AWS Generative AI Scaling 
Tom Tunguz
APRIL 6, 2023
There are 4 questions a startup should ask themselves about building a startup that uses generative AI. I started with a few sentences, uploaded them to gamma.app to outline the presentation, popped over to Midjourney to generate images along the story line, & published it in IA Presenter.
SaaStr
MARCH 1, 2024
Generative AI is a platform shift where models can take inputs such as text, image, audio, video, and code and generate new content into any of the modalities mentioned. They just happened to launch AWS and ended up capturing way more value just in themselves than the entire on-prem storage market before them.
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SaaStr
DECEMBER 14, 2023
Google Cloud Platform, on the other hand, is in a very different set that also competes with Microsoft, but AWS is considered their biggest competitor in the market. What they’re seeing with GenAI and Google Cloud is an opportunity to grab share from AWS. Right now, 70% of the GenAI startups are using Google Cloud.
How To Buy Saas
MAY 29, 2025
For instance, companies like Sephora and Amazon have deployed AI-driven recommendation engines. Executives also leverage generative AI for idea generation and reporting. Intelligent Automation for Operational Efficiency Intelligent automation blends robotic process automation (RPA) with AI to streamline complex workflows.
Sales Hacker
DECEMBER 8, 2023
While many gravitate towards major industry events for announcements, Joseph has a hot take (and we love a spicy take): do not announce a major new feature at a major industry conference (such as RSA Conference or AWS re:Invent). Start-ups to watch: This week, Demostack released its AI Data Generator to the public.
Clouded Judgement
MARCH 22, 2024
This is why the consumption players (Snowflake, Mongo, Confluent, Azure, AWS, etc) so more variability in the macro slowdown. And because the AI (particularly generative AI) space has so recently come on the scene, there aren’t a huge number of vendors!
Clouded Judgement
SEPTEMBER 19, 2023
If you want features in your lakehouse (on top of open source Iceberg) for ingestion, CDC, streaming (file loading, Kafka connect, etc), schema evolution, compaction, optimization, time travel, snapshots, auto-scaling, maintenance (no more writing spark jobs to delete snapshots!), A natural question is “why do we have two tiers?
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