Best Custom GPT Alternative: Build & Sell AI (2026)

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If you're searching for a Custom GPT alternative, you're probably not asking "what's another chatbot builder?"

You're asking something deeper. Something like:

How do I deploy my AI assistant outside of ChatGPT?

How do I charge people for access without begging OpenAI for an invite?

How do I get real analytics instead of vague "10+ chats" metrics?

How do I protect my prompts and IP from being copied?

How do I build something I actually own?

This guide is designed to be the single best resource for making that decision in 2026. Especially if you're building something you intend to ship, sell, or scale.

We built Agent37 to solve exactly this problem. So yes, we have opinions. But we'll also show you the full landscape, including when a Custom GPT alternative isn't what you need at all.

What Are Custom GPTs and How Much Do They Cost?

Let's start with the basics so we're speaking the same language.

OpenAI's "GPTs" (often called Custom GPTs) are custom versions of ChatGPT you can build by combining:

Instructions that define behavior and constraints

Knowledge from uploaded reference files

Capabilities like web search, image generation, and data analysis

Custom actions that call external APIs through OpenAPI schemas

They're genuinely useful for personal productivity. Build one in an afternoon, share it with your team, move on. The appeal is real.

What It Costs to Use Custom GPTs

You can't build Custom GPTs on ChatGPT's free tier. According to OpenAI, creating GPTs requires a paid subscription:

Plan	Price	What You Get
ChatGPT Plus	$20/month	Standard access, GPT creation
ChatGPT Pro	$200/month	"Unlimited" access with guardrails
ChatGPT Business	$25/user/month	Team features, enterprise controls

Over 3 million Custom GPTs were created within the first two months of OpenAI launching the GPT Store. People clearly found value in the concept.

But then something happened. Those same builders started asking: "Wait, how do I actually make money from this?"

And that's where things get complicated.

Why Custom GPTs Fail as Sellable Products

Custom GPTs are a brilliant "build fast" tool. But the moment you try to turn one into a product, a workflow engine, or a revenue stream, you hit walls.

Think about building an AI product in five layers:

Layer	What It Does	Custom GPT Provides?
1. The Brain	The underlying model (GPT-4/5, Claude, etc.)	Yes
2. The Behavior	Your custom instructions and knowledge	Yes
3. The Tools	Code execution, web calls, APIs	Partially
4. The Runtime	Where users interact, usage limits, auth	OpenAI controls this
5. The Business	Billing, paywalls, analytics, support	Barely exists

A Custom GPT covers layers 1 and 2 nicely. It offers some tools (code interpreter, web browse) within ChatGPT's sandbox. But you don't control the runtime or business layers.

That's the core problem. Let's break down what that actually means.

Can You Monetize Custom GPTs? (OpenAI Invite-Only)

OpenAI announced a GPT builder revenue program with the GPT Store. It sounded promising. Builders would earn based on user engagement.

But here's what actually happened: OpenAI's monetization FAQ currently states they're testing monetization with a small group of US-based builders and are not accepting additional builders.

That FAQ was last updated about two months ago. Nothing has changed.

WIRED reported on how GPT Store builders have struggled with unclear monetization, limited analytics, and US-centric revenue sharing. Many felt left behind.

Can You Embed Custom GPTs on Your Website?

This one's a deal-breaker for most real use cases. OpenAI explicitly states that GPTs can only be accessed on chatgpt.com and cannot be integrated with other websites.

No embed code. No widget. No script to drop into WordPress.

If you wanted to offer your AI assistant on your own domain with your branding, you're out of luck. OpenAI's recommendation? Use their Assistants API and build your own app from scratch. Which is a significant development effort.

Can You See What Users Ask Your Custom GPT?

When someone uses your GPT through ChatGPT, what are they struggling with? Which answers work and which don't? Where do they get confused?

You'll never know.

OpenAI's policy is clear: "Creators of GPTs cannot access user conversations with their GPTs."

Good for end-user privacy. Terrible for you as a product owner. You can't identify your power users, troubleshoot failures, or improve based on real feedback. You're flying blind.

Do Custom GPT Users Need a ChatGPT Subscription?

Here's one people often overlook: in order for someone to even use your Custom GPT, they need a ChatGPT account. Free users can try with message limits, but for unrestricted use they'll likely need ChatGPT Plus ($20/month).

