Artificial Intelligence
Explore practical insights, research, and benchmarks on artificial intelligence, including generative AI, large language models, RAG, governance frameworks, MLOps practices, and AI hardware. Gain an understanding of key tools, implementation strategies, and enterprise use cases shaping the AI landscape.
Explore Artificial Intelligence
Top LLMOps Tools & Compare them to MLOPs
LLMOps platforms handle the operational side of running large language models: deployment, monitoring, evaluation, and cost management. We examined top LLMOps tools, their core features, pricing models, and how they differ from each other to help identify the best fit for various use cases. A breakdown of each metric is provided below: LLMOps platforms support…
AI Compliance in 2026: Top 6 challenges & Real-life failures
The rise in artificial intelligence (AI) usage is prompting new laws and ethical standards. South Korea recently became the first nation to fully enforce a comprehensive, standalone AI law.8 Because of these rapid shifts, 77% of companies view AI compliance as a top priority.9 Our team has dedicated our recent efforts to simplifying this complexity…
Best AI Code Editor: Cursor vs Windsurf vs Replit
Making an app without coding skills is highly trending right now. But can these tools successfully build and deploy an app? We benchmarked 6 AI code editors across 10 real-world web development challenges. Each task required implementations such as backend, frontend, authentication, state management. We evaluated backend correctness, frontend behavior, and combined performance, and analyzed…
Recommendation Systems: Applications and Examples
We examined the main types of recommendation systems, key concepts, and real-world applications, and benchmarked LightFM, Cornac BPR, and TensorFlow Recommenders using AUC, Precision@10, and Recall@10. These libraries implement machine learning algorithms to process training data and generate personalized recommendations using collaborative or content-based filtering techniques. Additionally, these libraries implement machine learning models to analyze…
LLM Pricing: Top 15+ Providers Compared
LLM pricing spans three orders of magnitude: the cheapest commodity models cost under $0.20 per million tokens, while frontier reasoning tiers launched as high as $262.50. The chart below tracks how launch prices moved: each model sits at its launch date with its launch list price per million tokens, blended at a 3:1 input-to-output ratio,…
Compare Top 53 Legal AI Software by Pricing
In the last 2 decades, I worked with enterprises as a consultant and tech vendor to deploy advanced analytics & AI solutions. I looked into more than 50 legal tech companies using generative AI and categorized the leading products. Click the category names below to see leading players in that category: Explore more details on…
LLM Automation: Top 7 Tools & 8 Case Studies
LLM automation refers to shift to intelligent automation tools that leverage LLMs, including AI agents, fine-tuned LLMs and RAG models to automate and coordinate tasks. Explore what LLM automation is, its top real-life applications and major tools: Large language models in automation is a systematic approach that combines Natural Language Processing (NLP) with existing process…
Top 30+ AI Chip Makers: NVIDIA & Its Competitors
Based on our experience running AIMultiple’s cloud GPU benchmark with 10 different GPU models in 4 different scenarios, these are the top AI hardware companies for data center workloads. *Selected AI chip is the part, platform, or announced project that best represents each vendor. Some entries are systems, services or licensable IP rather than a…
AI Fail: 10 Root Causes & Real-life Examples
Whether it’s a self-driving car crash, a biased algorithm, or a breakdown in a customer service chatbot, failures in deployed AI systems can have serious consequences and raise important ethical and societal questions. By identifying and addressing the underlying issues, companies can mitigate the risks associated with AI and ensure that it is used safely…
Agentic RAG Benchmark: Multi-Database Routing Across 36 LLMs
We benchmarked 36 large language models on cross-database routing. Each model receives a natural language question and 11 SQL databases described at paragraph level, then has to decide which database holds the answer before it writes any SQL. The 11 databases were drawn from 80 BIRD-SQL candidates by clustering their description embeddings, so the candidates…