Base AI vs. Teammately Comparison Chart

RunPod offers a cloud-based platform designed for running AI workloads, focusing on providing scalable, on-demand GPU resources to accelerate machine learning (ML) model training and inference. With its diverse selection of powerful GPUs like the NVIDIA A100, RTX 3090, and H100, RunPod supports a wide range of AI applications, from deep learning to data processing. The platform is designed to minimize startup time, providing near-instant access to GPU pods, and ensures scalability with autoscaling capabilities for real-time AI model deployment. RunPod also offers serverless functionality, job queuing, and real-time analytics, making it an ideal solution for businesses needing flexible, cost-effective GPU resources without the hassle of managing infrastructure.

Apify is a full-stack web scraping and automation platform helping anyone get value from the web. At its core is Apify Store, a marketplace with over 10,000 Actors where developers build, publish, and monetize automation tools. Actors are serverless cloud programs that extract data, automate web tasks, and run AI agents. Developers build them using JavaScript, Python, or Crawlee, Apify's open-source library. Build once, publish to Store, and earn when others use it. Thousands of developers do this - Apify handles infrastructure, billing, and monthly payouts. Apify Store has ready-made Actors for scraping Amazon, Google Maps, social media, tracking prices, lead-gen, and more. Actors handle proxies, CAPTCHAs, JavaScript rendering, headless browsers, and scaling. Everything runs on Apify's cloud with 99.95% uptime. SOC2, GDPR, and CCPA compliant. Integrate with Zapier, Make, n8n, and LangChain. Apify's MCP server lets AI like Claude dynamically discover and use Actors

Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case. Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc, and Spark. You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI Workbench and run your models from there. Use Vertex Data Labeling to generate highly accurate labels for your data collection. Vertex AI Agent Builder enables developers to create and deploy enterprise-grade generative AI applications. It offers both no-code and code-first approaches, allowing users to build AI agents using natural language instructions or by leveraging frameworks like LangChain and LlamaIndex.

StackAI is an enterprise AI automation platform to build end-to-end internal tools and processes with AI agents in a fully compliant and secure way. Designed for large organizations, it enables teams to automate complex workflows across operations, compliance, finance, IT, and support without heavy engineering. With StackAI you can: • Connect knowledge bases (SharePoint, Confluence, Notion, Google Drive, databases) with versioning, citations, and access controls. • Deploy AI agents as chat assistants, advanced forms, or APIs integrated into Slack, Teams, Salesforce, HubSpot, or ServiceNow. • Govern usage with enterprise security: SSO (Okta, Azure AD, Google), RBAC, audit logs, PII masking, data residency, and cost controls. • Route across OpenAI, Anthropic, Google, or local LLMs with guardrails, evaluations, and testing. • Start fast with templates for Contract Analyzer, Support Desk, RFP Response, Investment Memo Generator, and more.

BigQuery is a serverless, multicloud data warehouse that simplifies the process of working with all types of data so you can focus on getting valuable business insights quickly. At the core of Google’s data cloud, BigQuery allows you to simplify data integration, cost effectively and securely scale analytics, share rich data experiences with built-in business intelligence, and train and deploy ML models with a simple SQL interface, helping to make your organization’s operations more data-driven. Gemini in BigQuery offers AI-driven tools for assistance and collaboration, such as code suggestions, visual data preparation, and smart recommendations designed to boost efficiency and reduce costs. BigQuery delivers an integrated platform featuring SQL, a notebook, and a natural language-based canvas interface, catering to data professionals with varying coding expertise. This unified workspace streamlines the entire analytics process.

Dragonfly is a drop-in Redis replacement that cuts costs and boosts performance. Designed to fully utilize the power of modern cloud hardware and deliver on the data demands of modern applications, Dragonfly frees developers from the limits of traditional in-memory data stores. The power of modern cloud hardware can never be realized with legacy software. Dragonfly is optimized for modern cloud computing, delivering 25x more throughput and 12x lower snapshotting latency when compared to legacy in-memory data stores like Redis, making it easy to deliver the real-time experience your customers expect. Scaling Redis workloads is expensive due to their inefficient, single-threaded model. Dragonfly is far more compute and memory efficient, resulting in up to 80% lower infrastructure costs. Dragonfly scales vertically first, only requiring clustering at an extremely high scale. This results in a far simpler operational model and a more reliable system.

Google AI Studio is a comprehensive, web-based development environment that democratizes access to Google's cutting-edge AI models, notably the Gemini family, enabling a broad spectrum of users to explore and build innovative applications. This platform facilitates rapid prototyping by providing an intuitive interface for prompt engineering, allowing developers to meticulously craft and refine their interactions with AI. Beyond basic experimentation, AI Studio supports the seamless integration of AI capabilities into diverse projects, from simple chatbots to complex data analysis tools. Users can rigorously test different prompts, observe model behaviors, and iteratively refine their AI-driven solutions within a collaborative and user-friendly environment. This empowers developers to push the boundaries of AI application development, fostering creativity and accelerating the realization of AI-powered solutions.

Compute Engine is Google's infrastructure as a service (IaaS) platform for organizations to create and run cloud-based virtual machines. Computing infrastructure in predefined or custom machine sizes to accelerate your cloud transformation. General purpose (E2, N1, N2, N2D) machines provide a good balance of price and performance. Compute optimized (C2) machines offer high-end vCPU performance for compute-intensive workloads. Memory optimized (M2) machines offer the highest memory and are great for in-memory databases. Accelerator optimized (A2) machines are based on the A100 GPU, for very demanding applications. Integrate Compute with other Google Cloud services such as AI/ML and data analytics. Make reservations to help ensure your applications have the capacity they need as they scale. Save money just for running Compute with sustained-use discounts, and achieve greater savings when you use committed-use discounts.

Data Collection and Labeling for AI Innovation. Transform your AI development with our decentralized platform that connects you to worldwide data contributors. We combine global crowdsourcing with blockchain verification to deliver diverse, traceable datasets. Global Network: Ensure AI models are trained on data that reflects diverse perspectives, reducing bias, and enhancing inclusivity. Distributed and Transparent: Every piece of data is timestamped for provenance stored securely stored in the OORT cloud , and verified for integrity, creating a trustless ecosystem. Ethical and Responsible AI Development: Ensure contributors retain autonomy with data ownership while making their data available for AI innovation in a transparent, fair, and secure environment Quality Assured: Human verification ensures data meets rigorous standards Access diverse data at scale. Verify data integrity. Get human-validated datasets for AI. Reduce costs while maintaining quality. Scale globally.

RaimaDB is an embedded time series database for IoT and Edge devices that can run in-memory. It is an extremely powerful, lightweight and secure RDBMS. Field tested by over 20 000 developers worldwide and has more than 25 000 000 deployments. RaimaDB is a high-performance, cross-platform embedded database designed for mission-critical applications, particularly in the Internet of Things (IoT) and edge computing markets. It offers a small footprint, making it suitable for resource-constrained environments, and supports both in-memory and persistent storage configurations. RaimaDB provides developers with multiple data modeling options, including traditional relational models and direct relationships through network model sets. It ensures data integrity with ACID-compliant transactions and supports various indexing methods such as B+Tree, Hash Table, R-Tree, and AVL-Tree.