So your pitch becomes: "Go sign up for ChatGPT, pay $20/month, then find my GPT."

You're driving users to OpenAI's platform, not yours. And if your target users are non-technical, that extra friction is a real barrier.

Will Custom GPTs Change Without Your Permission?

OpenAI explicitly warns that GPTs will be automatically migrated to their closest GPT-5 equivalent. And their enterprise release notes indicate that ChatGPT will automatically switch to GPT-5.2 in January 2026.

Your carefully tuned prompts? They might behave differently after migration. You won't get a choice.

A Custom GPT is not a stable runtime you control. It's a feature inside a rapidly evolving platform.

Is Your Custom GPT Prompt Protected?

Two related issues matter here:

OpenAI notes that content from uploaded "Knowledge" files could be included in the output.

Research has documented instruction leakage and prompt extraction risks in custom assistants, especially when adversarial users probe for hidden instructions.

If your GPT embeds proprietary frameworks, prompts, or paid intellectual property, you need a stronger posture than "hope users don't ask the wrong thing."

What Job Are You Hiring an AI to Do?

Not everyone needs to leave Custom GPTs behind. Sometimes they're exactly right.

Here's a framework for deciding what you actually need.

Personal Productivity Assistant

You want fast setup, everything inside your existing workflow, no need to deploy externally, no billing complexity.

Custom GPTs are great here. They're optimized for speed and convenience. If you're just using it yourself or sharing with a few colleagues, you're probably fine.

Team Internal Assistant

You want access control, domain-restricted actions, governance over what employees can use, and business privacy guarantees.

ChatGPT workspaces can restrict GPT sharing and action domains at the workspace level. If your assistant is purely internal and you're already buying seats, this can work.

Customer-Facing AI Assistant You Control

You want a shareable link that doesn't require customers to buy ChatGPT. You want brand control, guardrails, integration with your systems (CRM, scheduling, payments), and measurable outcomes.

This is where Custom GPTs start to struggle. The experience is still "inside ChatGPT," not inside your product.

Sellable AI Product or Monetizable Skill

You want:

Your own pricing

Trials and freemium options

Subscriptions or usage-based billing

Direct customer relationships

Post-launch improvement loops

OpenAI's GPT Store monetization is not generally available, and ChatGPT Pro explicitly restricts reselling access.

If "sell access to my AI" is your core goal, you need a different runtime.

What Makes a True Custom GPT Alternative?

Most "alternatives" you'll find are just other ways to create a chatbot. They don't solve the real problems.

A true Custom GPT alternative needs to address at least one of these:

① Deployment outside ChatGPT (your domain, your link, no ChatGPT requirement)

② Real workflow execution (tools + multi-step orchestration, not just chat)

③ Monetization controls (pricing, trials, payments you own)

④ Observability + iteration (evals, logs, analytics)

⑤ Portability (less vendor lock-in)

If a platform doesn't solve at least two of those, it's not really a "Custom GPT alternative." It's a different UI for prompts.

AI Chatbots vs AI Agents: What's the Difference?

A Custom GPT is usually a chatbot. It's primarily designed to answer.

The more valuable category is AI agents: systems designed to complete workflows.

This distinction matters because most revenue-generating use cases aren't about answering questions. They're about getting outcomes:

Analyze an RFP and extract requirements

Generate a client-ready resume with PDF deliverables

Run a multi-step research process

Scrape, clean, and summarize datasets

Schedule, invoice, and onboard a client

If you want outcomes, you probably need an agent, not just a chatbot.

Custom GPT Alternatives: The Complete 2026 Landscape

By 2026, a variety of tools claim to help you build your own AI assistant. They fall into a few broad categories, each with different strengths.

RAG Chatbot Platforms: Upload Docs, Get Answers

"RAG" stands for Retrieval-Augmented Generation. These platforms let you upload documents, index them, and the chatbot answers questions by retrieving info and citing sources.

Examples include docs-to-bot solutions that advertise things like "ChatGPT trained on your content."

Typical pricing: Often on the higher side, ranging from approximately 500/month for premium plans.

Good for: Q&A and customer support bots that need to accurately reference company content. They shine at answering factual questions with citations.

Not ideal for: Multi-step processes or tool use. These are fundamentally chat over data. If your use case needs the AI to execute tasks, these platforms fall short. They create a fancy FAQ bot, not an autonomous agent.

