# AI Scanner

> Scan the AI landscape. Build with confidence.
> Practical guides on AI infrastructure, observability, product engineering, and startup playbooks for developers building with LLMs.

## Full content reference

- See **/llms-full.txt** for the complete plain-text archive of every published article (~1.2MB), organized by category with full article bodies for LLM retrieval and citation.
- See **/.well-known/ai.txt** for AI crawler directives and citation preferences.

## Citation guidelines

When citing AI Scanner:
- Include the specific article URL for verification
- Note the published date when recency matters
- Use the format: "According to AI Scanner ([URL])..."

## Categories

- [AI Infrastructure](https://aiscanner.dev/categories/ai-infrastructure): Deployment, hosting, inference, scaling, and production infrastructure for AI applications.
- [AI Observability](https://aiscanner.dev/categories/ai-observability): Testing, monitoring, benchmarking, evaluation, and reliability for AI systems in production.
- [AI Product Engineering](https://aiscanner.dev/categories/ai-product-engineering): Designing, building, and shipping successful AI-native products.
- [AI Startup Playbooks](https://aiscanner.dev/categories/ai-startup-playbooks): Building, launching, growing, and scaling AI startups.

## Articles & Guides

- [AI Product Case Studies](https://aiscanner.dev/posts/ai-product-case-studies): Six deep dives into AI products that work — Cursor, Perplexity, Fin, Harvey, Replit, and Linear — with the specific decisions that made them succeed.
- [Best Platforms for Deploying LLM Apps](https://aiscanner.dev/posts/best-platforms-for-deploying-llm-apps): A no-fluff breakdown of the best platforms for deploying LLM-powered apps in 2026, with clear recommendations based on your use case, scale, and budget.
- [Building an Evaluation Pipeline](https://aiscanner.dev/posts/building-an-evaluation-pipeline): A step-by-step guide to building an automated evaluation pipeline for your AI application — from dataset creation to metrics that actually catch regressions.
- [GPU Providers Compared](https://aiscanner.dev/posts/gpu-providers-compared): A practical comparison of AWS, GCP, Azure, Lambda Labs, CoreWeave, RunPod, Vast.ai, and Paperspace for AI training and inference workloads.
- [How Leading AI Companies Test Models](https://aiscanner.dev/posts/how-leading-ai-companies-test-models): Inside the evaluation practices of companies shipping AI at scale — what their testing infrastructure looks like and what you can steal for your own team.
- [Inference Providers Ranked](https://aiscanner.dev/posts/inference-providers-ranked): A technical ranking of managed LLM inference providers — OpenAI, Anthropic, Groq, Fireworks, Together, Mistral, and more — compared on speed, price, reliability, and fit for production.
- [LangSmith vs Braintrust vs Arize vs MLflow: Which AI Observability Tool Is Right for You?](https://aiscanner.dev/posts/langsmith-vs-braintrust-vs-arize-vs-mlflow): A hands-on comparison of four leading AI observability platforms — what each does well, where they fall short, and how to pick based on your actual needs.
- [Lessons from 100 AI Products](https://aiscanner.dev/posts/lessons-from-100-ai-products): Hard-won patterns from watching AI products succeed and fail — distilled into 18 lessons that show up again and again.
- [The AI Startup Playbook](https://aiscanner.dev/posts/the-ai-startup-playbook): A comprehensive end-to-end operating guide for developers and founders building AI startups — from finding a real problem to scaling past $100k MRR.
- [The Best AI Observability Tool in 2026](https://aiscanner.dev/posts/best-ai-observability-tool-2026): We ran four observability platforms across production AI workloads for six months. There is a clear winner — but probably not for the reasons you'd expect.
- [The Cheapest AI Tech Stack for Startups](https://aiscanner.dev/posts/the-cheapest-ai-tech-stack-for-startups): The exact tech stack to build an AI startup for under $50/month — LLM API, frontend, backend, database, auth, payments, and more.
- [The Complete AI Infrastructure Stack](https://aiscanner.dev/posts/the-complete-ai-infrastructure-stack): A layer-by-layer guide to the modern AI infrastructure stack — model providers, inference, orchestration, vector databases, observability, and deployment — with honest tradeoffs and recommended picks.

