# Narev Docs ## Docs - [AI adoption rate for large firms continues to trend down](https://narev.ai/docs/blog/ai-adoption-rate.md): U.S. Census Bureau data shows AI adoption among large firms has continued to decline after peaking in July 2025, while smallest firms keep growing - [Claude Fable 5 Released: 10% Better, 20x More Expensive](https://narev.ai/docs/blog/claude-fable-released.md): Claude Fable 5 is here. It's 10% better than Composer 2.5, but costs 20x more. We break down the charts and analyze if the new model justifies its price. - [GPT-3.5 MMLU Score: why it beats GPT-5 at 3% of the Cost](https://narev.ai/docs/blog/gpt35-beats-gpt5.md): Despite its low MMLU score, GPT-3.5 outperformed top 2025 models like GPT-5 and Claude Opus on a real task, costing USD 823 vs USD 30,390 per 1M requests. - [Blog - Index](https://narev.ai/docs/blog/index.md): Read the latest Narev research, product updates, and analysis on LLM cost optimization, AI FinOps, and the future of AI unit economics. - [How we cut Dagster Cloud credits by 60% by collapsing dbt assets](https://narev.ai/docs/blog/lower-dagster-bill.md): Learn how we reduced our bill after the May 2026 pricing changes by collapsing our dbt project into a single asset. - [GPT-4o vs Claude Opus - MMLU Scores vs. Actual API Costs](https://narev.ai/docs/blog/mmlu-doesnt-matter.md): MMLU scores for GPT-4o vary by 13 points, while top models differ by 1%. The real difference? Massive cost disparities for the same performance. - [Why Narev AI Exists - Two futures for AI economics](https://narev.ai/docs/blog/why-we-launch.md): There's a world where AI gets infinitely cheaper, and another where users need to choose the best tool for every job. Narev is building for the latter. - [DeepSeek Usage-Based Billing with Vercel AI SDK](https://narev.ai/docs/guides/deepseek-usage-based-billing.md): Track DeepSeek model usage and calculate costs in a Vercel AI SDK app with the @ai-billing/deepseek middleware to power usage-based billing flows. - [Calculate DeepSeek V4 model pricing with the Narev API](https://narev.ai/docs/guides/deepseek-v4-pricing.md): Step-by-step guide to looking up DeepSeek V4 token prices and calculating per-request costs through the Narev Cloud pricing API in Python and TypeScript. - [FinOps for AI Framework](https://narev.ai/docs/guides/finops-for-ai/index.md): A practical three-step FinOps framework to measure, track, and optimize LLM spending in production without compromising quality or user experience. - [Step 1: know your objective and who's in charge](https://narev.ai/docs/guides/finops-for-ai/step-1.md): Step 1 of Narev's FinOps for AI framework: define success metrics, align stakeholders, and pick the decision-maker before optimizing LLM spend. - [Step 2: know what you're spending on](https://narev.ai/docs/guides/finops-for-ai/step-2.md): Step 2 of Narev's FinOps for AI framework: track LLM token costs at the source and break aggregate spend down by app, feature, customer, and team. - [Step 3: optimize LLM spend with benchmarks and phased roll-out](https://narev.ai/docs/guides/finops-for-ai/step-3.md): Step 3 of Narev's FinOps for AI framework: systematically benchmark variants, measure quality, and roll out the winners to cut LLM cost without losing quality. - [How to choose an LLM model for your product](https://narev.ai/docs/guides/how-to-choose-llm-model.md): A practical guide to picking the best LLM for your product when you don't have labelled data, comparing models on cost, quality, and task fit. - [How to choose an LLM (with labelled data)](https://narev.ai/docs/guides/how-to-choose-llm-model-with-labels.md): Practical guide to picking the best LLM when you have labelled data, using benchmarks and accuracy metrics to compare candidate models objectively. - [Guides - Index](https://narev.ai/docs/guides/index.md): Step-by-step guides for cutting LLM costs, choosing models, benchmarking quality, and rolling out usage-based billing with the Narev platform. - [Reduce LLM spend by switching models](https://narev.ai/docs/guides/reduce-cost-by-model-switch.md): Case study: how switching from GPT-4 to gpt-oss-20b cut LLM inference costs by 99% while keeping 100% accuracy on a real product workload. - [Reduce LLM spend by prompt engineering](https://narev.ai/docs/guides/reduce-cost-by-prompt-engineering.md): Case study: how