Best AI tools for< Monitor Workloads >

Nomi.cloud is a modern AI-powered CloudOps and HPC assistant designed for next-gen businesses. It offers developers, marketplace, enterprise solutions, and pricing console. With features like single pane of glass view, instant deployment, continuous monitoring, AI-powered insights, and budgets & alerts built-in, Nomi.cloud aims to revolutionize cloud management. It provides a user-friendly interface to manage infrastructure efficiently, optimize costs, and deploy resources across multiple regions with ease. Nomi.cloud is built for scale, trusted by enterprises, and offers a range of GPUs and cloud providers to suit various needs.

Palo Alto Networks is a cybersecurity company offering advanced security solutions powered by Precision AI to protect modern enterprises from cyber threats. The company provides network security, cloud security, and AI-driven security operations to defend against AI-generated threats in real time. Palo Alto Networks aims to simplify security and achieve better security outcomes through platformization, intelligence-driven expertise, and proactive monitoring of sophisticated threats.

Helicone is an open-source platform designed for developers, offering observability solutions for logging, monitoring, and debugging. It provides sub-millisecond latency impact, 100% log coverage, industry-leading query times, and is ready for production-level workloads. Trusted by thousands of companies and developers, Helicone leverages Cloudflare Workers for low latency and high reliability, offering features such as prompt management, uptime of 99.99%, scalability, and reliability. It allows risk-free experimentation, prompt security, and various tools for monitoring, analyzing, and managing requests.

Modal is a high-performance cloud platform designed for developers, AI data, and ML teams. It offers a serverless environment for running generative AI models, large-scale batch jobs, job queues, and more. With Modal, users can bring their own code and leverage the platform's optimized container file system for fast cold boots and seamless autoscaling. The platform is engineered for large-scale workloads, allowing users to scale to hundreds of GPUs, pay only for what they use, and deploy functions to the cloud in seconds without the need for YAML or Dockerfiles. Modal also provides features for job scheduling, web endpoints, observability, and security compliance.

Deflekt.ai is an AI-powered application designed to automate replies on all support channels. It helps businesses deflect customer and coworker support tickets by answering questions automatically. The tool can be integrated with various channels like web chat, email, Slack, and Teams, making it a versatile solution for support teams. Deflekt.ai allows users to customize content, localize responses, and provide human support when needed. With features like document hosting, channel activation, and conversation monitoring, Deflekt.ai streamlines support processes and reduces workload.

LLMMM is an AI tool designed to monitor how AI models perceive and present brands. It offers real-time monitoring and cross-model insights to help brands understand their digital presence across various leading AI platforms. With automated analysis and lightning-fast results, LLMMM provides immediate visibility into how AI chatbots interpret brands. The tool focuses on brand intelligence, brand safety monitoring, misalignment detection, and cross-model brand intelligence. Users can create an account in minutes and access a range of features to track and analyze their brand's performance in the AI landscape.

New Relic is an AI monitoring platform that offers an all-in-one observability solution for monitoring, debugging, and improving the entire technology stack. With over 30 capabilities and 750+ integrations, New Relic provides the power of AI to help users gain insights and optimize performance across various aspects of their infrastructure, applications, and digital experiences.

Browse AI is an AI-powered data extraction and monitoring platform that allows users to scrape and monitor data from any website without the need for coding. It offers a full suite of features for stress-free data extraction, including turning websites into APIs, monitoring for changes, and creating prebuilt robots for various use cases. With over 7,000 integrations, Browse AI ensures reliable and scalable data extraction with no coding required. The platform is trusted by over 558,000 users worldwide and is designed to simplify the process of turning any website into a reliable data pipeline.

Arize AI is an AI Observability & LLM Evaluation Platform that helps you monitor, troubleshoot, and evaluate your machine learning models. With Arize, you can catch model issues, troubleshoot root causes, and continuously improve performance. Arize is used by top AI companies to surface, resolve, and improve their models.

Devi is an AI-powered social media lead generation and outreach tool that helps businesses find and engage with potential customers on Facebook, LinkedIn, Twitter, Reddit, and other platforms. It uses artificial intelligence to monitor keywords and identify high-intent leads, and then provides users with tools to reach out to those leads and build relationships. Devi also offers a variety of other features, such as AI-generated content, scheduling, and analytics.

Hexowatch is an AI-powered website monitoring and archiving tool that helps businesses track changes to any website, including visual, content, source code, technology, availability, or price changes. It provides detailed change reports, archives snapshots of pages, and offers side-by-side comparisons and diff reports to highlight changes. Hexowatch also allows users to access monitored data fields as a downloadable CSV file, Google Sheet, RSS feed, or sync any update via Zapier to over 2000 different applications.