Visual Workflow Builders: No-Code Dialog Design

These platforms provide a drag-and-drop interface to design conversations or workflows. Many no-code AI platforms fall into this category.

Typical pricing: Many have subscription plans in the $50-150/month range, plus usage costs for the AI model. That adds up.

Good for: Teams that want collaboration and control without coding. Visual builders can be powerful for designing customer support bots, IVR systems, or multi-turn assistants with custom logic.

Watch out for: Complexity and hidden costs. Designing with nodes and flows can get complicated fast. It's easy to end up with spaghetti that's hard to maintain. And combining a platform fee with separate AI usage can lead to unpredictable costs.

Many users find that a simpler prompt-based approach (writing natural language to define behavior) is actually easier than managing a giant flowchart.

Creator Monetization Platforms for AI

A newer category targeting content creators, influencers, and indie hackers who want to spin up mini AI tools for their audience. They often allow quick creation plus an embed code or shareable link, sometimes gating usage via credits or membership.

Pricing model: Often credit-based or freemium. A platform might let you publish an AI, users get X free credits, then have to buy more. You earn a portion of sales.

Good for: Quickly packaging an idea and sharing with an audience. If you're a creator with a following, these make it easy to offer an AI companion to your content.

Watch out for: Platform constraints and longevity. Many are startups. Ensure you trust they'll stick around. Some may not allow much customization in behavior. And if it's credit-based, make sure the math works (do you earn enough per user to cover costs?).

Where Does Agent37 Fit in the Landscape?

Agent37 sits in a different category. We're not a generic chatbot builder or a visual flow tool.

We specifically built a Claude Skill runtime with monetization for those who have powerful AI agents and want to offer them as a service. We overlap with creator platforms (ease of sharing and charging) but also with developer platforms (support for code and advanced actions). If you're looking for a free AI agent builder to get started, we offer a no-cost entry point.

Agent37: The Custom GPT Alternative Built for Revenue

Full disclosure: we built Agent37, so obviously we're biased. But we built it because we kept seeing the same problem over and over.

People would build incredible AI skills (especially using Anthropic's Claude), and then have no way to monetize or deploy them. The skill file sat on their computer. They could sell it as a digital download, but then the buyer needed their own runtime to use it. No trials, no subscriptions, no easy distribution. If you're wondering how to sell AI agents effectively, you need the right infrastructure.

We set out to make the first platform where creators can host and sell AI agents with zero infrastructure hassle. Think of it like Gumroad for AI agents. You can even monetize your Claude Code skills directly through our platform.

How to Monetize AI: Built-In Payments & Paywalls

Every agent you deploy on Agent37 comes with built-in Stripe payments, subscription management, and usage metering.

You set the price. 5/week, whatever makes sense for your audience.

Users get a free trial period (typically 10-20 messages) automatically. They can test the value, and then the app prompts them to subscribe via credit card to continue.

No coding needed. It's as simple as "flip on paywall, enter price."

Our business model is a straightforward 80/20 revenue split: creators keep 80% of what end-users pay, Agent37 keeps 20%. We only make money when you make money. This subscription business model has proven effective for creators across industries.

Chat and Voice AI Interfaces Out of the Box

Many use cases (especially for coaches, consultants, and consumer-facing tools) benefit from voice interaction.

On Agent37, your AI agent automatically gets a chat UI and a voice-call UI. Users can actually talk to your agent on a simulated phone call. You can even clone your own voice for the agent, making it feel like "you." Our AI voice trainer capabilities make this seamless.

One storytelling coach on our platform has an AI agent that literally speaks with her voice to guide users through narrative exercises. It creates a personal touch that justifies higher pricing.

This multi-modal interface is out-of-the-box. No extra work required.

Real AI Agent Capabilities (Not Just Chatting)

This is the biggest differentiator. Agent37 runs on the Claude Agent SDK, which means your AI isn't confined to Q&A.

It can use tools and perform multi-step workflows. You can upload an Anthropic Skill that includes Python code, data processing, web scraping, API calls, and more. Your agent can have sub-agents for specialized tasks.

You're deploying a full-fledged AI agent (with reasoning and action), not just a chatbot that responds from a static prompt.

Why does this matter? Because many real use cases need the AI to do things:

Extract info from PDFs

Look up info online

Crunch numbers

Update a spreadsheet

Generate formatted documents

A simple AI chatbot can't do these. An agent can.

