Cloud cost optimization startups have become ubiquitous, and they’ve found a friendly ear among enterprise clients looking to cut costs amid the downturn. But should younger startups similarly scrutinize their cloud spend?
According to several cloud investors, startups should prioritize building over optimization — unless it’s going to save them a big chunk of money.
Boldstart Ventures partner Shomik Ghosh summed it up succinctly: “In early product or go-to-market stages, optimizing cloud spend should be the last thing on a founder’s mind besides utilizing as much cloud resource credits as possible.”
We’re widening our lens, looking for more investors to participate in TechCrunch surveys, where we poll top professionals about challenges in their industry.
If you’re an investor and would like to participate in future surveys, fill out this form.
While founders shouldn’t lose sleep over cloud costs at the early stages, they should still carefully ponder other expansionary decisions, like cloud marketplaces, before foraying out. Himself an entrepreneur, angel investor Anshu Sharma noted that using cloud marketplaces as a distribution channel has pros and cons, and shouldn’t perhaps be done from day one because “it can commoditize your offering.”
Quiet Capital founding partner Astasia Myers concurred, saying startups should focus on finding product-market fit first. “We encourage startups to consider cloud marketplaces once they have found product–market fit, not before,” she said.
“To successfully leverage cloud marketplaces, a solution’s product marketing, value proposition and return on investment need to be clear while exhibiting a fast time to value, which happens post-PMF.”
However, because of how fast things are moving, startups can explore marketplaces earlier than they could: “Historically we saw startups join cloud marketplaces at Series D+. Now we are starting to see companies consider it post Series B.”
Founders should also remember that startups are destined to become bigger and should therefore plan ahead. “It’s always important to select a technology stack that is available in all major cloud providers and that is as elastic as possible to support those migrations should they be needed (using Kubernetes is a great example of allowing for that),” Liran Grinberg, co-founder and managing partner at Team8 said.
To find out what cloud-related advice investors are giving startups these days, we spoke with:
- Shomik Ghosh, partner, Boldstart Ventures
- Liran Grinberg, co-founder and managing partner, Team8
- Tim Tully, partner, Menlo Ventures
- Astasia Myers, founding partner, Quiet Capital
- Anshu Sharma, angel investor and co-founder/CEO, Skyflow
Shomik Ghosh, partner, Boldstart Ventures
Founders are looking to cut costs amid the downturn. How important is it for startups to optimize their cloud spend in the early days?
It depends on what is meant by “early days.” In early product or go-to-market (GTM) stages, optimizing cloud spend should be the last thing on a founder’s mind besides utilizing as much cloud resource credits as possible. Finding product-market fit, engaged users and understanding the end-user workflow and how the product is essential to these users is the most important area founders need to focus on.
As the company starts to have a few million in ARR, then it starts to make sense to manage cloud spend more closely to improve gross margins and therefore the bottom line (net cash burn or free cash flow).
Major cloud providers often lure startups with free credit, but they also charge data egress fees later on. As cost optimization becomes a bigger consideration than ever, how consequential are early-stage decisions on choosing a cloud provider?
I think picking a cloud provider at the early stage based on cost is missing the forest for the trees. I know some founders who, in the early days, switch cloud providers to keep utilizing free credits. This may be possible when there are only a few people on the team, but as the team gets bigger, everyone needs to learn and relearn documentation, APIs and UIs, which has a bigger hidden “cost” than any money being saved.
Cost optimization is not just the size of the bill at the end of the month. It’s also the velocity of the team’s product development, downtime avoided, developer experience to allow teams to move faster, etc. All of these points should be top of mind when choosing a cloud provider at the early stages.
What are the pros and cons of using a multicloud setup instead of building on top of a single public cloud?
As a company scales, teams become a bit more focused on functional areas. In the early days, everyone does everything, but as the team scales, you have not just a back-end infra team but inside of that, a database team, a security team, an ML team, a QA team, etc. Multicloud can help get the benefits of best-of-breed tooling from each cloud provider.
In the early stages of a startup’s life, it is most important to go from zero to one. Astasia Myers, founding partner, Quiet Capital
For example, Google BigQuery may be better for some use cases than Redshift or Azure Synapse, while AWS may have the best infra management tooling. The trade-off, of course, is having to make all those tools across platforms interoperable, and the major cloud providers are not exactly incentivized to do this.
This is where startups come in, and by focusing on making one product the best, they can work across platforms and integrate easily (i.e., Snowflake can be used across any major cloud provider).
When should a startup consider going on-prem, if at all? Would you advise AI/ML startups any differently?
In terms of terminology, I think on-prem should also be called “modern on-prem,” which Replicated coined, as it addresses not just bare metal self-managed servers, but also virtual private clouds.
The most common reason startups should consider modern on-prem is for dealing with sensitive data, which especially occurs in regulated industries (healthcare, financial services or pharma). The scope of what is considered sensitive is growing over time with regulations though, so it’s something more startups need to be aware of.
A lot of ML tooling does need to be deployed across any environment, as the large enterprises keep some of this data in strictly controlled environments. In the end, startups need to meet the customer where they are — if you are designing cloud-first and dealing with customers who have sensitive data, then you should consider what your “any environment” deployment strategy would be, whether using Replicated, building your own or choosing not to work with those customers.
Have cloud costs reached a plateau in relation to the marginal cost of computing or storage?
I think this is a hard prediction for anyone to make. People say Moore’s law is coming to a close, but then another law pops up. I don’t think human ingenuity has plateaued, and companies continue to reduce the costs on their platform using ASICs [application-specific integrated circuits] or ML to optimize workloads. For example, Snowflake continues to drop pricing; so it’s hard for me to say cloud costs have reached a plateau.
What do you think of cloud marketplaces as a distribution channel?
They are great! The clearest benefit is being bundled into the overall billing commitment of a customer to that cloud provider. It speeds up the procurement cycle, allows the customer to consolidate billing and enables them to better take advantage of the massive forward contract that they have likely committed to the cloud provider for many years.
If that contract is not fully utilized by end of term, then the customer ends up paying for services not rendered.
How big is the market for cloud providers to provide extra services beyond their core offering?
I’m not being facetious when I say infinite. For proof, just go to AWS and look at its product catalog for all the various services listed. It would take years to fully comprehend all that it offers.
And if we expand the terminology of “cloud providers” beyond the compute and storage layer, pretty much every public and private company delivering a cloud service has multiple product offerings at scale.
