printf " ${DIM}This install is using DeepSQL's shared managed Azure OpenAI key, so your${RESET}\n" printf " ${DIM}schema and queries are processed through DeepSQL's shared LLM resource.${RESET}\n"
also, it requires docker.
Yes, this requires docker. Here is more technical documentation on prerequisites and suggested setup (including AWS CFT)
For deployment help, pl email me at venkat@deepsql.ai
Can you explain this in more detail? You saying that DeepSQL can create BI dashboards that makes Tableau and friends redundant -- are you talking about analytics dashboards consumed by business teams or more like db ops dashboards used by backend teams? Does the user prompt the agent for them, or does it build dashboards that it infers are needed?
I am happy to get connected over email venkat@deepsql.ai and share more details on pricing and deployment help.
Where does this number come from? It feels completely arbitrary.
We worked with 6 YC companies who are running on Aurora postgres and worked through the inefficiencies of workloads - too many indexes (in some cases almost every column is indexed), over indexing significantly increases I/O ops (writes), this hidden costs are enormous for fully managed services like Aurora. In all these cases, we could safely come up with a plan to cut down their DB spend by more than 40%.
Personally, I will instantly distrust a new product claiming an exact number of 40% savings -- not even "up to 40%" or "over 40%", which would still be misleading, but less weird than claiming exactly 40%. Especially in the second line of the homepage, without any supporting link or information.
> we removed the retool and appsmith licenses, another $2k savings.
These aren't DB-specific tools though? Why would this count towards the savings?
> 6 YC companies [...] in some cases almost every column is indexed
If your savings math is based on very early-stage startups who made horrendously bad indexing mistakes, honestly that would reduce my trust even further. That said, I'm definitely not the audience for your product.
Our goal was to tell a convincing story with case studies. However, you are right on many dimensions that this number means nothing for many optimized scenarios.
Also, deepsql is intelligent in understanding the deployment type. Postgres standalone deployment is very different than Aurora postgres. Cost factors are quite different. Other DBA tools might ignore these factors - Hence they need well trained DBA to use them.
DeepSQL does continuous evaluation of queries, we maintain performance life cycle of a query by customer.
A dashboard query for customer A might run for 2s, but for customer B it would take 50s. Here data clustering is the problem, skewness of distinct join keys … various factors. If query fix has to happen by looking at that query alone, one would make wrong decision (probably creating index). But a holistic decision would be partitioning here.