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| Pricing | Free | Free |
| Capabilities | 8 decomposed | 7 decomposed |
| Times Matched | 0 | 0 |
Automatically captures and aggregates OpenAI API usage events (tokens, model calls, embeddings) in real-time by integrating directly with OpenAI's billing API and usage endpoints, calculating per-request costs based on current pricing tiers without requiring manual instrumentation. The system maintains a live cost ledger that updates as API calls complete, enabling immediate visibility into spending patterns and cost-per-feature attribution.
Unique: Direct integration with OpenAI's billing API endpoints rather than parsing invoice PDFs or relying on SDK instrumentation, enabling real-time cost updates at the moment API calls complete without requiring application-level logging middleware
vs alternatives: Faster cost visibility than waiting for OpenAI's monthly invoices and more accurate than SDK-based sampling, but narrower scope than enterprise APM tools like Datadog or New Relic that support multi-provider LLM tracking
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.
Unique: Automatically extracts latency from OpenAI API response headers without requiring custom middleware or SDK modifications, providing zero-instrumentation performance visibility for existing OpenAI integrations
vs alternatives: Simpler setup than instrumenting application code with timing libraries, but lacks the granularity of tools like LangSmith that instrument at the LLM chain level with token-by-token timing
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.
Unique: Automatically detects usage anomalies by comparing against rolling baselines without requiring manual threshold configuration, using statistical methods to distinguish normal variance from genuine spikes
vs alternatives: More accessible than building custom anomaly detection pipelines, but less sophisticated than ML-based anomaly detection systems that account for seasonality and external factors
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.
Unique: Enables feature-level cost attribution without requiring application-level instrumentation frameworks, using lightweight metadata tagging in API requests to correlate costs with business features
vs alternatives: Simpler than building custom cost allocation logic in application code, but less flexible than comprehensive observability platforms like Datadog that can correlate costs with arbitrary application context
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.
Unique: Provides simple threshold-based alerting without requiring users to set up external monitoring infrastructure, with real-time cost comparison enabling alerts to fire within seconds of threshold breach
vs alternatives: Easier to configure than building custom alerting logic with cloud monitoring services, but less flexible than comprehensive alerting platforms that support complex rule expressions and multi-channel delivery
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.
Unique: Implements zero-knowledge credential storage where API keys are encrypted client-side before transmission, ensuring llm.report never has access to plaintext keys even during transmission or storage
vs alternatives: More secure than services that store plaintext API keys server-side, but less convenient than OAuth-based authentication which OpenAI does not currently support
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.
Unique: Provides pre-built dashboard templates optimized for LLM cost analysis without requiring users to configure custom BI tools, with automatic metric selection based on OpenAI API usage patterns
vs alternatives: Faster to set up than configuring custom dashboards in Tableau or Looker, but less flexible for creating arbitrary custom visualizations or integrating with other data sources
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.
Unique: Removes friction for new users by offering a genuinely useful free tier with no credit card requirement, enabling teams to validate LLM cost tracking value before paying
vs alternatives: More accessible than enterprise APM tools with high minimum pricing, but quota limits may force quick upgrade for teams with growing API usage
Enables evaluation of AI systems' ability to perform chained mathematical operations (addition, subtraction, multiplication, division, comparisons) across both structured tables and unstructured text extracted from SEC filings. The dataset provides ground-truth question-answer pairs where answers require synthesizing data from multiple locations within earnings reports and applying sequential arithmetic operations, testing whether models can decompose complex financial queries into discrete computational steps.
Unique: Combines real SEC filing documents (not synthetic) with crowdsourced questions requiring multi-step arithmetic, creating a hybrid dataset that tests both domain knowledge extraction and quantitative reasoning in a single evaluation task. Unlike generic math word problems, answers require locating figures within 10+ page documents first.
vs alternatives: More challenging than DROP or SVAMP because it requires financial domain knowledge AND document retrieval before arithmetic, whereas generic math benchmarks assume figures are already extracted
Assesses whether AI systems understand financial terminology, accounting concepts, and domain-specific metrics by requiring them to answer questions about real earnings reports from S&P 500 companies. The dataset tests recognition of financial line items (revenue, COGS, operating expenses, net income), ability to distinguish between different financial statements (income statement vs balance sheet), and understanding of financial ratios and metrics without explicit instruction on their definitions.
