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AI companies] have a real use case for the cloud which is somewhat different than what we see from some other companies. As Google also reported, usage-based pricing models may weather downturns betterbecause the products they meter grow irrespective of headcount growth, a positive sign for infrastructure.
Every week I’ll provide updates on the latest trends in cloud software companies. This is why the consumption players (Snowflake, Mongo, Confluent, Azure, AWS, etc) so more variability in the macro slowdown. Headcount planning, budgeting, fundraising, etc can often be largely based on a top line plan.
On a much smaller scale, we’ve also started to see some leaders deploying their genAI budget against headcount savings, particularly in customer service. While some leaders addressed this concern by hosting open source models themselves, others noted that they were prioritizing models with virtual private cloud (VPC) integrations.
If you don’t want to manage all of the infrastructure around Iceberg (plus allocating headcount to do this!) Before diving into more specifics about what Tabular does, I’d like to start with a brief overview of how cloud-native data architectures have evolved over the last decade.
Cloud destinations (coming soon) — we are in process of adding the ability to export your MRR movements data from ChartMogul onto the cloud. We will support exports to Amazon S3, Microsoft Azure Blob, and Google cloud storage. This year, we also migrated ChartMogul to AWS cloud.
So yes, while it’s true that challenges are real for those in the right-hand column above – overall cloud spend is still up 20%. Google Cloud , Azure, and GitLab, all tied directly or indirectly to AI, are seeing massive acceleration. But Google Cloud, Azure, and GitLab are all benefiting and on fire.
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