Databricks vs turbopuffer

Which one do AI engines recommend, and how do they differ on price, fit and features?

The short answer

turbopuffer is the stronger AI pick in Vector Databases: it has a visibility score of 4.6 versus 1.4 for Databricks.

AI answers describe it more positively (35% vs 33% positive)
You need ISO 27001 or ISO 27017 or ISO 27018 or ISO 27701 or SOC 1 or SOC 2 Type II or SOC 3 coverage
You prefer a larger, established vendor (5,000+ employees)
Lakehouse architecture combining data warehousing with data lake flexibility

You want the brand AI engines rank higher (4.6 vs 1.4)
You need SOC 2 Type 2 or HIPAA (Business Associate Agreement available) or GDPR-ready DPA coverage
serverless vector and full-text search built on object storage
high performance at a fraction of traditional costs

Latest weekly run · Nov 2

The stronger value in each row is marked with a ✓.

Weekly runs over the last 60 days

Databricks 0pp · turbopuffer 0pp over 0 weekly runs.

turbopuffer leads on 3 of 3 engines

ChatGPT

1.9%

6.7%

AI Mode

1.3%

4.3%

Perplexity

1.1%

2.8%

Databricksturbopuffer

0 engine answers name both brands

0Databricks ranked higher

0turbopuffer ranked higher

Dimension	Databricks	turbopuffer
Pricing model	—	Usage-based
Entry price	—	$16 / mo
Free plan	—	No
Deployment	Available across all major cloud providers	Multi-tenant cloud on usage-based plans; single-tenant and bring-your-own-cloud (BYOC) deployments on the enterprise plan.
Compliance	ISO 27001, ISO 27017, ISO 27018, ISO 27701, SOC 1, SOC 2 Type II, SOC 3	SOC 2 Type 2, HIPAA (Business Associate Agreement available), GDPR-ready DPA
Support	Technical support on request, Technical documentation	Community Slack and email on all plans, Private Slack channel on higher plans, 24/7 support and 99.95% uptime SLA on enterprise
Founded	2013	2023
Headquarters	San Francisco, CA	Ottawa, Ontario, Canada
Employees	5,000+	11–50
Funding	Round at $60B valuation, including secondary sale	Seed
Best for	Data engineering, data science, analytics and AI teams at startups and global enterprises	Teams building AI applications, semantic search or recommendation systems that need large-scale vector and full-text search at lower cost.