Langtrace AI is an open-source observability tool powered by Scale3 Labs that helps monitor, evaluate, and improve LLM (Large Language Model) applications. It collects and analyzes traces and metrics to provide insights into the ML pipeline, ensuring security through SOC 2 Type II certification. Langtrace supports popular LLMs, frameworks, and vector databases, offering end-to-end observability and the ability to build and deploy AI applications with confidence.

KWatch.io is a social listening tool that helps businesses monitor keywords on social media platforms like LinkedIn, Twitter, Reddit, and Hacker News. It uses AI to analyze the sentiment around keywords and provides real-time alerts when specific keywords are mentioned. KWatch.io can be used for a variety of purposes, including attracting customers, getting feedback, watching competitors, conducting market intelligence, and providing customer support. It offers various plans, including a free plan, an essential plan for $19/month, a business plan for $79/month, and an enterprise plan for $199/month.

AI Spend is an AI application designed to help users monitor their AI costs and prevent surprises. It allows users to keep track of their OpenAI usage and costs, providing fast insights, a beautiful dashboard, cost insights, notifications, usage analytics, and details on models and tokens. The application ensures simple pricing with no additional costs and securely stores API keys. Users can easily remove their data if needed, emphasizing privacy and security.

Google Cloud Service Health Console provides status information on the services that are part of Google Cloud. It allows users to check the current status of services, view detailed overviews of incidents affecting their Google Cloud projects, and access custom alerts, API data, and logs through the Personalized Service Health dashboard. The console also offers a global view of the status of specific globally distributed services and allows users to check the status by product and location.

Pulse is a world-class expert support tool for BigData stacks, specifically focusing on ensuring the stability and performance of Elasticsearch and OpenSearch clusters. It offers early issue detection, AI-generated insights, and expert support to optimize performance, reduce costs, and align with user needs. Pulse leverages AI for issue detection and root-cause analysis, complemented by real human expertise, making it a strategic ally in search cluster management.

Vocera is an AI voice agent testing tool that allows users to test and monitor voice AI agents efficiently. It enables users to launch voice agents in minutes, ensuring a seamless conversational experience. With features like testing against AI-generated datasets, simulating scenarios, and monitoring AI performance, Vocera helps in evaluating and improving voice agent interactions. The tool provides real-time insights, detailed logs, and trend analysis for optimal performance, along with instant notifications for errors and failures. Vocera is designed to work for everyone, offering an intuitive dashboard and data-driven decision-making for continuous improvement.

Fiddler AI is an AI Observability platform that provides tools for monitoring, explaining, and improving the performance of AI models. It offers a range of capabilities, including explainable AI, NLP and CV model monitoring, LLMOps, and security features. Fiddler AI helps businesses to build and deploy high-performing AI solutions at scale.

The website offers Full-Stack Cloud Observability services with a focus on Middleware. It provides comprehensive monitoring and analysis tools for cloud-based applications, allowing users to gain insights into the performance and health of their middleware components. With a user-friendly interface and advanced features, it helps organizations optimize their cloud infrastructure and enhance overall system reliability.

Veriti is an AI-driven platform that proactively monitors and safely remediates exposures across the entire security stack, without disrupting the business. It helps organizations maximize their security posture while ensuring business uptime. Veriti offers solutions for safe remediation, MITRE ATT&CK®, healthcare, MSSPs, and manufacturing. The platform correlates exposures to misconfigurations, continuously assesses exposures, integrates with various security solutions, and prioritizes remediation based on business impact. Veriti is recognized for its role in exposure assessments and remediation, providing a consolidated security platform for businesses to neutralize threats before they happen.

Runbooks is a repository that is no longer active. The project has been deprecated in favor of KubeAI, a platform designed to simplify the operationalization of AI on Kubernetes. For more information, please refer to the new repository at https://github.com/substratusai/kubeai.

Vidur is a high-fidelity and extensible LLM inference simulator designed for capacity planning, deployment configuration optimization, testing new research ideas, and studying system performance of models under different workloads and configurations. It supports various models and devices, offers chrome trace exports, and can be set up using mamba, venv, or conda. Users can run the simulator with various parameters and monitor metrics using wandb. Contributions are welcome, subject to a Contributor License Agreement and adherence to the Microsoft Open Source Code of Conduct.

The Radicalbit AI Monitoring Platform provides a comprehensive solution for monitoring Machine Learning and Large Language models in production. It helps proactively identify and address potential performance issues by analyzing data quality, model quality, and model drift. The repository contains files and projects for running the platform, including UI, API, SDK, and Spark components. Installation using Docker compose is provided, allowing deployment with a K3s cluster and interaction with a k9s container. The platform documentation includes a step-by-step guide for installation and creating dashboards. Community engagement is encouraged through a Discord server. The roadmap includes adding functionalities for batch and real-time workloads, covering various model types and tasks.

