The latest articles on DEV Community by Beck (@beck_4198151490146711f11b). https://dev.to/beck_4198151490146711f11b https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F4082719%2Fcede8530-fc7f-43c7-b370-bca07b0f881f.png https://dev.to/beck_4198151490146711f11b en Beck Tue, 18 Aug 2026 09:30:23 +0000 https://dev.to/beck_4198151490146711f11b/building-a-telegram-ai-assistant-with-a-multi-llm-fallback-chain-2jk9 https://dev.to/beck_4198151490146711f11b/building-a-telegram-ai-assistant-with-a-multi-llm-fallback-chain-2jk9 <p>I'm not a professional developer — my background is in banking, food production, and now construction — but I've spent the last few weeks building Jimi, a personal AI assistant that lives inside Telegram. Wanted to share some of the architecture decisions, since a few things surprised me along the way.</p> <h2> The setup </h2> <p>Telegram is just the client. All the actual logic — intent parsing, memory, tool calls, voice — lives in a FastAPI backend, completely decoupled from the messaging layer. The idea was to avoid locking myself into Telegram forever; if I ever want a web client or a different messaging platform, the core doesn't need to change.</p> <h2> Multi-LLM fallback </h2> <p>Jimi's primary brain is OpenAI (gpt-4o-mini for cost reasons), but I built a fallback chain to Gemini and DeepSeek in case of rate limits or outages. This turned out to be more important than I expected — even a short outage on one provider used to mean the whole assistant went silent. Now it just quietly falls back.</p> <h2> The expensive lesson: voice is not cheap </h2> <p>I assumed text tokens would be my main cost driver. They're not even close. Once I actually modeled the unit economics, voice (transcription + TTS) turned out to be roughly 20-25x more expensive per interaction than text — mostly because of TTS on every response, not the transcription itself. That completely changed my monetization plan: text stays free, voice becomes the premium feature.</p> <h2> What's next </h2> <p>Working on persistent memory (so Jimi actually remembers preferences, not just events) and eventually expanding beyond chat into things like calendar and email actions.</p> <p>Curious if others here have dealt with similar cost surprises when adding voice/audio to an LLM-based product — would love to hear how you approached it.</p> ai architecture backend llm