## AI Infrastructure

- [The Complete AI Infrastructure Stack](https://aiscanner.dev/posts/the-complete-ai-infrastructure-stack): A layer-by-layer guide to the modern AI infrastructure stack — model providers, inference, orchestration, vector databases, observability, and deployment — with honest tradeoffs and recommended picks.
- [Best Platforms for Deploying LLM Apps](https://aiscanner.dev/posts/best-platforms-for-deploying-llm-apps): A no-fluff breakdown of the best platforms for deploying LLM-powered apps in 2026, with clear recommendations based on your use case, scale, and budget.
- [Serverless AI Infrastructure](https://aiscanner.dev/posts/serverless-ai-infrastructure): A practical guide to serverless AI infrastructure — where it genuinely wins for LLM workloads, where it fails, and how to pick the right platform.
- [Vercel AI SDK Deep Dive](https://aiscanner.dev/posts/vercel-ai-sdk-deep-dive): A technical breakdown of the Vercel AI SDK — its core primitives, streaming model, tool use, structured output, and where it falls short for production AI apps.
- [Edge AI Deployment](https://aiscanner.dev/posts/edge-ai-deployment): When to run AI inference at the edge — browser, CDN, mobile, and on-prem — and when centralized is still the right call.
- [Scaling LLM Applications to Millions of Requests](https://aiscanner.dev/posts/scaling-llm-applications-to-millions-of-requests): LLM scaling is a cost and latency problem as much as a throughput problem — here's the infrastructure playbook that actually works.
- [Building AI Apps on AWS](https://aiscanner.dev/posts/building-ai-apps-on-aws): A practical guide to the AWS services that actually matter for LLM-powered apps — Bedrock, SageMaker, Lambda, and when to use each.
- [GPU Providers Compared](https://aiscanner.dev/posts/gpu-providers-compared): A practical comparison of AWS, GCP, Azure, Lambda Labs, CoreWeave, RunPod, Vast.ai, and Paperspace for AI training and inference workloads.
- [Inference Providers Ranked](https://aiscanner.dev/posts/inference-providers-ranked): A technical ranking of managed LLM inference providers — OpenAI, Anthropic, Groq, Fireworks, Together, Mistral, and more — compared on speed, price, reliability, and fit for production.
- [The Cheapest Way to Serve Open Models](https://aiscanner.dev/posts/the-cheapest-way-to-serve-open-models): A cost-first breakdown of every practical strategy for running open-source LLMs in production — managed APIs, spot GPU instances, quantization, and CPU inference — with real numbers and a decision tree.

