Sanjeev Mohan, Principal Analyst at SanjMo returns to speak with Vinay and kick off Sovereign DBaaS Decoded for 2024. The discussion touches on the evolving landscape of cloud, data sovereignty, the ongoing influence of geopolitical and economic factors driving IT strategies, as well as the technological undercurrent that is driving key decisions over where enterprises are running their data workloads.

They dive into the growing hyperscaler acknowledgement of data sovereignty through the lens of AWS’s and Microsoft Azure’s recent sovereign cloud programs and explore whether this is meaningful action of merely “sovereignty washing”. They reflect on key events of 2023, from the rise of generative AI and court rulings like Schrems II, to notable industry events like security breaches, and MariaDB’s challenges.

The conversation then looks to 2024 as they contemplate the shift from a “cloud-first” to “cloud-smart” approach, driven by economic uncertainties, evolving technology and the need for cost optimization. Sanjeev predicts a year of consolidation, speculating on how AI workloads could be brought to on-premises data. They highlight the renewed relevance of hybrid environments in potentially leveraging AI capabilities while navigating vendor lock-in concerns and evolving regulatory landscapes. The episode concludes on a positive note as Sanjeev predicts that enterprises will shift their focus back to innovation.

Key insights

⚡Hybrid cloud gets a second act

Are enterprises shifting from a “cloud-first” to “cloud-smart” approach, re-thinking hybrid models as a way to optimize for cost, compliance and control? Ironically, it is cloud technologies like containerization that are making this possible; and, although we’ll continue to see workloads move to cloud, it is likely that enterprises will take a more deliberate approach when deciding where their workloads need to live.

⚡Stop taking your data to AI, bring AI to your data

Given recent events such as the New York Times’s lawsuit against OpenAI and high-profile data breaches, the evolving privacy regulation landscape, and the commoditization of the “picks and shovels” of AI, it may be better and now more realistic to bring AI to your workload instead of the other way around. If your workload is already in the cloud, sure, leverage AI tooling there. If it isn’t, maybe not?

⚡The optimization vs. innovation pendulum swings again

A bold prediction from Sanjeev; if 2023 was a year of retrenchment and optimization, 2024 will, and in fact, needs to be a year of returning to innovation. It’s not a money problem of money; VCs have it, enterprises have it. They just need to decide where to make the bet and Vinay is bullish on a H2 return.

Episode highlights

💡 Hyperscalers release sovereign cloud solutions, is it “sovereignty washing”? [00:17:11]

AWS and Microsoft are the latest entrants in the data sovereignty space with their sovereign cloud programs. But, Vinay wonders whether this is merely “sovereignty washing”, causing unnecessary confusion when in fact, their validity is predicated on US / EU data flow agreements that are consistently challenged and overturned — as the latest most likely be.

💡AI bringing the sexy back to hybrid deployments? Yes and no… [00:26:21]

Vinay asks whether cloud-first is transitioning into cloud-smart, re-opening the door for hybrid deployments. Sanjeev agrees, caveating that we won’t be going back to the on-prem days and that there will continue to be a migration to the cloud. They discuss what is making this possible and how enterprises are seeing signs that bringing AI to their data may be a better move.

💡Reserved instances — transforming OpEx into the worst of CapEx? [00:30:52]

Vinay and Sanjeev discuss the muddy waters of committed spend. Vinay questions whether or not this transforms the OpEx model into the worst of the CapEx one, because you don’t own the asset while Sanjeev speaks to some of the difficulties he’s heard mentioned in properly estimating that spend, as well as how some comparisons between it and on-prem aren’t honest.

💡Reconsider workload control vs. benefiting from integrated, all-in-one solutions [00:37:41]

Using Microsoft Fabric as an example, Sanjeev explores the pros and cons of going with best-of-breed vs. all-in-one solutions, and vice versa. Like IaaS, the latter provides integrability, “one neck to choke” if something goes wrong, and cost savings. But again, you’re facing  lock-in and you’ll likely forgo some specialty database-specific advantages — so, it’s a trade-off. 

Here’s the full transcript:

Vinay: Hello, and welcome to 2024’s first episode of Sovereign DBaaS Decoded. I’m Vinay Joosery, and this episode is brought to you by Severalnines. Our guest today is Sanjeev Mohan, Principal Analyst at SanjMo and former Gartner Research VP. Thanks for joining us today.

So, Sanjeev, what’s new? 

Sanjeev: So, Vinay, thank you so much. It seems like an annual pilgrimage for me to start my year by being on your podcast, so for the 2nd year in a row, I’m honored to be on it. To answer your question of what’s new and what’s going on, I have to say 2024, just as a number, seems a bit magical.

It’s a leap year. It feels like this is a year we will really leap forward. And, although I have to say that I’ve entered the year with a lot of excitement and momentum, I’m also noticing there’s a lot of trepidation in the markets, if I may say so. 

