AI Infrastructure landscape

Infrabase.ai provides an overview of the AI infrastructure landscape, highlighting tools and services for AI builders. Explore vector databases for RAG, discover prompt engineering tools, and find runtimes for LLMs to power AI projects. Use the AI infrastructure landscape to identify the components required to enable your next product.

🗄️ Vector databases

A vector database stores data as mathematical vectors, enabling efficient similarity searches for AI-driven applications like search engines, recommendation systems, and Retrieval-Augmented Generation (RAG). This makes it easier for developers to integrate advanced AI functionalities into their applications with ability to search and understand relationships within data.

✍️ Prompt engineering

Prompt engineering is the skill of designing inputs that steer large language models (LLMs) toward generating targeted behaviors and outputs. It is a foundation skill for developers creating AI applications using many of the current AI systems. These services enhance prompt development, maintenance and generation.

📊 Observability & Analytics

Specialized DevOps tools tailored for optimizing LLMs: from tuning parameters to enhance task-specific performance to analytics for monitoring and refining LLM applications.

🤖 Inference APIs

APIs and runtimes for AI models, especially LLMs, enable powerful text generation and processing in apps. They serve as the foundation for many AI solutions and allow easy integration, making advanced AI accessible for developers.

🏗️ Frameworks & Stacks

The tools and frameworks providing the foundation for AI development offer practical solutions for constructing and deploying AI applications. They facilitate the use of collective research, knowledge, and experience in the field of AI solution development.

🧠 Fine-tuning

Fine-tuning AI models involves adjusting the parameters of a pre-trained model to perform better on a specific task or dataset. This process allows the model to adapt its learned knowledge to new, related problems, enhancing its accuracy and effectiveness for specialized applications.

🔊 Audio

Generative AI audio models create realistic text-to-speech voices and music, offering a human touch to applications and enabling accessibility, as well as enriching user experiences with audio interactions.

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