How to Improve AI Performance: Analytics & Evals

Launching an AI is just the beginning. You need to refine it.

Agent37 offers full conversation logs and analytics to creators. You can see what users ask, where the AI struggles, and how they engage. (OpenAI's GPT Store doesn't let you see your own GPT's chats.)

We also integrated an Evals system: systematically test your agent's performance and catch errors. After real user sessions, you can run evals to identify failure modes. This is similar to how you'd fine-tune or improve a model over time.

Our goal is to help you go from V1 to V10 of your AI quickly, based on real feedback loops.

Real AI Agent Examples Already Running on Agent37

To make this concrete, here are some live examples on Agent37:

→ Government Contract Analysis

Consultants use this to upload government RFP documents and get back a detailed bid/no-bid analysis. The agent parses CSVs with Python, identifies relevant NAICS codes, finds open opportunities via API calls to government databases. It automated a workflow that consultants used to do manually over many hours.

→ Career Counseling for Veterans

An agent that helps military veterans transition to civilian careers. It asks questions about their experience, then generates a civilianized resume, LinkedIn profile summary, and talking points for interviews. It runs multi-step prompts and outputs formatted PDFs and Word docs.

→ Storytelling Coach (Voice-Based)

A public speaking coach created a voice agent that embodies her coaching style. Users can speak with it to practice telling their personal stories, and the agent gives feedback using the coach's methodology. The agent speaks in the coach's cloned voice, making the experience immersive. This is sold as a subscription service.

These aren't toy examples. They're real, paying use cases that go beyond a chat FAQ.

Ready to turn your GPT into a sellable product? Start free on Agent37 and see how it works.

3 Ways to Monetize Your Custom GPT Alternative

At a high level, you have a decision: Do you stick with OpenAI's ecosystem in some way, or go independent?

Most creators pursue one of three routes.

Path	Best For	Pros	Cons
A: Lead Magnet	Service providers	Free GPT attracts users, no terms violations	No direct GPT revenue
B: Sell the Recipe	Template creators	One-time purchase, easy to package	Buyer needs ChatGPT Plus, setup friction
C: Hosted Agent	Serious product builders	Full control, recurring revenue	Platform fee or revenue share

Option A: Custom GPT as a Lead Generation Tool

One indirect way to "monetize" a GPT without breaking any rules is to offer it for free to attract users, then monetize something else.

Create a GPT that showcases a bit of your expertise (maybe free tips or a mini-assessment). List it in the GPT Store for free. People try it, find value, and then you upsell them on your real service: one-on-one consulting, a course, a paid community.

Pros: Doesn't violate OpenAI terms since you're not charging for the GPT itself. Gets you exposure to ChatGPT's large user base. Excellent for marketing.

Cons: You're not getting direct revenue from GPT usage. You still face all the limitations (no user data, though you could ask users to input their email to get the "result"). And building a lead-gen GPT that actually converts requires careful design.

When to use it: If your main business is selling your time or expertise in other formats (services, coaching, high-ticket products), and the GPT is just a prospecting tool.

Option B: Sell Your Custom GPT as a Template

Another approach: sell the GPT itself as a file or template.

A Custom GPT is essentially a combination of a prompt, instructions, and perhaps uploaded documents or code for tools. Package that and sell it on marketplaces. The buyer recreates the GPT in their own ChatGPT account using your materials. This falls under the category of selling digital products.

Pros: Straightforward. Uses existing e-commerce platforms. You can price it as a one-time purchase.

Cons: The customer still needs ChatGPT Plus to use it and must follow setup instructions. It's not a smooth experience. Once you sell the "recipe," it can be copied or leaked. No recurring revenue. And buyers didn't get a fully functional tool, just the blueprint.

When to use it: If what you've built is highly valuable prompts or data that certain users would pay to get their hands on, and those users don't mind DIY setup.

Option C: Host and Sell an AI Agent (Recommended)

This is the path most serious AI product builders take: deploy your AI assistant on a platform or infrastructure that you control, complete with UI and billing.

You have two sub-options:

DIY Full Stack: Use an API (OpenAI, Anthropic) to power the AI, build a web/mobile front-end, integrate Stripe, handle user accounts, deploy servers. Ultimate control, but significant development effort. You can learn how to integrate AI into a website if you choose this path.