What I mean by that is while most of the people I talk to are quite upbeat about 2024, I also feel there’s a little bit of hesitation. There’s a lot going on in our world right now, and, which we’ll talk about, but there is little bit of caution.

So, companies are saying that “we are excited about 2024, but,” so the feeling I’m getting is that companies want to take it cautiously in Q1 and Q2. So the first half of 2024. And then they think that the rest of the year should be fine after that. 

But, basically, they’re being very careful with how much money they spend and where they spend it and the investments they make, hoping that if things don’t improve, then they should be able to last the whole year with whatever they’re budgeting. 

Vinay: And in a way, it kind of sets the stage for the recap we’ll do for 2023. Actually, let me say this is our 10th episode of the Sovereign DBaaS Decoded podcast.

I never thought we’d get past three or four. So, we’re happy that people are listening. We’re happy that we’ve had a bunch of guests. And in a way, our hunch was correct when we launched the podcast.

People want to hear about data sovereignty. And during that time, like last year, even the top two hyperscalers, AWS and Microsoft, came out with their own sovereign cloud programs. So, that’s kind of interesting.

I remember when we last spoke, you were like, well, from the US point of view, sovereignty, maybe it’s more of a EU thing. But we found it interesting that all the hyperscalers actually have these sovereign clouds programs now. And probably thinking like, okay, India has their own law now on data protection, a bunch of other countries as well. 

So that was a good move actually to start this podcast, and our episodes have been downloaded more than a 1000 times. And, so now we are back with you where we started, one year ago. And, so what can we expect for today? Well, we’ll talk a bit about the state of cloud.

Sanjeev, you gave us your hunch about 2024. We’ll actually first recap the cloud and the DB space in ‘23.  

And we’ll do it in three stages: influences, events, and surprises. So we’ll see what’s going on today after that, and then we’ll look forward to ‘24. So taking the influences, we saw more instability in the world.

I mean, from the geopolitical scene, it’s just getting mad. And we also see a more restrictive business environment, which you are guessing that it will actually continue. I mean, if we thought the end of ‘22 was bad, ‘23 got worse in terms of layoffs. Inflation, central banks, hiking interest rates. 

How do you think these macro influences have affected the cloud market?

Sanjeev: I’m reminded of something I heard from some of my peers that the CEO of AWS was saying that he’s seen good times, he’s seen bad times, but these are neither. They’re uncertain times.

So that, and the uncertainty is coming this year because, first of all, in the US where I’m based, we have a major election coming up every 4 years, but we are not the only ones. There are over 50 countries. More than half the population is having elections this year. 

I don’t let these things come in my way, you know, I think a lot of these things are noise, but still, you know, people are affected. Elections, we have a couple of wars going on in the world, which every day you read, they just seem to be there’s a flashpoint and they’re expanding.

So these things cause companies to reconsider and think, what should we be doing this year? 

I know we talk about politics and these nontechnical topics, but then there’s this whole uncertainty, if you may, which has been introduced by AI. And, it’s like, how transformative is AI? 

I see articles every day, saying that AI will add every year, McKinsey says, like, $4 trillion a year to the global GDP, which will increase by 7 to 20% on an annual basis, which is a lot of potential. But then you also see that next to it is an article that says, “Is the generative AI hype over?”

So what is it? Is it over-hyped? Is it under-hyped? Or is it normal? So these are a lot of issues that every company has to grapple with before they know where to make the right bets.

That’s something which I feel is adding to a little bit of uncertainty around us. 

Vinay: And, actually, in terms of events, we can certainly say that to me, ’23 was probably the year of generative AI. 

And it helped, quite a few companies offset losses in value. If you think about some companies like Adobe, I mean, NVIDIA, it kind of pushed up all the valuations. So from that point of view, that has driven a lot.

Another event which is actually in the EU, it had quite a big impact and there were some updates on Schrems 2.

So last year there was an adequacy decision for EU to US data flows and basically it means that data transfers from the EU to the USA are not objectionable from a data protection perspective. And that’s based on an executive order of president Biden in 2022, which is called Enhancing Safeguards for United States Signals Intelligence Activities.

So what it means is we’ve done it 2x times now. We’ve had some kind of framework to transfer data, then there was  Schrems 1. It was canceled.

Then there was a new framework, Schrems 2. It was canceled. Now we’re kind of getting into this well, it’s kind of okay anyway, and now the bets are on for  Schrems 3. So it’s kind of like can an enterprise really trust that they can follow this?

Because it’s been overruled two times, and the basic facts, right, haven’t changed. In Europe, we have a very different way of looking at privacy compared to the US.  And there are surveillance laws in the US, and they won’t change those laws.

So and then there was one final thing that I thought was interesting. There were some high profile outages and security breaches, including MongoDB, which is quite a major DBaaS provider. And then obviously VMware was acquired by Broadcom. We talk about private clouds. I mean, that’s just huge. 