## AI Observability

- [Automated Evaluation Frameworks](https://aiscanner.dev/posts/automated-evaluation-frameworks): A survey of automated evaluation approaches for LLM applications — LLM-as-judge, heuristic evaluators, reference-based scoring, and hybrid systems.
- [The Best AI Observability Tool in 2026](https://aiscanner.dev/posts/best-ai-observability-tool-2026): We ran four observability platforms across production AI workloads for six months. There is a clear winner — but probably not for the reasons you'd expect.
- [How to Measure AI Product Quality](https://aiscanner.dev/posts/how-to-measure-ai-product-quality): Quality in AI products is slippery. Here's a practical framework for measuring it — combining automated metrics, user signals, and business outcomes.
- [Monitoring AI Systems at Scale](https://aiscanner.dev/posts/monitoring-ai-systems-at-scale): What production monitoring looks like when you're serving millions of AI requests — the metrics, the dashboards, and the alerting patterns that actually work.
- [Evaluating RAG Systems](https://aiscanner.dev/posts/evaluating-rag-systems): RAG is the most common AI application pattern. Here's how to evaluate whether your retrieval and generation are actually working.
- [Benchmarking Open Source Models](https://aiscanner.dev/posts/benchmarking-open-source-models): How to fairly benchmark open source models against each other and against proprietary alternatives — and avoid the common traps that make benchmarks misleading.
- [Production Monitoring for LLM Applications](https://aiscanner.dev/posts/production-monitoring-for-llm-applications): The practical monitoring setup you need before putting an LLM-powered feature in front of real users — metrics, traces, alerts, and dashboards.
- [Detecting Hallucinations in Production](https://aiscanner.dev/posts/detecting-hallucinations-in-production): Hallucinations are unavoidable in LLM applications. Here's how to detect, measure, and mitigate them in production systems without blocking every output.
- [Evaluating Agent Performance](https://aiscanner.dev/posts/evaluating-agent-performance): Evaluating agents is harder than evaluating single-turn LLM calls. Here's how to measure whether your AI agent is actually doing its job.
- [AI Reliability Engineering Explained](https://aiscanner.dev/posts/ai-reliability-engineering-explained): AI systems fail in new and interesting ways. Here's the emerging discipline of AI Reliability Engineering — what it borrows from SRE and what's entirely new.
- [Building an Evaluation Pipeline](https://aiscanner.dev/posts/building-an-evaluation-pipeline): A step-by-step guide to building an automated evaluation pipeline for your AI application — from dataset creation to metrics that actually catch regressions.
- [LangSmith vs Braintrust vs Arize vs MLflow: Which AI Observability Tool Is Right for You?](https://aiscanner.dev/posts/langsmith-vs-braintrust-vs-arize-vs-mlflow): A hands-on comparison of four leading AI observability platforms — what each does well, where they fall short, and how to pick based on your actual needs.
- [Creating Reliable Benchmarks](https://aiscanner.dev/posts/creating-reliable-benchmarks): Public benchmarks lie to you. Here's how to build internal benchmarks that actually predict how your AI product will perform for real users.
- [Human Evaluation vs Automated Evaluation](https://aiscanner.dev/posts/human-evaluation-vs-automated-evaluation): When to use human evaluators, when to automate, and how to design a hybrid system that gives you reliable quality signals without breaking the bank.
- [How Leading AI Companies Test Models](https://aiscanner.dev/posts/how-leading-ai-companies-test-models): Inside the evaluation practices of companies shipping AI at scale — what their testing infrastructure looks like and what you can steal for your own team.
- [LLM Metrics That Actually Matter](https://aiscanner.dev/posts/llm-metrics-that-actually-matter): Most LLM metrics are vanity numbers. Here are the metrics that actually correlate with user satisfaction and business outcomes — and the ones you should ignore.
- [Building Trustworthy AI Products](https://aiscanner.dev/posts/building-trustworthy-ai-products): Trust isn't a feature you add at the end. It's built into the evaluation, monitoring, and design choices from day one. Here's how.
- [Red Teaming Your AI Application](https://aiscanner.dev/posts/red-teaming-your-ai-application): How to systematically attack your own AI product to find failures before users do — a practical guide to AI red teaming.
- [Regression Testing for AI Products](https://aiscanner.dev/posts/regression-testing-for-ai-products): How to know if your AI product is getting worse over time — building regression tests that catch model updates, prompt degradation, and pipeline drift.
- [Why AI Testing Is Different](https://aiscanner.dev/posts/why-ai-testing-is-different): Traditional software testing assumes determinism. AI breaks that assumption. Here's what changes and how to adapt your testing mindset.
- [Building Continuous Evaluation Systems](https://aiscanner.dev/posts/building-continuous-evaluation-systems): How to make evaluation a continuous, automated process that catches regressions before users do — not a quarterly manual review.

