Grammarly
Productivity · AWS · Native (own / self-hosted weights)
Long-standing in-house NLP models plus GPT for generative rewriting.
Productivity · AI architecture
How Productivity companies build with AI — which foundational models they run and how directly, ranked by proximity to the model. Avg Foundation Proximity Score 65/100.
Productivity · AWS · Native (own / self-hosted weights)
Long-standing in-house NLP models plus GPT for generative rewriting.
Productivity · AWS · Direct API
Low-code database whose 'spreadsheet-database' is backed by MySQL with heavy Redis caching on AWS. Airtable AI/Cobuilder routes to OpenAI and Anthropic.
Productivity · Hybrid · Direct API
Asana runs a large-scale, stateful, microservice-style backend with autoscaling infrastructure and a dedicated Production Infrastructure pod for observability and platform reliability, layered behind a multi-region, hybrid cloud deployment. Its data stack separates transactional MySQL/Aurora stores from analytical event pipelines and a cloud data warehouse used by growth and analytics teams.
Productivity · AWS · Direct API
Work OS; AI blocks on OpenAI over board data.
Productivity · AWS · Off the LLM map (in-house non-LLM ML)
Notion’s core production architecture is centered on a sharded PostgreSQL system on AWS, with 96 physical Postgres instances and five logical shards per instance as of 2023. Its data lake pipeline uses Debezium CDC into Confluent Cloud Kafka, then Apache Hudi and S3 for downstream analytics, search, and AI-related workloads; the public sources do not confirm a native foundational model inside Notion’s core product.
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