rewriting a verbose prompt into a shorter, simpler version cut LLM costs by 24% while preserving accuracy and improving consistency. - [Why benchmark?](https://narev.ai/docs/guides/why-benchmark.md): Academic LLM benchmarks like MMLU don't predict real-world quality. Learn why benchmarking on your own product data is what actually drives model choice. - [Introduction](https://narev.ai/docs/index.md): Narev is an AI FinOps platform that helps teams monetize their AI applications in minutes not days. Measure, optimize, and bill for LLM usage. - [Authentication](https://narev.ai/docs/platform/api-reference/authentication.md): Reference for the Narev Cloud REST APIs - [Overview](https://narev.ai/docs/platform/api-reference/overview.md): Reference for the Narev Cloud REST APIs - [LiteLLM-compatible pricing map](https://narev.ai/docs/platform/api-reference/v1/litellm-pricing-map.md): Full LiteLLM-format model cost map with narev's own pricing kept current for every model narev prices. Point LiteLLM's LITELLM_MODEL_COST_MAP_URL at this endpoint as a drop-in replacement to keep its cost map up to date. - [List prices](https://narev.ai/docs/platform/api-reference/v1/prices/list-prices.md): Returns pricing entries filtered by optional provider_id and model_id query parameters. - [Search pricing by model](https://narev.ai/docs/platform/api-reference/v1/prices/search.md): Search pricing across all providers by model ID. - [List models](https://narev.ai/docs/platform/api-reference/v1/reference/models.md): Returns all available models with their provider. - [List providers](https://narev.ai/docs/platform/api-reference/v1/reference/providers.md): Returns all supported providers with their display name. - [Calculate cost for trace](https://narev.ai/docs/platform/api-reference/v1/traces/cost.md): Given a model ID, provider, and token usage, returns an itemized cost breakdown in USD. - [Polar](https://narev.ai/docs/platform/billing/integrations/billing-platforms/polar.md): Send Narev Cloud LLM usage events to Polar and sync customers so you can bill end users for AI consumption with subscriptions and metered pricing. - [Next.js](https://narev.ai/docs/platform/billing/integrations/frameworks/nextjs.md): Add Narev Cloud usage metering and AI billing to a Next.js app with a billing-platform-agnostic pattern that works with Stripe, Polar, and OpenMeter. - [Integrations overview](https://narev.ai/docs/platform/billing/overview.md): Connect Narev Cloud to your billing stack with framework integrations like Next.js and billing platforms like Stripe, Polar, OpenMeter, and Kong. - [Benchmarks](https://narev.ai/docs/platform/concepts/benchmarks.md): Learn how Narev benchmarks group your prompts into evaluation datasets so you can compare LLM variants on cost, quality, and latency side by side. - [AI pricing models](https://narev.ai/docs/platform/concepts/pricing-models.md): Compare seat, subscription, credit, and usage-based pricing models for AI products and learn how to map inference costs to customer charges with Narev. - [Quality evaluations](https://narev.ai/docs/platform/concepts/quality-evaluations.md): Configure automatic and human-in-the-loop quality evaluations in Narev to score LLM responses, detect regressions, and pick the best variant. - [Usage-based billing](https://narev.ai/docs/platform/concepts/usage-based-billing.md): Learn how usage-based billing meters token and compute consumption per customer so AI products keep healthy margins as inference costs scale linearly. - [Variants](https://narev.ai/docs/platform/concepts/variants.md): A Narev variant captures a model choice, system prompt, and inference parameters so you can A/B test LLM configurations against your own benchmarks. - [Quickstart - Humans](https://narev.ai/docs/quickstart.md): Set up Narev Cloud, install the Narev SDK, meter Vercel AI SDK calls, tag customer usage, and send billable AI events to Polar. - [Quickstart - Agents](https://narev.ai/docs/quickstart-agents.md): Connect Cursor, Claude, VS Code, or any MCP-compatible agent to the Narev MCP server for real-time LLM pricing and current AI billing docs. - [Open Source at Narev](https://narev.ai/docs/sdk/index.md): Discover Narev's open-source initiatives for AI billing and infrastructure cost mapping. ## OpenAPI Specs - [openapi](https://narev.ai/docs/platform/api-reference/openapi.json)