Best AI tools for< Gcp Architect >
Infographic
9 - AI tool Sites
![Cirrascale Cloud Services Screenshot](/screenshots/cirrascale.com.jpg)
Cirrascale Cloud Services
Cirrascale Cloud Services is an AI tool that offers cloud solutions for Artificial Intelligence applications. The platform provides a range of cloud services and products tailored for AI innovation, including NVIDIA GPU Cloud, AMD Instinct Series Cloud, Qualcomm Cloud, Graphcore, Cerebras, and SambaNova. Cirrascale's AI Innovation Cloud enables users to test and deploy on leading AI accelerators in one cloud, democratizing AI by delivering high-performance AI compute and scalable deep learning solutions. The platform also offers professional and managed services, tailored multi-GPU server options, and high-throughput storage and networking solutions to accelerate development, training, and inference workloads.
![Cerebium Screenshot](/screenshots/www.cerebrium.ai.jpg)
Cerebium
Cerebium is a serverless AI infrastructure platform that allows teams to build, test, and deploy AI applications quickly and efficiently. With a focus on speed, performance, and cost optimization, Cerebium offers a range of features and tools to simplify the development and deployment of AI projects. The platform ensures high reliability, security, and compliance while providing real-time logging, cost tracking, and observability tools. Cerebium also offers GPU variety and effortless autoscaling to meet the diverse needs of developers and businesses.
![CloudKeeper Screenshot](/screenshots/cloudkeeper.ai.jpg)
CloudKeeper
CloudKeeper is a comprehensive cloud cost optimization partner that offers solutions for AWS, Azure, and GCP. The platform provides services such as rate optimization, usage optimization, cloud consulting & support, and cloud cost visibility. CloudKeeper combines group buying, commitments management, expert consulting, and analytics to reduce cloud costs and maximize value. With a focus on savings, visibility, and services bundled together, CloudKeeper aims to simplify the cloud cost optimization journey for businesses of all sizes.
![Pump Screenshot](/screenshots/pump.co.jpg)
Pump
Pump is a cost-saving AI tool that utilizes group buying and artificial intelligence to help startups save up to 60% on cloud services like AWS and GCP. It offers discounts previously only available to large companies, alongside 24/7 automated savings. Pump promises to slash runaway cloud computing costs by working tirelessly to find and apply the best savings for its users. The tool is trusted by over 1000 startups across 22 countries and has been recognized as the 'Costco of Cloud' by Forbes.
![Mystic.ai Screenshot](/screenshots/mystic.ai.jpg)
Mystic.ai
Mystic.ai is an AI tool designed to deploy and scale Machine Learning models with ease. It offers a fully managed Kubernetes platform that runs in your own cloud, allowing users to deploy ML models in their own Azure/AWS/GCP account or in a shared GPU cluster. Mystic.ai provides cost optimizations, fast inference, simpler developer experience, and performance optimizations to ensure high-performance AI model serving. With features like pay-as-you-go API, cloud integration with AWS/Azure/GCP, and a beautiful dashboard, Mystic.ai simplifies the deployment and management of ML models for data scientists and AI engineers.
![Teraflow.ai Screenshot](/screenshots/teraflow.ai.jpg)
Teraflow.ai
Teraflow.ai is an AI-enablement company that specializes in helping businesses adopt and scale their artificial intelligence models. They offer services in data engineering, ML engineering, AI/UX, and cloud architecture. Teraflow.ai assists clients in fixing data issues, boosting ML model performance, and integrating AI into legacy customer journeys. Their team of experts deploys solutions quickly and efficiently, using modern practices and hyper scaler technology. The company focuses on making AI work by providing fixed pricing solutions, building team capabilities, and utilizing agile-scrum structures for innovation. Teraflow.ai also offers certifications in GCP and AWS, and partners with leading tech companies like HashiCorp, AWS, and Microsoft Azure.
![PrimeOrbit Screenshot](/screenshots/primeorbit.ai.jpg)
PrimeOrbit
PrimeOrbit is an AI-driven cloud cost optimization platform designed to empower operations and boost ROI for enterprises. The platform focuses on streamlining operations and simplifying cost management by delivering quality-centric solutions. It offers AI-driven optimization recommendations, automated cost allocation, and tailored FinOps for optimal efficiency and control. PrimeOrbit stands out by providing user-centric approach, superior AI recommendations, customization, and flexible enterprise workflow. It supports major cloud providers including AWS, Azure, and GCP, with full support for GCP and Kubernetes coming soon. The platform ensures complete cost allocation across cloud resources, empowering decision-makers to optimize cloud spending efficiently and effectively.