Unique: Uses authentic SEC filings rather than synthetic financial data, exposing models to real-world accounting variations, footnote complexity, and the actual structure of professional financial documents. This tests transfer learning from general text to specialized domain without domain-specific pretraining.
vs alternatives: More authentic than synthetic financial QA datasets because it uses real earnings reports with their inherent complexity, but narrower than general financial knowledge benchmarks because it focuses only on historical data interpretation
FinQA scores higher at 60/100 vs Llm.report at 40/100.
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Enables evaluation of AI systems' ability to extract numerical data from both structured HTML/text tables and unstructured prose within the same document, then reason over the extracted values. The dataset contains questions where relevant data appears in different formats — some figures are in formatted tables with clear row/column headers, while others are embedded in narrative text or footnotes — requiring robust parsing and entity linking before computation can occur.
Unique: Combines structured table data with unstructured narrative in the same evaluation, forcing systems to handle format heterogeneity and resolve references across different data representations. Most table QA datasets use clean, isolated tables; this tests real-world document complexity.
vs alternatives: More realistic than isolated table QA benchmarks (like SQA or WikiTableQuestions) because it requires handling narrative context and format mixing, but simpler than full document understanding because tables are already in text format (no OCR needed)
Provides a curated, crowdsourced-annotated dataset of 8,281 question-answer pairs with multi-step reasoning requirements, enabling systematic evaluation of AI systems on financial numerical reasoning. The dataset includes quality control mechanisms through crowdworker annotation, answer validation against ground truth, and coverage across diverse financial metrics and company types within the S&P 500, creating a reproducible evaluation standard for the financial AI community.
Unique: Provides a publicly available, reproducible benchmark specifically designed for financial numerical reasoning with real SEC filings, enabling standardized comparison across different financial AI systems. Most financial datasets are proprietary or synthetic; this is open-source and authentic.
vs alternatives: More specialized and challenging than generic QA benchmarks (SQuAD, MRQA) because it requires financial domain knowledge and multi-step arithmetic, but narrower in scope than comprehensive financial understanding benchmarks because it focuses only on numerical reasoning
Assesses AI systems' ability to perform multi-hop reasoning by requiring them to locate and combine information from different sections of earnings reports. Questions may require finding a figure in the income statement, then locating a related metric in the balance sheet, then performing arithmetic across both — testing whether models can maintain context across document boundaries and understand relationships between different financial statement sections.
Unique: Embeds multi-hop reasoning requirements within authentic financial documents where hops correspond to real relationships between financial statement sections, rather than synthetic reasoning chains. This tests whether models understand domain structure, not just generic multi-hop patterns.
vs alternatives: More realistic than synthetic multi-hop datasets (HotpotQA, 2WikiMultiHopQA) because reasoning hops follow actual financial relationships, but less controlled because document structure varies and reasoning paths are implicit rather than explicitly annotated
Enables evaluation of whether AI systems can identify which arithmetic operations (addition, subtraction, multiplication, division, comparison) are required to answer financial questions, then execute them correctly. The dataset implicitly tests operation selection — a question asking 'what is the profit margin' requires division (net income / revenue), while 'what is total assets' requires addition — forcing models to understand financial semantics before applying math.
Unique: Embeds arithmetic operation selection within financial domain context, requiring models to understand that 'margin' semantically maps to division and 'total' maps to addition. This tests semantic grounding of operations, not just arithmetic execution.
vs alternatives: More semantically grounded than generic math word problem datasets because operation selection is implicit in financial terminology, but less explicit than datasets with annotated operation types because operations must be inferred
Provides evaluation capability for AI systems to compare financial metrics across multiple S&P 500 companies or aggregate metrics across different time periods within the same company's earnings reports. While individual questions reference single documents, the dataset structure enables evaluation of systems that can retrieve and compare relevant companies, requiring understanding of which metrics are comparable across entities and how to normalize for company size or accounting differences.
Unique: Provides a foundation for evaluating cross-company financial comparison by including diverse S&P 500 companies with different business models and scales, enabling assessment of whether systems can normalize and compare metrics appropriately. Most financial QA datasets focus on single-document questions.
vs alternatives: Enables cross-company evaluation unlike single-document QA datasets, but requires external retrieval and comparison logic because the dataset itself contains only single-document questions