Conversational automation for social comments and DMs across messaging apps
Manychat automates social conversations across Messenger, WhatsApp and Telegram, built on a Python stack (FastAPI, Django, asyncio) running on AWS and Kubernetes. Its adoption of LiteLLM, Kong, vLLM and Triton alongside OpenAI, Anthropic and Bedrock points to a multi-model AI gateway build, matching pain points around LLM reliability, inference latency and AI cost. A parallel push targets affiliate partner scale and a pricing-model migration.
Manychat, founded in 2016 and headquartered in Austin, TX, builds a platform that automates replies to social comments and direct messages so creators and brands can convert engagement into leads and sales. It works across Instagram, TikTok, WhatsApp, Messenger and Telegram, and is a Meta Business Partner. The privately held company has 201–500 employees, with teams in Austin, Barcelona, Yerevan, São Paulo and Amsterdam. Current hiring spans the Netherlands, Spain and the United States.
Manychat runs on Python (FastAPI, Django, Flask, asyncio) with PostgreSQL and Redis, deployed via Docker, Kubernetes and Terraform on AWS, with Snowflake, dbt and Dagster for data and Braze for messaging.
Yes. Manychat uses OpenAI, Anthropic, Azure OpenAI and Amazon Bedrock, and is adopting LiteLLM, vLLM and Triton, suggesting a self-managed AI gateway and inference layer.
Manychat is hiring in the Netherlands, Spain and the United States, mostly senior-level roles with engineering the largest function, followed by marketing, product and HR.
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Manychat's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.