We saw that Broadcom ended the partner program for VMware. So a lot of partners are actually wondering, you know, what’s going to happen to us. So what are you, what’s your take on this event, Sanjeev?

Sanjeev: I won’t actually go back to AI, but another very important, from a political point of view or economics point of view, is this whole inflation, interest rates.

So to answer your questions about what’s happening on the business side, a lot of companies got heavily funded in 2021 when we had the zero interest, almost zero interest rate, going on. So now, with this inflation and all these other things we talked about, we don’t have a lot of runway. 

A lot of companies this year are going to have a rude awakening and find out that whatever valuation they had two years ago is no longer going to be the case. So if they need to raise more money, that’ll be interesting. I don’t think there’s a shortage of money, by the way.

I think LPs have a lot of money to spend. There’s just another way to spend it. And like I said, they’ve been cautious, where to spend it. So it’s not the shortage of money. It is that these companies will have to swallow a bitter pill and go in with a down round.

We might see some mergers and acquisitions going on, although some companies may be just a fire sale. So it may be an M and A on paper, but it’s really they just couldn’t, they just shut down. You know? So there’s a lot of trepidation. 

But now to go back once again to AI and the regulations, so while I know  Schrems 1,  Schrems 2, and now again to  Schrems 3, the the EU AI Act, at least a new version, because there there was already a version for many years, but the new one came into effect in 2023, literally a few weeks ago.

My personal feeling is that Europeans over index on privacy, and it’s like you said we’ve tried a couple of different versions, and we haven’t really succeeded. Now we’re trying again, but the problem is that AI is morphing and chaining faster than we can pin it down. 

So  I just feel that in a traditional database environment where things are very static or well known or very defined, like with a data warehouse, you could still have some rules and regulations in place. 

But with AI, with the speed at which it’s moving, I don’t know how privacy acts can keep up with that. Even in the US, we already saw just a few weeks ago, New York Times sued OpenAI.

OpenAI is like, no. I’m sorry. This is derivative work. This is not your original work, and all kinds of, like, arguments that are being made on both sides of the pond as to what we should be doing.

So I just feel that these ads are going to be in the process of keeping up with the advancements in AI, etc. So I don’t know if that answers your question, but I just feel that because we don’t because AI is the generative AI piece, not AI itself, but generative AI piece is relatively new. Europe needs to decide what side of the battle we need to be on. 

And I’m being very honest here. People, a lot of your followers are in Europe. They may not want to hear me say these things, but I feel Europe lost badly in the cloud business. Like, literally, they don’t even play in that space. 

It’s all American companies, except in China they have thriving cloud service providers, but Europe with all this emphasis on privacy never really managed to create an alternative to AWS’s, Google, Microsoft Azure, or the world, and they should have because privacy is so important.

So let’s not make the same mistake for AI. 

Vinay: I will totally agree with you. I think, you know, looking at market shares and everything, hyperscalers are number 1, number 2, number 3. I mean, they have most of the market.

And I don’t know. I mean, I don’t know how we got there. But for sure, AI is another space. But, again, the same as the usual suspects are, they are very well positioned to take that market.  

I mean, in terms of surprises, we looked at like generative AI, how much it grew. I mean, it had a huge impact on, for example, people like NVIDIA.  I mean, they do the picks and shovels of AI.  

Their market value tripled. Then the other thing is you don’t do AI without databases. So there are these vector databases, which we saw a lot of so there are pure databases like Pinecone, Milvus. Then you have existing databases that have their own, vector search.  For example, Postgres with pgvector.

Speaking of which, we added it to the list of extensions that we support. So for me, that was a surprise, for last year, all these, like, all these vector databases getting funded because, it seems that the database market keeps growing and growing every 2 years, there’s something new that comes in and kind of expands.  I mean the other thing was sovereignty.

So we made a bet in ‘22 that sovereignty would become a concern. But we could not predict the spate of sovereignty initiatives from the hyperscalers. And AWS and Microsoft, right, they announced their sovereign cloud programs just last year, 3, 4 months ago.

And in a way, it kind of brings confusion to the EU market, because these products are only sovereign in so far as the agreements allow.  I mean, if there’s a  Schrems 3 and it invalidates the sort of agreement that the EU made with, you know, mister Biden, then customers would be back to square one. 

And I think for an enterprise, it’s kind of strange to navigate.  But, again, there’s not like, as you mentioned, we don’t have those super superpower companies, tech companies in Europe where you can say they can get the technology from Europe itself.  

Finally, there’s one thing.

We have partnered with a partner for many years, a company, MariaDB. And that one, it’s sad to see MariaDB Inc. Is in big trouble. I mean they were listed on the New York Stock Exchange, you know, on December 22. They were trading at $11.50.

