Below are the most powerful and widely used AI tools and platforms that businesses relied on in 2024, grouped by purpose with a short note on why each mattered. I’ve cited authoritative sources for the major platforms so you can read the vendor details or announcements.

Core large-model providers / enterprise LLM products

• OpenAI — ChatGPT Enterprise & GPT‑4o family: widely adopted for enterprise chat, document analysis, embedding/search, and APIs (ChatGPT Enterprise adds admin controls, data/privacy guarantees and large context windows). GPT‑4o was OpenAI’s natively multimodal “omni” model in 2024. (openai.com)
• Anthropic — Claude (Claude Enterprise / Claude 3 / 3.5 Haiku): strong enterprise feature set (large context windows, GitHub and data integrations, enterprise controls) and cost/performance-optimized variants on cloud marketplaces (e.g., Bedrock/Vertex). (anthropic.com)
• Google Cloud — Gemini models via Vertex AI: Google pushed Gemini (2.x and updates) into Vertex AI for enterprises, focusing on grounding, agents, and production tooling on Google Cloud. (cloud.google.com)
• Meta, Mistral, Cohere, and other model vendors (via cloud marketplaces): many enterprises used Meta’s Llama models, Mistral, Cohere, and others either directly or through cloud platforms for private/custom models (often via Snowflake, AWS Bedrock, or Hugging Face). (investors.snowflake.com)

Cloud platforms & managed foundation-model services

• AWS Amazon Bedrock: a managed platform offering multiple foundation models (Anthropic, Cohere, etc.), model customization, guardrails and multi-agent capabilities for production GenAI. (press.aboutamazon.com)
• Google Vertex AI: production tools for deploying Gemini-based models, evaluation, model tuning and agent/ops integrations on Google Cloud. (cloud.google.com)
• Microsoft Azure (Azure OpenAI + Microsoft 365 Copilot): Azure provided managed OpenAI access plus deep integration into Microsoft 365 via Copilot (business copilots, Semantic Index, Copilot Studio). (microsoft.com)
• Snowflake Cortex AI / Snowflake ML: Snowflake added Cortex AI and serverless fine-tuning / model access, making it simple to run LLMs close to enterprise data and handle governance. (investors.snowflake.com)

Data + model ops / lakehouse / MLOps platforms

• Databricks Lakehouse (Lakehouse AI / LLMops): unified data + model lifecycle for building and serving LLM/GenAI apps, vector search, model serving and monitoring (LLMOps). (databricks.com)
• DataRobot: enterprise AutoML + GenAI app-building suite focused on governable, composable AI apps and observability for production deployments. (datarobot.com)
• H2O.ai (h2oGPT, Driverless AI): AutoML and GenAI tooling, including Snowflake-native apps and LLM tooling for enterprises. (h2o.ai)

Open-source / model-hub & developer tools

• Hugging Face (Hub, Inference Providers, Inference-as-a-Service): the primary open-source model hub + managed inference and deployment options for teams wanting open models and vendor-flexible infrastructure. (huggingface.co)

Automation, integration & RPA (business process acceleration)

• UiPath (RPA + AI Center): RPA integrated with AI capabilities to automate end-to-end business processes and embed ML models into workflows. (ir.uipath.com)
• Zapier (AI by Zapier): no-code automation with built-in AI steps (GPT-4o-mini in 2024) for SMEs to automate workflows without separate model infra. (help.zapier.com)
• Workato, Make, and similar integration platforms also expanded GenAI integrations in 2024 (useful for workflow automation at scale).

Specialized enterprise AI stacks & hardware

• NVIDIA (NeMo, NIM, DGX Cloud tooling): key for on-prem / accelerated inference and training, and for enterprises that needed optimized inference and fine-tuning infrastructure (often bundled with other platforms). (infoq.com)

Why these were “most powerful” for businesses in 2024

• Production readiness and enterprise controls: the top tools combined cutting-edge model capabilities with enterprise-grade security, compliance and admin controls (SSO, DLP, SOC2, data residency). (See OpenAI/ChatGPT Enterprise and Anthropic Claude Enterprise announcements.) (openai.com)
• Multi-model/access flexibility: cloud platforms (Bedrock, Vertex, Azure) let companies pick models and vendor ecosystems that fit cost, latency and policy needs. (press.aboutamazon.com)
• Data-first stacks: Snowflake, Databricks and similar platforms made it easy to keep models close to enterprise data (governance, fine-tuning, vector search), reducing data movement and improving ROI. (investors.snowflake.com)
• Automation + agents: RPA + agent frameworks and no-code automation linked LLMs to real systems (email, ERPs, ticketing), turning AI from a research toy into actual operational efficiency gains (UiPath, Zapier, Copilot). (ir.uipath.com)

Quick recommendations (practical)

• SMBs / startups: start with ChatGPT Team/Enterprise or managed offerings (Copilot Pro/Zapier AI) for immediate productivity gains and minimal infra. (openai.com)
• Mid-market: use cloud-managed LLMs (Vertex AI / Amazon Bedrock / Azure OpenAI) to run private workloads, connect to Snowflake/Databricks for data, and add automation with Zapier/UiPath. (press.aboutamazon.com)
• Regulated enterprises: prefer vendors that explicitly offer no-training-on-customer-data, SOC2/industry compliance and strong admin/DLP tooling (ChatGPT Enterprise, Claude Enterprise, Bedrock integrations). (openai.com)

If you want, I can:

• Recommend the best 2–3 specific products for your company size/industry and an implementation path (pilot → scale), or
• Compare two tools you’re choosing between (e.g., ChatGPT Enterprise vs Claude Enterprise vs Vertex on a given use case), with cost/technical tradeoffs and an integration checklist.

Which would you prefer?