LLM Use Cases, Analyses & Benchmarks
LLMs are AI systems trained on vast text data to understand, generate, and manipulate human language for business tasks. We benchmark performance, use cases, cost analyses, deployment options, and best practices to guide enterprise LLM adoption.
Explore LLM Use Cases, Analyses & Benchmarks
LLM Pricing: Top 15+ Providers Compared
LLM API pricing can be complex and depends on your preferred usage. We analyzed 15+ LLMs and their pricing and performance: Hover over model names to view their benchmark results, real-world latency, and pricing, to assess each model’s efficiency and cost-effectiveness. Ranking: Models are ranked by their average position across all benchmarks.
LLM VRAM Calculator for Self-Hosting
The use of LLMs has become inevitable, but relying solely on cloud-based APIs can be limiting due to cost, reliance on third parties, and potential privacy concerns. That’s where self-hosting an LLM for inference (also called on-premises LLM hosting or on-prem LLM hosting) comes in.
Compare Top 12 LLM Orchestration Frameworks
Leveraging multiple LLMs concurrently demands significant computational resources, driving up costs and introducing latency challenges. In the evolving landscape of AI, efficient LLM orchestration is essential for optimizing performance while minimizing expenses. Explore key strategies and tools for managing multiple LLMs effectively.
Top 5 AI Gateways for OpenAI: OpenRouter Alternatives
The increasing number of LLM providers complicates API management. AI gateways simplify this by serving as a unified access point, allowing developers to interact with multiple providers through a single API.
LLM Observability Tools: Weights & Biases, Langsmith
LLM-based applications are becoming more capable and increasingly complex, making their behavior harder to interpret. Each model output results from prompts, tool interactions, retrieval steps, and probabilistic reasoning that cannot be directly inspected. LLM observability addresses this challenge by providing continuous visibility into how models operate in real-world conditions.
Compare 9 Large Language Models in Healthcare
We benchmarked 9 LLMs using the MedQA dataset, a graduate-level clinical exam benchmark derived from USMLE questions. Each model answered the same multiple-choice clinical scenarios using a standardized prompt, enabling direct comparison of accuracy. We also recorded latency per question by dividing total runtime by the number of MedQA items completed.
Large Language Models in Cybersecurity
We evaluated 7 large language models across 9 cybersecurity domains using SecBench, a large-scale and multi-format benchmark for security tasks. We tested each model on 44,823 multiple-choice questions (MCQs) and 3,087 short-answer questions (SAQs), covering areas such as data security, identity & access management, network security, vulnerability management, and cloud security.
Top 40+ LLMOps Tools & Compare them to MLOPs
The rapid adoption of large language models has outpaced the operational frameworks needed to manage them efficiently. Enterprises increasingly struggle with high development costs, complex pipelines, and limited visibility into model performance. LLMOps tools aim to address these challenges by providing structured processes for fine-tuning, deployment, monitoring, and governance.
Large Language Model Training
While using existing LLMs in enterprise workflows is table stakes, leading enterprises are building their custom models. However, building custom models can cost millions and require investing in an internal AI team.
LLM Fine-Tuning Guide for Enterprises
Follow the links for the specific solutions to your LLM output challenges. If your LLM: The widespread adoption of large language models (LLMs) has improved our ability to process human language. However, their generic training often results in suboptimal performance for specific tasks.