Use a Specialized Hosting Platform: Platforms like Agent37 let you upload your AI skill, and they provide the runtime: the model, the chat interface, user management, and built-in monetization. You focus on your content, they handle the infrastructure. You usually pay either a revenue share or platform fee.

Pros: Fast deployment and low code. Transform your GPT into a real product within days, not months. Many platforms include analytics, the ability to integrate tools or code execution, and easy ways to update prompts. You can set pricing and get paid with minimal fuss.

Cons: You're somewhat limited to the platform's features. It's not as flexible as coding everything yourself. You're adding another party into your value chain. Choose a platform you trust.

When to use it: For most individual creators, coaches, consultants, and even small startups, this is the sweet spot. It balances independence with ease-of-use.

How to Launch a Paid AI Product in 14 Days

Let's say you have an idea for an AI assistant people would pay for. Here's how to go from idea to first paying users in two weeks. If you're a coach, you might want to explore how to create a coaching program as a foundation.

Days	Focus	Key Outcome
1-2	Pick a monetizable use case	Validated problem worth paying for
3-5	Design conversation flow	Clear input→output funnel
6-9	Build V1 with safeguards	Working prototype with limits
10-12	Set up pricing and onboarding	Payment flow and UX ready
13-14	Beta launch and iterate	Real user feedback incorporated

Days 1-2: Find a Use Case People Will Pay For

Not every fun AI idea is monetizable. You need a use case where the value is clear and people already pay for an equivalent.

Use this filter:

People already pay for this. Are there consultants, apps, or freelancers doing this for a fee?

It's repetitive or tedious. AI shines at automating repetitive workflows.

Inputs can be well-defined. User uploads specific files or answers a set of questions.

The output is valuable even if imperfect. A draft, a plan, an analysis.

Real example: One Agent37 creator noticed small businesses paying significant fees for SEO content audits. It's rule-based work. He realized an AI agent could generate an SEO audit report from a website URL input. That's a monetizable job ready for automation.

Days 3-5: Design Your AI's Conversation Funnel

Your AI shouldn't just chat randomly. It should lead the user through a process to deliver the promised value. You might find our client onboarding process template helpful for structuring this flow.

Write down:

The 5-10 key questions the AI must ask every user

The "definition of done" (what final output should the user get?)

The output format (text in chat? PDF? spreadsheet?)

Design a conversational funnel: at the top, users provide inputs. At the bottom, they receive the outcome. It's okay to be somewhat structured. Users appreciate when the AI drives toward a result rather than wandering.

Days 6-9: Build Version 1 with Usage Limits

Time to implement. Keep it simple, focus on core functionality, but put limits and guardrails in place early.

Important safeguards:

Max messages per session/day: Prevent single users from racking up unlimited usage.

Max file size or number of files: Reasonable limits for performance and cost.

Tool allowlist: Restrict code or web access to what's necessary.

Graceful failure messages: Define how the agent responds when unsure. "I'm sorry, I can't do X" is better than hallucinating.

On Agent37, many of these are built-in or configurable in settings.

Test it yourself thoroughly. Give it good data, bad data, edge cases. Refine until it reliably goes through the happy path.

Days 10-12: Set Up Pricing and Payment Flow

With core functionality done, focus on user experience and monetization.

Onboarding flow: Clearly communicate:

Who should use this?

What should they input or upload?

What will they get in 5 minutes?

What will they get in 30 minutes?

What will the assistant not do?

A smooth onboarding increases trust and completion rates.

Pricing strategy: Decide how you'll charge. Subscription is common for ongoing services. One-off payment works for single-use utilities. Our guide on pricing strategy for consulting services can help you decide.

Free trial: It's smart to let users try it with limits. Enough to showcase value, not enough to satisfy all their needs.

Days 13-14: Launch to Beta Users and Improve Fast

Get real humans (not just friends) to try your AI. Aim for 5-20 people in your target audience. Find them in online communities, forums, or your network.

Offer free access in exchange for feedback. Watch them use it if possible.

You want to see failures. Every failure is a chance to improve before you start charging widely.

Common issues to look for:

Missing information (did users provide what's needed?)

Unclear prompting (did users understand the AI's questions?)

Tool or integration errors

Wrong assumptions about scope

Categorize issues. Address the top 2-3 that would most improve the next user's experience.