![LogicMonitor Screenshot](/screenshots/logicmonitor.com.jpg)
LogicMonitor
LogicMonitor is a cloud-based infrastructure monitoring platform that provides real-time insights and automation for comprehensive, seamless monitoring with agentless architecture. It offers a wide range of features including infrastructure monitoring, network monitoring, server monitoring, remote monitoring, virtual machine monitoring, SD-WAN monitoring, database monitoring, storage monitoring, configuration monitoring, cloud monitoring, container monitoring, AWS Monitoring, GCP Monitoring, Azure Monitoring, digital experience SaaS monitoring, website monitoring, APM, AIOPS, Dexda Integrations, security dashboards, and platform demo logs. LogicMonitor's AI-driven hybrid observability helps organizations simplify complex IT ecosystems, accelerate incident response, and thrive in the digital landscape.
![integrate.ai Screenshot](/screenshots/integrate.ai.jpg)
integrate.ai
integrate.ai is a platform that enables data and analytics providers to collaborate easily with enterprise data science teams without moving data. Powered by federated learning technology, the platform allows for efficient proof of concepts, data experimentation, infrastructure agnostic evaluations, collaborative data evaluations, and data governance controls. It supports various data science jobs such as match rate analysis, exploratory data analysis, correlation analysis, model performance analysis, feature importance & data influence, and model validation. The platform integrates with popular data science tools like Azure, Jupyter, Databricks, AWS, GCP, Snowflake, Pandas, PyTorch, MLflow, and scikit-learn.
20 - Open Source Tools
![llmops-duke-aipi Screenshot](/screenshots_githubs/alfredodeza-llmops-duke-aipi.jpg)
llmops-duke-aipi
LLMOps Duke AIPI is a course focused on operationalizing Large Language Models, teaching methodologies for developing applications using software development best practices with large language models. The course covers various topics such as generative AI concepts, setting up development environments, interacting with large language models, using local large language models, applied solutions with LLMs, extensibility using plugins and functions, retrieval augmented generation, introduction to Python web frameworks for APIs, DevOps principles, deploying machine learning APIs, LLM platforms, and final presentations. Students will learn to build, share, and present portfolios using Github, YouTube, and Linkedin, as well as develop non-linear life-long learning skills. Prerequisites include basic Linux and programming skills, with coursework available in Python or Rust. Additional resources and references are provided for further learning and exploration.
![Upsonic Screenshot](/screenshots_githubs/Upsonic-Upsonic.jpg)
Upsonic
Upsonic offers a cutting-edge enterprise-ready framework for orchestrating LLM calls, agents, and computer use to complete tasks cost-effectively. It provides reliable systems, scalability, and a task-oriented structure for real-world cases. Key features include production-ready scalability, task-centric design, MCP server support, tool-calling server, computer use integration, and easy addition of custom tools. The framework supports client-server architecture and allows seamless deployment on AWS, GCP, or locally using Docker.
![google-cloud-gcp-openai-api Screenshot](/screenshots_githubs/Cyclenerd-google-cloud-gcp-openai-api.jpg)
google-cloud-gcp-openai-api
This project provides a drop-in replacement REST API for Google Cloud Vertex AI (PaLM 2, Codey, Gemini) that is compatible with the OpenAI API specifications. It aims to make Google Cloud Platform Vertex AI more accessible by translating OpenAI API calls to Vertex AI. The software is developed in Python and based on FastAPI and LangChain, designed to be simple and customizable for individual needs. It includes step-by-step guides for deployment, supports various OpenAI API services, and offers configuration through environment variables. Additionally, it provides examples for running locally and usage instructions consistent with the OpenAI API format.
![TokenFormer Screenshot](/screenshots_githubs/Haiyang-W-TokenFormer.jpg)
TokenFormer
TokenFormer is a fully attention-based neural network architecture that leverages tokenized model parameters to enhance architectural flexibility. It aims to maximize the flexibility of neural networks by unifying token-token and token-parameter interactions through the attention mechanism. The architecture allows for incremental model scaling and has shown promising results in language modeling and visual modeling tasks. The codebase is clean, concise, easily readable, state-of-the-art, and relies on minimal dependencies.
![cb-tumblebug Screenshot](/screenshots_githubs/cloud-barista-cb-tumblebug.jpg)
cb-tumblebug
CB-Tumblebug (CB-TB) is a system for managing multi-cloud infrastructure consisting of resources from multiple cloud service providers. It provides an overview, features, and architecture. The tool supports various cloud providers and resource types, with ongoing development and localization efforts. Users can deploy a multi-cloud infra with GPUs, enjoy multiple LLMs in parallel, and utilize LLM-related scripts. The tool requires Linux, Docker, Docker Compose, and Golang for building the source. Users can run CB-TB with Docker Compose or from the Makefile, set up prerequisites, contribute to the project, and view a list of contributors. The tool is licensed under an open-source license.