CRB (Composable Runtime Blocks) is a unique framework that implements hybrid workloads by seamlessly combining synchronous and asynchronous activities, state machines, routines, the actor model, and supervisors. It is ideal for building massive applications and serves as a low-level framework for creating custom frameworks, such as AI-agents. The core idea is to ensure high compatibility among all blocks, enabling significant code reuse. The framework allows for the implementation of algorithms with complex branching, making it suitable for building large-scale applications or implementing complex workflows, such as AI pipelines. It provides flexibility in defining structures, implementing traits, and managing execution flow, allowing users to create robust and nonlinear algorithms easily.

Felafax is a framework designed to tune LLaMa3.1 on Google Cloud TPUs for cost efficiency and seamless scaling. It provides a Jupyter notebook for continued-training and fine-tuning open source LLMs using XLA runtime. The goal of Felafax is to simplify running AI workloads on non-NVIDIA hardware such as TPUs, AWS Trainium, AMD GPU, and Intel GPU. It supports various models like LLaMa-3.1 JAX Implementation, LLaMa-3/3.1 PyTorch XLA, and Gemma2 Models optimized for Cloud TPUs with full-precision training support.

Ray is a unified framework for scaling AI and Python applications. It consists of a core distributed runtime and a set of AI libraries for simplifying ML compute, including Data, Train, Tune, RLlib, and Serve. Ray runs on any machine, cluster, cloud provider, and Kubernetes, and features a growing ecosystem of community integrations. With Ray, you can seamlessly scale the same code from a laptop to a cluster, making it easy to meet the compute-intensive demands of modern ML workloads.

CentralMind Gateway is an AI-first data gateway that securely connects any data source and automatically generates secure, LLM-optimized APIs. It filters out sensitive data, adds traceability, and optimizes for AI workloads. Suitable for companies deploying AI agents for customer support and analytics.

Awesome MLOps is a curated list of tools related to Machine Learning Operations, covering areas such as AutoML, CI/CD for Machine Learning, Data Cataloging, Data Enrichment, Data Exploration, Data Management, Data Processing, Data Validation, Data Visualization, Drift Detection, Feature Engineering, Feature Store, Hyperparameter Tuning, Knowledge Sharing, Machine Learning Platforms, Model Fairness and Privacy, Model Interpretability, Model Lifecycle, Model Serving, Model Testing & Validation, Optimization Tools, Simplification Tools, Visual Analysis and Debugging, and Workflow Tools. The repository provides a comprehensive collection of tools and resources for individuals and teams working in the field of MLOps.

Kagent is a Kubernetes native framework for building AI agents, designed to be easy to understand and use. It provides a flexible and powerful way to build, deploy, and manage AI agents in Kubernetes. The framework consists of agents, tools, and model configurations defined as Kubernetes custom resources, making them easy to manage and modify. Kagent is extensible, flexible, observable, declarative, testable, and has core components like a controller, UI, engine, and CLI.

Call Center AI is an AI-powered call center solution leveraging Azure and OpenAI GPT. It allows for AI agent-initiated phone calls or direct calls to the bot from a configured phone number. The bot is customizable for various industries like insurance, IT support, and customer service, with features such as accessing claim information, conversation history, language change, SMS sending, and more. The project is a proof of concept showcasing the integration of Azure Communication Services, Azure Cognitive Services, and Azure OpenAI for an automated call center solution.

Llama-github is a powerful tool that helps retrieve relevant code snippets, issues, and repository information from GitHub based on queries. It empowers AI agents and developers to solve coding tasks efficiently. With features like intelligent GitHub retrieval, repository pool caching, LLM-powered question analysis, and comprehensive context generation, llama-github excels at providing valuable knowledge context for development needs. It supports asynchronous processing, flexible LLM integration, robust authentication options, and logging/error handling for smooth operations and troubleshooting. The vision is to seamlessly integrate with GitHub for AI-driven development solutions, while the roadmap focuses on empowering LLMs to automatically resolve complex coding tasks.

LLM App Stack, also known as Emerging Architectures for LLM Applications, is a comprehensive list of available tools, projects, and vendors at each layer of the LLM app stack. It covers various categories such as Data Pipelines, Embedding Models, Vector Databases, Playgrounds, Orchestrators, APIs/Plugins, LLM Caches, Logging/Monitoring/Eval, Validators, LLM APIs (proprietary and open source), App Hosting Platforms, Cloud Providers, and Opinionated Clouds. The repository aims to provide a detailed overview of tools and projects for building, deploying, and maintaining enterprise data solutions, AI models, and applications.