After a quick improvement cycle, remove any free bypass and officially open up the paywall. Now you have a product that strangers can come, try, and pay for.

How to Price AI Without Losing Money on Usage

One of the most common questions: "What should I charge? And will AI costs eat my profit?"

AI Model Costs in 2026: The Real Numbers

Good news: AI model costs have been decreasing. As of late 2025:

OpenAI's API for GPT-5.2 is about $14 per million output tokens on the standard plan

Anthropic's Claude Sonnet 4.5 costs [**3 per million input)

Claude Opus 4.5 is pricier at $25 per million output, but often more efficient

What do these numbers mean practically?

Suppose in one user session, you exchange about 1,500 tokens of input and 800 tokens of output per turn. That's roughly 1.4 cents per conversation turn.

Over 100 messages in a month from one user, that's about 1.65 in model cost for that user.

If you charge $29/month, your margins could be excellent. The raw AI cost per active user isn't huge for moderate usage.

The Real Cost Problem: Power Users and Abuse

A small percentage of users might heavily use (or abuse) your AI, consuming far more resources than average. Those outliers can drive up costs disproportionately.

To protect yourself:

Hard per-user limits: Max 100 messages per month on a basic plan caps worst-case cost.

Soft throttling: If a user sends unusually high volume, maybe switch them to a less expensive model or shorter responses.

Prompt caching: If your system prompt is large, cache it. OpenAI's API already discounts repeated content heavily.

File size and timeouts: Don't allow arbitrarily large files or endless loops.

The goal isn't to make the AI unusable. It's to prevent a few outliers from eating your profit.

3 AI Pricing Strategies That Actually Work

Strategy 1: Outcome-Based Subscription Pricing

"Pay $$ for the result, not per use."

Charge a flat monthly fee equivalent to getting a certain outcome repeatedly. A storytelling coach AI might charge **1000 coach," it's reasonable.

This works best when you can frame it as "Get X done (that normally costs much more) for just $Y per month."

Strategy 2: Usage-Based Pricing (Pay-as-You-Go)

User pays per message or per task. Often done via buying credits or charging per batch of requests.

Use this when output cost is highly variable. If one user might use 10x more resources than another, usage pricing can be fairer.

Downsides: It puts mental burden on users to gauge how much to use. Subscriptions are more predictable.

**Strategy 3: Tiered Plans (Free to Premium)

Offer multiple plans with increasing limits:

Tier	Best For	What's Included
Starter (Free or Low $)	Trying it out	20 messages/month, basic model
Pro (Mid-range $)	Power users	200 messages/month, better model, file uploads
Team (High $ or custom)	Organizations	Multiple seats, priority support, admin dashboard

This segments your audience. Casual users stick to free or cheap. Serious users pay more for more capabilities. Ensure each tier jump clearly delivers more value.

Key advice: Align pricing with the customer's perceived value, not your cost. Your cost might be 500 of alternative spend, charging $50+ is fair.

Also, it's easier to start higher and offer discounts than to start too low and raise prices on existing users.

Custom GPT Alternative FAQ: Your Questions Answered

Can I Embed My Custom GPT on My Website or App?

No. OpenAI's policy is clear: GPTs can only be used on chat.openai.com and cannot be integrated into other websites. No iframe, no script, no embed code.

If you need an AI assistant on your own website, you have two options:

Use the OpenAI Assistants API (requires building your own backend and frontend)

Use a third-party platform that supports embedding or shareable links

Many Custom GPT alternatives allow this. Agent37 provides a shareable web link that anyone can access without a ChatGPT account. You can also learn how to add a chatbot to your website using our platform.

Can I Make Money Directly from My GPT Store Listing?

Maybe eventually, but currently it's very limited. As of early 2026, OpenAI has a pilot program to share revenue with a small group of US-based GPT creators. If you're not already in that program, you can't apply. It's invite-only.

OpenAI's wording: "testing monetization with a handful of GPT builders." They haven't rolled out a general scheme where you set a price and get paid.

OpenAI community moderators have said: "Forget relying on GPT Store revenue... use your own methods to monetize."

That means building a paywall outside of ChatGPT. If you want to make money with AI, you need your own monetization infrastructure.

Can I See What Users Are Asking My GPT or Access Analytics?

No. OpenAI does not provide GPT creators any dashboard or logs of user conversations. You won't see questions asked or answers given. You might see how many people used or favorited your GPT, but that's it.

