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We priced too low at first, tried too high when we released our enterprise product, did some compares with other dev tooling in sales convos with early customer
by lewq 6y ago
We priced too low at first, tried too high when we released our enterprise product, did some compares with other dev tooling in sales convos with early customers and then settled on something in the middle ($600/user/month) and stuck with that cuz it seemed to be working. If we'd been around for longer we would have done more pricing tests but getting the right features to unlock larger deals (more users) was higher priority at the end. Btw, the delivery model that had PMF was customer hosts it in their own cloud, we ship terraform
- streetcat1 6y agoThanks again for the info. This is what I thought about self-hosting. However, did customers raise concerns about hosting your code in their on perm clusters, For example, did they insist that you open source? Also, when you host the code on the customer cluster, how did you track usage? For example, was there a process within the customer site, that would send usage information to your site ? And, I would also imagine that every engagement is unique (not sure if this is correct), Hence, did you outsource the solutions engineering or that was done as part of the team?
- deforciant 6y agoHi, one of the engineers who created dotscience here. Customers didn't insist on open sourcing anything, we did have some open source components but I don't think they cared about that :) Regarding tracking usage - usually it was just many conversations with customers and having them on our Slack channel. Solutions engineering - mostly development team would be helping with writing anything specific that they need. On the SaaS side we had a lot more analytics, used segment, intercom and internal "audit events" to better understand what's happening.
- streetcat1 6y agoThanks much for the response. So to sum up, what did customers care about the most from your point of view? (maybe top 3)
- lewq 6y ago1. Deploying models easily (data scientist doesn't need to grok docker/kube) 2. Monitoring models including data and model drift/statistical monitoring (data scientists don't need to grok prom/grafana) 3. Only once these base concerns are met, model inventory, provenance, data versioning, reproducibility, collaboration with notebooks, ci integration etc. Happy to talk more - drop me a note at luke@dotscience.com