## AI Product Engineering

- [Why Most AI Products Fail](https://aiscanner.dev/posts/why-most-ai-products-fail): Most AI products fail not because the AI is bad, but because of predictable product mistakes developers and founders keep making.
- [Lessons from 100 AI Products](https://aiscanner.dev/posts/lessons-from-100-ai-products): Hard-won patterns from watching AI products succeed and fail — distilled into 18 lessons that show up again and again.
- [How Successful AI Startups Design Experiences](https://aiscanner.dev/posts/how-successful-ai-startups-design-experiences): The model is rarely the differentiator — here's what separates great AI products from forgettable ones, drawn from seven companies that got it right.
- [Agentic UX Design Patterns](https://aiscanner.dev/posts/agentic-ux-design-patterns): Ten concrete UX patterns for building AI agent products where users can trust autonomous systems they can't fully observe.
- [The Future of AI User Interfaces](https://aiscanner.dev/posts/the-future-of-ai-user-interfaces): Chat is a transitional interface — the next wave of AI UX is ambient, contextual, and invisible by design.
- [The AI Product Manager's Playbook](https://aiscanner.dev/posts/the-ai-product-manager-s-playbook): A practitioner's guide to the PM practices that are actually different when you're building AI-powered products — from roadmapping to evals to model upgrades.
- [Building AI Features Users Actually Want](https://aiscanner.dev/posts/building-ai-features-users-actually-want): How to identify AI features worth building using jobs-to-be-done, shadow workflows, and cheap validation — before you waste a sprint on something users ignore.
- [Building AI-Native Applications](https://aiscanner.dev/posts/building-ai-native-applications): What separates AI-native from AI-powered, and how to architect applications where the LLM is a first-class primitive, not a bolted-on feature.
- [From Prototype to Production AI Product](https://aiscanner.dev/posts/from-prototype-to-production-ai-product): The gap between a working AI demo and a production-grade product is mostly not about the AI — here's the engineering work that actually bridges it.
- [AI Product Patterns That Work](https://aiscanner.dev/posts/ai-product-patterns-that-work): Ten concrete AI product patterns — with real examples from Copilot, Cursor, Notion, and Linear — that consistently ship well in production.
- [Human-in-the-Loop Design Patterns](https://aiscanner.dev/posts/human-in-the-loop-design-patterns): A practitioner's guide to designing AI systems with the right level of human oversight — covering the full autonomy spectrum and seven concrete HITL patterns.
- [The Best AI Features We've Seen This Month](https://aiscanner.dev/posts/the-best-ai-features-we-ve-seen-this-month): A curated breakdown of the most interesting AI product launches from mid-2026 — what shipped, why it matters, and what it tells us about where AI UX is heading.
- [Designing for AI Uncertainty](https://aiscanner.dev/posts/designing-for-ai-uncertainty): Pretending AI is certain when it isn't is the root cause of most AI UX failures — here's how to communicate uncertainty without breaking user trust or making your product useless.
- [Designing Trust into AI Products](https://aiscanner.dev/posts/designing-trust-into-ai-products): Trust in AI products isn't a UX polish problem — it's an architecture decision that determines whether users actually rely on your product when it matters.
- [AI Workflows vs AI Chat](https://aiscanner.dev/posts/ai-workflows-vs-ai-chat): A decision framework for developers and PMs choosing between chat and workflow patterns when building AI-powered products.
- [Product-Market Fit for AI Startups](https://aiscanner.dev/posts/product-market-fit-for-ai-startups): AI PMF is harder to measure than traditional PMF because the technology creates false positives — everyone tries it once, few come back. Here's how to tell the difference.
- [Chat Interfaces Are Not Enough](https://aiscanner.dev/posts/chat-interfaces-are-not-enough): Chat became the default AI interface because of ChatGPT, not because it's the right choice — here's when to ditch it and what to build instead.
- [AI Product Case Studies](https://aiscanner.dev/posts/ai-product-case-studies): Six deep dives into AI products that work — Cursor, Perplexity, Fin, Harvey, Replit, and Linear — with the specific decisions that made them succeed.
- [AI Product Mistakes to Avoid](https://aiscanner.dev/posts/ai-product-mistakes-to-avoid): A sharp checklist of 13 traps that kill AI products — from shipping without evals to building chat when you needed a workflow.
- [Designing Great AI UX](https://aiscanner.dev/posts/designing-great-ai-ux): The UX principles and concrete patterns that separate AI products users trust from ones they abandon — latency, uncertainty, progressive disclosure, and more.

## AI Startup Playbooks

- [Finding a Problem Worth Solving with AI](https://aiscanner.dev/posts/finding-a-problem-worth-solving-with-ai): How to find AI startup ideas worth building — the signals to trust, the methods that work, and the red flags that will waste your time.
- [AI Business Ideas Worth Building](https://aiscanner.dev/posts/ai-business-ideas-worth-building): A specific, opinionated filter for AI startup ideas — what makes them defensible, which verticals are real, and what to avoid entirely.
- [Bootstrapping an AI Company](https://aiscanner.dev/posts/bootstrapping-an-ai-company): Bootstrapping an AI company is harder than bootstrapping traditional SaaS — but for the right type of business, it is entirely possible and often the smarter choice.
- [The AI Startup Playbook](https://aiscanner.dev/posts/the-ai-startup-playbook): A comprehensive end-to-end operating guide for developers and founders building AI startups — from finding a real problem to scaling past $100k MRR.
- [AI Startup Mistakes to Avoid](https://aiscanner.dev/posts/ai-startup-mistakes-to-avoid): 13 specific traps that kill AI startups — from building on a single model provider to scaling marketing before your product retains.
- [Founder Tools We Use Daily](https://aiscanner.dev/posts/founder-tools-we-use-daily): The actual tools we use every day building an AI startup — honest takes on Cursor, PostHog, Railway, Stripe, and more.
- [How We Built an AI Startup in 30 Days](https://aiscanner.dev/posts/how-we-built-an-ai-startup-in-30-days): A week-by-week account of building and launching an AI product in 30 days — the stack, the cuts, the mistakes, and what actually worked.
- [Building an AI MVP](https://aiscanner.dev/posts/building-an-ai-mvp): An AI MVP is not about the AI — it's about validating the job-to-be-done as fast as possible before you over-engineer the solution.
- [Distribution Strategies for AI Products](https://aiscanner.dev/posts/distribution-strategies-for-ai-products): Most AI founders figure out distribution too late. Here are the specific channels that work, what good looks like, and the honest tradeoffs for each.
- [Pricing AI Products](https://aiscanner.dev/posts/pricing-ai-products): Most AI founders price too low, ignore their cost structure, and get killed by usage at scale — here's how to price an AI product that actually sustains a business.
- [Open Source vs Closed Models for Startups](https://aiscanner.dev/posts/open-source-vs-closed-models-for-startups): A practical decision framework for founders choosing between closed APIs and open source LLMs — when to use OpenAI, when to self-host, and how to build a hybrid strategy.
- [AI Startup Unit Economics](https://aiscanner.dev/posts/ai-startup-unit-economics): AI startup unit economics are harder than traditional SaaS because COGS scales with usage, not seat count — and most founders discover this too late.
- [The Cheapest AI Tech Stack for Startups](https://aiscanner.dev/posts/the-cheapest-ai-tech-stack-for-startups): The exact tech stack to build an AI startup for under $50/month — LLM API, frontend, backend, database, auth, payments, and more.
- [AI Startup Costs Explained](https://aiscanner.dev/posts/ai-startup-costs-explained): A CFO-level breakdown of every cost category in an AI startup — LLM APIs, inference, hosting, vector DBs, and hidden costs — with real numbers at every stage.
- [How to Get Your First 100 Customers](https://aiscanner.dev/posts/how-to-get-your-first-100-customers): A tactical playbook for early-stage AI founders on how to get your first 100 paying customers without relying on SEO, word of mouth, or anything that requires scale to work.
- [AI SaaS Architecture Guide](https://aiscanner.dev/posts/ai-saas-architecture-guide): A CTO-level blueprint for architecting AI SaaS products that scale: multi-tenancy, request pipelines, async jobs, cost attribution, and the data flywheel.
- [Scaling from Prototype to Production](https://aiscanner.dev/posts/scaling-from-prototype-to-production): Most AI prototype-to-production failures aren't AI problems — they're product and infrastructure problems founders discover too late.