Kalavai is an open-source platform that transforms everyday devices into an AI supercomputer by aggregating resources from multiple machines. It facilitates matchmaking of resources for large AI projects, making AI hardware accessible and affordable. Users can create local and public pools, connect with the community's resources, and share computing power. The platform aims to be a management layer for research groups and organizations, enabling users to unlock the power of existing hardware without needing a devops team. Kalavai CLI tool helps manage both versions of the platform.

The AI Enablement Stack is a curated collection of venture-backed companies, tools, and technologies that enable developers to build, deploy, and manage AI applications. It provides a structured view of the AI development ecosystem across five key layers: Agent Consumer Layer, Observability and Governance Layer, Engineering Layer, Intelligence Layer, and Infrastructure Layer. Each layer focuses on specific aspects of AI development, from end-user interaction to model training and deployment. The stack aims to help developers find the right tools for building AI applications faster and more efficiently, assist engineering leaders in making informed decisions about AI infrastructure and tooling, and help organizations understand the AI development landscape to plan technology adoption.

Contoso Chat is a Python sample demonstrating how to build, evaluate, and deploy a retail copilot application with Azure AI Studio using Promptflow with Prompty assets. The sample implements a Retrieval Augmented Generation approach to answer customer queries based on the company's product catalog and customer purchase history. It utilizes Azure AI Search, Azure Cosmos DB, Azure OpenAI, text-embeddings-ada-002, and GPT models for vectorizing user queries, AI-assisted evaluation, and generating chat responses. By exploring this sample, users can learn to build a retail copilot application, define prompts using Prompty, design, run & evaluate a copilot using Promptflow, provision and deploy the solution to Azure using the Azure Developer CLI, and understand Responsible AI practices for evaluation and content safety.

Synmetrix (prev. MLCraft) is an open source data engineering platform and semantic layer for centralized metrics management. It provides a complete framework for modeling, integrating, transforming, aggregating, and distributing metrics data at scale. Key features include data modeling and transformations, semantic layer for unified data model, scheduled reports and alerts, versioning, role-based access control, data exploration, caching, and collaboration on metrics modeling. Synmetrix leverages Cube (Cube.js) for flexible data models that consolidate metrics from various sources, enabling downstream distribution via a SQL API for integration into BI tools, reporting, dashboards, and data science. Use cases include data democratization, business intelligence, embedded analytics, and enhancing accuracy in data handling and queries. The tool speeds up data-driven workflows from metrics definition to consumption by combining data engineering best practices with self-service analytics capabilities.

Synmetrix is an open source data engineering platform and semantic layer for centralized metrics management. It provides a complete framework for modeling, integrating, transforming, aggregating, and distributing metrics data at scale. Key features include data modeling and transformations, semantic layer for unified data model, scheduled reports and alerts, versioning, role-based access control, data exploration, caching, and collaboration on metrics modeling. Synmetrix leverages Cube.js to consolidate metrics from various sources and distribute them downstream via a SQL API. Use cases include data democratization, business intelligence and reporting, embedded analytics, and enhancing accuracy in data handling and queries. The tool speeds up data-driven workflows from metrics definition to consumption by combining data engineering best practices with self-service analytics capabilities.

FastAgency is a powerful tool that leverages the AutoGen framework to quickly build applications with multi-agent workflows. It supports various interfaces like ConsoleUI and MesopUI, allowing users to create interactive applications. The tool enables defining workflows between agents, such as students and teachers, and summarizing conversations. FastAgency aims to expand its capabilities by integrating with additional agentic frameworks like CrewAI, providing more options for workflow definition and AI tool integration.

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.

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Seismic and volcanic monitor with in-depth data and visuals.

Frost Protection System is an AI bot optimizing open field farming of fruits, vegetables, and flowers, combining real-time data and AI to boost yield, cut costs, and foster sustainable practices in a user-friendly interface.

Your SRE, DevOps and Observability buddy

A specialist in DataOps and Data Observability, aiding in data management and monitoring.

EUR/ZAR analysis with real-time rate and current news focus

stunspot's advisor for all things Financial Cybersec

Expert in Broad Financial Compliance

Specializes in Anti-Money Laundering/Counter-Financing of Terrorism compliance and analysis.

Advanced DevOps Guru with Linux distro and cloud-native tech expertise.

Facilitates Socratic dialogues to refine your network state.

Expert in Maritime Conservation & Drone Technology.

A bot that provides detailed cybersecurity threat intelligence.

Ensures product quality through systematic process monitoring and evaluation.

Global Conflict Analyst that will provide a 'wartime update' on the worst global conflict atm.

Monitoring tools expert with brief responses and guiding hints.

Ensures efficient and effective network performance and security.

Critically examines government actions and policies