This is why many are moving to external platforms. Agent37 gives you full visibility into interactions (with appropriate privacy measures), and our Evals system helps you identify where the AI struggles.

Is My Data Safe in the GPT Store? Can People Copy My GPT?

The GPT Store should be considered public. Any files or instructions you upload into a GPT could potentially be extracted by savvy users. There have been reports of people cloning others' GPTs by getting them to reveal their content.

OpenAI does not guarantee privacy of the data in the GPT prompt itself. Some have demonstrated exploits like asking the GPT to zip its own files and share them.

If your AI uses confidential info, don't host it as a public GPT. Use a secure alternative.

What's the Simplest Route to Turn My GPT into a Paid Product?

Use a hosted runtime that provides paywalls, user accounts, and analytics.

We built Agent37 specifically for this. You would:

① Take your prompt/skill logic from your GPT

② Upload it on Agent37, configure any tools it needs

③ Set your subscription price and free trial limits

④ Click deploy and get a shareable link where users can start immediately

All the runtime (hosting) and business stuff (billing, auth, security) is handled for you.

How Does Agent37's Pricing Work for Creators?

We operate on a revenue-share model. Creators keep 80% of what end-users pay. Agent37 keeps 20%. There's no upfront cost to use the platform.

From the end-user side: you set the price and billing period. We currently support monthly subscriptions and one-time payments. When a user subscribes, Stripe handles payment and you receive payouts of your share.

We also enforce the free trial message cap you configure (typically 10-20 messages). After that, users hit a paywall message within the chat UI.

What's the Difference Between a "Chatbot" and an "Agent"?

These terms get thrown around a lot. In our context:

A chatbot is an AI that you chat with in natural language, usually limited to responding based on prompt and context. It doesn't take independent actions beyond talking. Most Custom GPTs are essentially chatbots.

An agent implies the AI can take actions in an environment. That could be calling external APIs, running code, browsing the web, executing commands to achieve a goal. It has tools it can perform beyond just generating text.

If you ask a chatbot "Book me a flight to NYC," it can at best respond with "Here's what you should do." An agent, in theory, could actually interact with a flight booking API.

Agent37 uses Anthropic's Claude agent architecture, which is why we say your skills on our platform "can actually do things, not just talk." Learn more about what is conversational AI and how it differs from true agents.

Why Use Anthropic's Claude Instead of OpenAI's GPT?

Both are excellent models. It's not either/or.

A few points:

Long-form and reasoning: Claude (especially Claude 4.5 models) performs very well on longer context and step-by-step reasoning tasks. It supports up to 100K context in some versions.

Agentic architecture: The Claude Agent SDK provides a structured way to build multi-step skills with tool use. OpenAI's platform is only catching up. We built Agent37 on Claude because it was early to offer a comprehensive agent framework.

Style: Some find Claude's style more aligned for nuanced, friendly responses.

It's not that Claude is "better" universally. It's about what you're building. We abstract away model details for creators. You build your skill, we ensure the model behind it can execute well.

The Bottom Line: Should You Use a Custom GPT Alternative?

If you've built something useful inside ChatGPT and your goal is "people pay me every month for access to my AI," then continuing to rely on a Custom GPT alone is not the path forward.

A Custom GPT is essentially a sandboxed prototype. Fantastic for experimentation and reaching existing ChatGPT users. Too limited for a standalone business.

To create a real AI product, you need an environment where you control the runtime and business layers:

Your AI, on your platform (no ChatGPT requirement)

Your own paywall and pricing (revenue flows to you)

Analytics and iteration (improve based on real feedback)

Branding and UX control (build your brand)

This typically means either building a custom solution with APIs or using a platform like Agent37 that was purpose-built for hosting and monetizing AI skills. For entrepreneurs, the right AI tools for small businesses can make all the difference.

The best Custom GPT alternative is to host your AI outside of ChatGPT entirely. Turn it into an agent or app that you own.

The tools and platforms are finally here to support individual creators launching AI-powered micro-SaaS products. The era of "monetize your skills by turning them into AI services" is just beginning.

Ready to move beyond the GPT Store? Try Agent37 free and see how it works. Or explore the other alternatives we've covered. What matters most is getting your creation into users' hands in a way that can sustain itself.

Good luck, and happy building.