How My Telegram Bot Got 500 Paying Users in 3 Months (Python + aiogram)

I'm Kirill Strelnikov, a freelance developer in Barcelona. I build Telegram bots, AI chatbots, and SaaS platforms. This is the story of a Telegram AI aggregator bot I built that hit 500 paying users in 3 months — and was profitable from month 1.

The Idea

The client wanted a Telegram bot that aggregates multiple AI models (GPT-4, Claude, Gemini) into a single chat interface. Users pick a model, send a message, get a response — all without leaving Telegram. The monetization model: credit-based billing.

Simple concept. The execution is where it gets interesting.

Architecture

Telegram user
  → aiogram bot handler
  → Django backend (REST API)
  → Credit system checks balance
  → Route to selected AI model (OpenAI / Anthropic / Google)
  → Stream response back to Telegram
  → Deduct credits

Key components:

1. Credit-based billing system

class UserCredits(models.Model):
    user = models.OneToOneField(User, on_delete=models.CASCADE)
    balance = models.DecimalField(max_digits=10, decimal_places=2, default=0)

    def deduct(self, model: str, tokens: int) -> bool:
        cost = self.calculate_cost(model, tokens)
        if self.balance >= cost:
            self.balance -= cost
            self.save(update_fields=["balance"])
            CreditTransaction.objects.create(
                user=self.user, amount=-cost,
                model=model, tokens=tokens
            )
            return True
        return False

    def calculate_cost(self, model: str, tokens: int) -> Decimal:
        rates = {
            "gpt-4": Decimal("0.003"),      # per 1K tokens
            "gpt-3.5": Decimal("0.0005"),
            "claude-3": Decimal("0.002"),
            "gemini": Decimal("0.001"),
        }
        return rates.get(model, Decimal("0.002")) * (tokens / 1000)

2. Multi-model router

Each AI provider has its own adapter class. The router picks the right one based on user selection:

class ModelRouter:
    adapters = {
        "gpt-4": OpenAIAdapter,
        "claude-3": AnthropicAdapter,
        "gemini": GoogleAdapter,
    }

    async def generate(self, model: str, messages: list) -> str:
        adapter = self.adapters[model]()
        return await adapter.chat(messages)

3. Telegram payment integration

Users buy credits directly in Telegram using Stripe:

@router.message(Command("buy"))
async def buy_credits(message: Message):
    prices = [
        LabeledPrice(label="100 credits", amount=499),   # $4.99
        LabeledPrice(label="500 credits", amount=1999),   # $19.99
        LabeledPrice(label="2000 credits", amount=5999),  # $59.99
    ]
    # Send Telegram invoice
    await message.answer_invoice(
        title="AI Credits",
        description="Credits for AI model usage",
        payload="credits_purchase",
        provider_token=STRIPE_TOKEN,
        currency="USD",
        prices=[prices[0]],  # Default package
    )

Growth Metrics

Month	Users	Paying users	Revenue	MRR
1	800	85	$1,200	$1,200
2	2,100	280	$3,800	$3,800
3	3,500	500	$6,200	$6,200

Conversion rate: ~14% free-to-paid (industry average for bots is 2-5%).

Why so high? Free tier gives 20 credits — enough to try all models but not enough for daily use. The "aha moment" happens when users compare GPT-4 vs Claude on the same prompt. Once they see the value of model switching, they pay.

What Made It Work

1. Telegram's 900M+ user base

No app to install. No signup form. Users just open the bot and start chatting. The friction-to-value ratio is unbeatable.

2. Credit-based pricing (not subscription)

Users pay for what they use. Heavy users spend $20-60/month. Light users spend $5. Nobody feels locked into a subscription they don't use. This increased retention significantly vs subscription-only models.

3. Message open rates: 80%+

Telegram notifications actually get read. When we sent "New model added: Claude 3 Opus" — 82% open rate. Compare that to email marketing (20-25%).

4. Viral inline mode

Users can use the bot inline in any chat: type @botname what is Django? in a group chat and get an AI answer. This drove organic growth — other group members see the bot in action and try it themselves.

Tech Stack

• Bot framework: Python 3.12 + aiogram 3.x (async)
• Backend: Django 5.0 + Django REST Framework
• Database: PostgreSQL (user accounts, credits, transactions)
• Cache: Redis (rate limiting, session data)
• Task queue: Celery (webhook processing, analytics)
• AI providers: OpenAI, Anthropic, Google AI APIs
• Payments: Stripe via Telegram Payments API
• Deployment: Docker + Nginx on a VPS

Lessons Learned

1. Monetize from day 1. Don't wait for "enough users." The free tier should be a taste, not a full meal.

2. Credits > subscriptions for bots. Bot usage is spiky. People use it heavily for a week, then pause. Credits accommodate this pattern naturally.

3. Telegram bots are underrated as a business channel. 900M+ MAU, 80%+ open rates, built-in payments, inline sharing. For many use cases, a Telegram bot is better than a mobile app.

4. Multi-model is the moat. Any single-model wrapper gets commoditized instantly. The value is in comparison and switching between models.

Cost to Build Something Similar

Based on my experience building this and similar bots:

Complexity	Timeline	Cost
Simple bot (1 model, basic billing)	1-2 weeks	EUR 500-1,000
Multi-model with credits	3-4 weeks	EUR 1,500-3,000
Full platform with admin, analytics	5-8 weeks	EUR 3,000-6,000

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I'm Kirill Strelnikov, freelance Python developer in Barcelona, Spain. I build Telegram bots, AI chatbots, and SaaS platforms for businesses across Europe. 15+ projects delivered.

• Website: kirweb.site
• Telegram: @KirBcn
• Cost guide: How much does a Telegram bot cost?