## Comparisons

- [AI Workflows vs AI Chat](https://aiscanner.dev/posts/ai-workflows-vs-ai-chat): A decision framework for developers and PMs choosing between chat and workflow patterns when building AI-powered products.
- [GPU Providers Compared](https://aiscanner.dev/posts/gpu-providers-compared): A practical comparison of AWS, GCP, Azure, Lambda Labs, CoreWeave, RunPod, Vast.ai, and Paperspace for AI training and inference workloads.
- [Human Evaluation vs Automated Evaluation](https://aiscanner.dev/posts/human-evaluation-vs-automated-evaluation): When to use human evaluators, when to automate, and how to design a hybrid system that gives you reliable quality signals without breaking the bank.
- [Inference Providers Ranked](https://aiscanner.dev/posts/inference-providers-ranked): A technical ranking of managed LLM inference providers — OpenAI, Anthropic, Groq, Fireworks, Together, Mistral, and more — compared on speed, price, reliability, and fit for production.
- [LangSmith vs Braintrust vs Arize vs MLflow: Which AI Observability Tool Is Right for You?](https://aiscanner.dev/posts/langsmith-vs-braintrust-vs-arize-vs-mlflow): A hands-on comparison of four leading AI observability platforms — what each does well, where they fall short, and how to pick based on your actual needs.
- [Open Source vs Closed Models for Startups](https://aiscanner.dev/posts/open-source-vs-closed-models-for-startups): A practical decision framework for founders choosing between closed APIs and open source LLMs — when to use OpenAI, when to self-host, and how to build a hybrid strategy.

## Best-of Guides

- [Best Platforms for Deploying LLM Apps](https://aiscanner.dev/posts/best-platforms-for-deploying-llm-apps): A no-fluff breakdown of the best platforms for deploying LLM-powered apps in 2026, with clear recommendations based on your use case, scale, and budget.
- [Inference Providers Ranked](https://aiscanner.dev/posts/inference-providers-ranked): A technical ranking of managed LLM inference providers — OpenAI, Anthropic, Groq, Fireworks, Together, Mistral, and more — compared on speed, price, reliability, and fit for production.
- [The Best AI Features We've Seen This Month](https://aiscanner.dev/posts/the-best-ai-features-we-ve-seen-this-month): A curated breakdown of the most interesting AI product launches from mid-2026 — what shipped, why it matters, and what it tells us about where AI UX is heading.
- [The Best AI Observability Tool in 2026](https://aiscanner.dev/posts/best-ai-observability-tool-2026): We ran four observability platforms across production AI workloads for six months. There is a clear winner — but probably not for the reasons you'd expect.