Custom GPTs launched in late 2023 and became one of ChatGPT's most-used features. The concept: take ChatGPT's base capability and customize it for a specific purpose — upload your company's knowledge base, write custom instructions that define the GPT's role and behavior, enable tools like web browsing, DALL-E image generation, and Code Interpreter, and optionally connect to external APIs through Actions. The result is a specialized AI assistant that your team (or the public, via the GPT Store) interacts with through conversation.

The power of Custom GPTs is accessibility. Anyone with ChatGPT Plus ($20/month) or ChatGPT Team ($25/user/month) can create a Custom GPT in minutes. No code. No deployment. No infrastructure. Write instructions like 'You are a customer support agent for [Company]. Use the uploaded product manual to answer questions. Be concise and friendly. If you do not know the answer, say so.' Upload the manual. The GPT works immediately.

The GPT Store provides distribution. Share your Custom GPT publicly and others discover and use it. GPTs for marketing, coding, research, writing, education, and hundreds of other categories are available. This marketplace turns AI expertise into distributable products.

TinyAgents is a fundamentally different kind of AI. Instead of a conversational assistant you chat with, it is an AI engine that processes your business data automatically. A form submission triggers AI scoring. A database record gets AI enrichment. An email gets AI-drafted content. A workflow uses AI for decision-making. No conversation needed. The AI works on your data silently, connected to forms, databases, workflows, and email through one platform.

Custom GPTs are AI you talk to. TinyAgents is AI that works for you. Both use large language models. They apply them to completely different interaction patterns.

Conversational AI assistant vs operational AI engine

Custom GPTs excel at tasks that benefit from conversation — answering questions, brainstorming, drafting content, analyzing documents, explaining concepts. Upload your company's product documentation and the GPT answers customer questions. Upload your brand guidelines and the GPT generates on-brand marketing copy. Upload your codebase documentation and the GPT helps developers debug. The conversational interface makes complex AI accessible to anyone who can type a question.

The knowledge file upload (up to 20 files per GPT) grounds responses in your specific content. Instead of GPT-4's general training data, the Custom GPT references your uploaded documents first. This reduces hallucination for domain-specific questions — the AI pulls from your actual documentation rather than making things up from general knowledge.

Actions connect Custom GPTs to external APIs — fetch data from your CRM, update records in your database, trigger webhooks, or query any REST API. This bridges the gap between conversation and action. A sales GPT that checks your CRM for account status, a support GPT that creates tickets in your help desk, a HR GPT that looks up employee information — Actions make GPTs actionable, not just conversational.

Web browsing searches the internet for current information. Code Interpreter runs Python code for data analysis, chart generation, and calculations. DALL-E generates images. These built-in tools make Custom GPTs genuinely capable for a wide range of tasks without any additional tooling.

But Custom GPTs are interactive, not automated. Someone must start a conversation for the GPT to work. There is no trigger that automatically runs the GPT when a form is submitted, a database record changes, or a workflow event fires. You cannot schedule a Custom GPT to score 100 leads at 2am. You cannot connect it to an email tool that sends AI-drafted outreach to every new contact. The GPT waits for a human to type a message. It does not run on its own.

Custom GPTs also lock you into OpenAI's models. GPT-4o, GPT-4, GPT-3.5 — all OpenAI. You cannot use Claude for nuanced writing, Gemini for multimodal analysis, or Llama for cost-effective tasks. The model choice is OpenAI, exclusively.

The per-user pricing means every person who needs access pays $20-$25/month. A 10-person team on ChatGPT Team pays $250/month — for conversational AI only. No forms. No database. No workflow automation. No email campaigns. Just the chat interface and whatever Custom GPTs you build within it.

TinyAgents eliminates the conversation requirement entirely. AI runs automatically as part of business workflows. A TinyForms submission triggers TinyAgents to score the lead using Claude, GPT-4, or Gemini (7 providers, your choice per step). The score is stored in TinyTables as an AI-generated column. TinyWorkflows routes the lead based on the score. TinyEmails sends an AI-drafted follow-up personalized from database context. This entire pipeline runs without any human conversation. No one types a message. No one waits for a response. The AI processes data as it flows through the system.

Seven LLM providers (Claude, GPT-4, Gemini, and 4 others) with per-step model selection. Use Claude for email drafting (it writes more naturally), GPT-4 for structured data extraction (it follows formats precisely), and Gemini for multimodal tasks (it handles images). This multi-model flexibility produces better results than any single-model system because each task gets optimized for the best model.

TinyCommand at $19/month flat for 3 users includes AI agents plus forms, database, workflows, and email. ChatGPT Team at $25/user for 3 users costs $75/month — for conversational AI only. Same AI capability. Different deployment model. Different pricing economics.

This comparison also applies to

• Teams comparing TinyAgents with Claude Projects (Anthropic's custom AI workspaces)
• Teams comparing TinyAgents with Google Gems (Gemini's custom AI)
• Teams comparing TinyAgents with Microsoft Copilot (AI assistant in Office)
• Companies deciding between conversational AI assistants and automated AI operations
• Teams that use both — Custom GPTs for thinking, TinyAgents for executing