![ethereum-etl-airflow Screenshot](/screenshots_githubs/blockchain-etl-ethereum-etl-airflow.jpg)
ethereum-etl-airflow
This repository contains Airflow DAGs for extracting, transforming, and loading (ETL) data from the Ethereum blockchain into BigQuery. The DAGs use the Google Cloud Platform (GCP) services, including BigQuery, Cloud Storage, and Cloud Composer, to automate the ETL process. The repository also includes scripts for setting up the GCP environment and running the DAGs locally.
![awesome-transformer-nlp Screenshot](/screenshots_githubs/cedrickchee-awesome-transformer-nlp.jpg)
awesome-transformer-nlp
This repository contains a hand-curated list of great machine (deep) learning resources for Natural Language Processing (NLP) with a focus on Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), attention mechanism, Transformer architectures/networks, Chatbot, and transfer learning in NLP.
![airflow-diagrams Screenshot](/screenshots_githubs/feluelle-airflow-diagrams.jpg)
airflow-diagrams
Auto-generated Diagrams from Airflow DAGs. This project aims to easily visualize Airflow DAGs on a service level from providers like AWS, GCP, Azure, etc. via diagrams. It connects to your Airflow installation to retrieve all DAGs and tasks, processes them using Fuzzy String Matching, and renders the results into a Python file for diagram generation. Contributions are welcome.
![infra Screenshot](/screenshots_githubs/e2b-dev-infra.jpg)
infra
E2B Infra is a cloud runtime for AI agents. It provides SDKs and CLI to customize and manage environments and run AI agents in the cloud. The infrastructure is deployed using Terraform and is currently only deployable on GCP. The main components of the infrastructure are the API server, daemon running inside instances (sandboxes), Nomad driver for managing instances (sandboxes), and Nomad driver for building environments (templates).
![kaytu Screenshot](/screenshots_githubs/kaytu-io-kaytu.jpg)
kaytu
Kaytu is an AI platform that enhances cloud efficiency by analyzing historical usage data and providing intelligent recommendations for optimizing instance sizes. Users can pay for only what they need without compromising the performance of their applications. The platform is easy to use with a one-line command, allows customization for specific requirements, and ensures security by extracting metrics from the client side. Kaytu is open-source and supports AWS services, with plans to expand to GCP, Azure, GPU optimization, and observability data from Prometheus in the future.
![extension-gen-ai Screenshot](/screenshots_githubs/looker-open-source-extension-gen-ai.jpg)
extension-gen-ai
The Looker GenAI Extension provides code examples and resources for building a Looker Extension that integrates with Vertex AI Large Language Models (LLMs). Users can leverage the power of LLMs to enhance data exploration and analysis within Looker. The extension offers generative explore functionality to ask natural language questions about data and generative insights on dashboards to analyze data by asking questions. It leverages components like BQML Remote Models, BQML Remote UDF with Vertex AI, and Custom Fine Tune Model for different integration options. Deployment involves setting up infrastructure with Terraform and deploying the Looker Extension by creating a Looker project, copying extension files, configuring BigQuery connection, connecting to Git, and testing the extension. Users can save example prompts and configure user settings for the extension. Development of the Looker Extension environment includes installing dependencies, starting the development server, and building for production.
![kubeai Screenshot](/screenshots_githubs/substratusai-kubeai.jpg)
kubeai
KubeAI is a highly scalable AI platform that runs on Kubernetes, serving as a drop-in replacement for OpenAI with API compatibility. It can operate OSS model servers like vLLM and Ollama, with zero dependencies and additional OSS addons included. Users can configure models via Kubernetes Custom Resources and interact with models through a chat UI. KubeAI supports serving various models like Llama v3.1, Gemma2, and Qwen2, and has plans for model caching, LoRA finetuning, and image generation.
![testzeus-hercules Screenshot](/screenshots_githubs/test-zeus-ai-testzeus-hercules.jpg)
testzeus-hercules
Hercules is the world’s first open-source testing agent designed to handle the toughest testing tasks for modern web applications. It turns simple Gherkin steps into fully automated end-to-end tests, making testing simple, reliable, and efficient. Hercules adapts to various platforms like Salesforce and is suitable for CI/CD pipelines. It aims to democratize and disrupt test automation, making top-tier testing accessible to everyone. The tool is transparent, reliable, and community-driven, empowering teams to deliver better software. Hercules offers multiple ways to get started, including using PyPI package, Docker, or building and running from source code. It supports various AI models, provides detailed installation and usage instructions, and integrates with Nuclei for security testing and WCAG for accessibility testing. The tool is production-ready, open core, and open source, with plans for enhanced LLM support, advanced tooling, improved DOM distillation, community contributions, extensive documentation, and a bounty program.
