access to OpenAI's usage reporting endpoints","Active OpenAI API account","Valid OpenAI API key","Minimum 10-20 API requests to generate meaningful latency distributions","Active OpenAI API account with 7+ days of usage history","Application code modified to pass feature context in OpenAI API request headers or metadata","Feature mapping configuration in llm.report dashboard"],"failure_modes":["Limited to OpenAI ecosystem only — no support for Anthropic, Google Cloud Vertex AI, or other LLM providers","Requires valid OpenAI API key with billing access — cannot track usage from third-party integrations or cached requests","Real-time updates depend on OpenAI API availability and latency — may lag 30-60 seconds during high-volume periods","No token-level granularity — aggregates at request level, cannot break down costs by specific prompt vs completion tokens","Latency metrics are end-to-end only — cannot break down network latency vs model inference time vs token generation time","No correlation with application-level metrics (user request latency, database queries) — isolated to OpenAI API layer","Shallow analytics compared to enterprise APM — no flame graphs, no distributed tracing across multiple services","Cannot track queued requests or rate-limit wait times — only measures actual API response latency","Trend detection requires minimum 7-14 days of historical data — cannot detect anomalies in new accounts","No causal analysis — cannot explain WHY usage changed, only that it did","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:31.447Z","last_scraped_at":"2026-04-05T13:23:42.560Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=llm-report","compare_url":"https://unfragile.ai/compare?artifact=llm-report"}},"signature":"6T2PszZcoyoEokC7MJP1GSN9dpu/U/LxZNjDSVKbFqxIc9tmYLolTqh8XmF7WnznAoeWrxqbFVDeC6zvRwVhBg==","signedAt":"2026-06-21T23:46:04.815Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llm-report","artifact":"https://unfragile.ai/llm-report","verify":"https://unfragile.ai/api/v1/verify?slug=llm-report","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"}}