![terraform-genai-doc-summarization Screenshot](/screenshots_githubs/GoogleCloudPlatform-terraform-genai-doc-summarization.jpg)
terraform-genai-doc-summarization
This solution showcases how to summarize a large corpus of documents using Generative AI. It provides an end-to-end demonstration of document summarization going all the way from raw documents, detecting text in the documents and summarizing the documents on-demand using Vertex AI LLM APIs, Cloud Vision Optical Character Recognition (OCR) and BigQuery.
![palico-ai Screenshot](/screenshots_githubs/palico-ai-palico-ai.jpg)
palico-ai
Palico AI is a tech stack designed for rapid iteration of LLM applications. It allows users to preview changes instantly, improve performance through experiments, debug issues with logs and tracing, deploy applications behind a REST API, and manage applications with a UI control panel. Users have complete flexibility in building their applications with Palico, integrating with various tools and libraries. The tool enables users to swap models, prompts, and logic easily using AppConfig. It also facilitates performance improvement through experiments and provides options for deploying applications to cloud providers or using managed hosting. Contributions to the project are welcomed, with easy ways to get involved by picking issues labeled as 'good first issue'.
![CoolCline Screenshot](/screenshots_githubs/coolcline-CoolCline.jpg)
CoolCline
CoolCline is a proactive programming assistant that combines the best features of Cline, Roo Code, and Bao Cline. It seamlessly collaborates with your command line interface and editor, providing the most powerful AI development experience. It optimizes queries, allows quick switching of LLM Providers, and offers auto-approve options for actions. Users can configure LLM Providers, select different chat modes, perform file and editor operations, integrate with the command line, automate browser tasks, and extend capabilities through the Model Context Protocol (MCP). Context mentions help provide explicit context, and installation is easy through the editor's extension panel or by dragging and dropping the `.vsix` file. Local setup and development instructions are available for contributors.
![tidb Screenshot](/screenshots_githubs/pingcap-tidb.jpg)
tidb
TiDB is an open-source distributed SQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads. It is MySQL compatible and features horizontal scalability, strong consistency, and high availability.
![inbox-zero Screenshot](/screenshots_githubs/elie222-inbox-zero.jpg)
inbox-zero
Inbox Zero is an open-source email app that helps you reach inbox zero fast with AI assistance. It offers various features such as a newsletter cleaner, AI assistant for auto-responding, archiving, labeling, and forwarding emails, a cold email blocker, email analytics, tracking of new senders and unreplied emails, and a large email finder to free up space. Inbox Zero is built with Next.js, Tailwind CSS, Prisma, Tinybird, Upstash, and Turbo.
![metavoice-src Screenshot](/screenshots_githubs/metavoiceio-metavoice-src.jpg)
metavoice-src
MetaVoice-1B is a 1.2B parameter base model trained on 100K hours of speech for TTS (text-to-speech). It has been built with the following priorities: * Emotional speech rhythm and tone in English. * Zero-shot cloning for American & British voices, with 30s reference audio. * Support for (cross-lingual) voice cloning with finetuning. * We have had success with as little as 1 minute training data for Indian speakers. * Synthesis of arbitrary length text
10 - OpenAI Gpts
![cloud exams coach Screenshot](/screenshots_gpts/g-iaOhuiS4t.jpg)
cloud exams coach
AI Cloud Computing (Engineering, Architecture, DevOps ) Certifications Coach for AWS, GCP, and Azure. I provide timed mock exams.
![Cloud Price Screenshot](/screenshots_gpts/g-CE15AfMSL.jpg)
Cloud Price
Your up-to-date GCP, AWS and Azure pricing expert with the latest virtual machines details.
![🌟Technical diagrams pro🌟 Screenshot](/screenshots_gpts/g-D61xRXJME.jpg)
🌟Technical diagrams pro🌟
Create UML for flowcharts, Class, Sequence, Use Case, and Activity diagrams using PlantUML. System design and cloud infrastructure diagrams for AWS, Azue and GCP. No login required.
![GCP-BigQueryGPT Screenshot](/screenshots_gpts/g-lcgUwgq64.jpg)
GCP-BigQueryGPT
BigQueryGPT aids in mastering BigQuery SQL with concise, practical examples. Tailored for all skill levels, it simplifies complex queries, offering clear explanations and optimized solutions for efficient learning and query troubleshooting.
![Instructor GCP ML Screenshot](/screenshots_gpts/g-ToivyV7Ht.jpg)
Instructor GCP ML
Formador para la certificación de ML Engineer en GCP, con respuestas y explicaciones detalladas.