The stock now is down to 18¢. They’ve had layoffs. They’ve ditched expand, which used to be Clustrix, which was this kind of cluster database running on multiple nodes.

They had their SkySQL DBaaS. So not sure what’s next for these guys.

But does any of these surprise you, Sanjeev? 

Sanjeev: Actually, I was quite a fan of MariaDB and the work they were doing. whereas in Gartner, I’d been to their event.

They used to do very big events, and I was upbeat about MariaDB, because MariaDB deployment is actually pretty big. ServiceNow, embeds MariaDB, and they have over 100,000 deployments. 

So it came as a surprise to me that they were just saying they went on so quickly, and now they are a small shell. I still talk to them because some of the people with some of my good friends are still there, but a lot of people I know have since been let go. I now wonder if they made the mistake of going public.

They went public through their SPAC. And I think that was a big mistake. They shouldn’t have stayed private because then they could have navigated this.

Once you’re a public company, you’re exposed. Everybody knows what’s going on. So maybe that was not the smartest move. And I think going public using the SPAC vehicle to me is a little bit of a red herring anyway. 

Vinay: The thing is when they went public with the SPAC, there was a lot of bad writing about SPAC in general, about that mechanism.

I mean, it’s sad to see the company almost imploding and probably, yeah, maybe it will be picked up by some acquirer.

Sanjeev: And, very interesting to see where MySQL is going. Oracle for many, many years, tried again. I’m saying these things, I hope people don’t take it the wrong way, but I felt Oracle was in denial with the MySQL existence because Oracle has such a flourishing DBaaS market. And by the way, I started my career at Oracle, and I think very highly of Oracle.

But MySQL now has taken on a new form with HeatWave, and it’s now not just a transactional database, but sort of transactional and analytical capabilities. And one more point I want to make and then I want to hand it over to you is you mentioned about vector databases. What started as a very exciting new use case of, of a database, which is to store the vector embeddings so you can then do a vector search, has now become table stakes. 

So having vector search in a database is no longer a differentiation. The only differentiation that remains is now how you can take the vector search capability and have that workload coexist with your traditional workloads.

And this is why so many different databases now have a better search capability because they’re trying to say, you know what? You could already do an OLTP. You could do OLAP. Then we introduce full text search using Lucene, and now we’ve got vector search. 

It’s very exciting in my opinion. 

Vinay: And in a way, I think it follows a little bit the trends that we’ve seen. I mean, we talked about PolyClot persistence. Like, we’ve talked about that for, I don’t know, since 10 years ago. And then what we saw was that suddenly people started adding maybe a key value interface. Maybe they had graph capabilities.

And then the question is, what does the enterprise do? Do they continue maybe selecting specialized databases? Because then they’re going to end up with a lot of databases and that maybe it doesn’t work too well. 

Or do they go multimodal? Because the Postgres that they’re using or their Oracle already has all these capabilities. So I think that’s something that even we’ve seen with the graphs and other news.

Sanjeev: So, I want to add two points. So first of all, I completely agree with you. In fact, if you read between the lines of what I just said, multimodal databases, in my opinion, are the way to go. 

But the second point I also want to make is that it’s the same as true for graph databases, but there are a couple of people like Neo4j, for example, that are done very well in graphs. So maybe there’s an opportunity for just a handful of vector database stand alone products to do really well because they do things so well.

For example, Pinecone is a leader in vector databases in my mind from the commercial point of view. There are many others or, like, open-source like Milvus, Qualcomm. There are a lot of really good options. Some are even embedded. But let’s start with Pinecone.

I saw their announcement, literally, a day ago where they talked about some new capability that could reduce hallucinations down to 0 when you build your RAG pipelines. So if they do that, then that could be a game changer. 

So having a stand alone database actually may not be such a bad idea if you can introduce capabilities that are far beyond what you would find in a highly integrated database. 

I think well, another point I want to make, by the way, talking about Pinecone, is last year, Snowflake introduced this whole new engine called Snowpark container services. So on the same data, you can now, you don’t have to use Snowflake’s native engine. You can use a containerized engine where you can even pick and say, I don’t want to run it on CPUs, I want to run it on GPUs. In that container, you could run Pinecone.

So interestingly, what happens is that now I can take my data in Snowflake, turn that data into vector embedding, and store it in a containerized Pinecone all within my VPC. So that’s another interesting point I wanted to raise.

Vinay: I totally agree with you there. There are capabilities that, obviously, if you are specialized in doing vector search, then you’ll have more time to add more things. 

Whereas Oracle or even Postgres, they have so much to work on, so that’s tough. So looking a little bit at these influences and events that we talked about, right, for last year, I mean, the question is you mentioned Q1, Q2, maybe companies will be in a little bit of a kind of waiting mode because, there’s so much insecurity. 

Now my question to you is in terms of cloud strategy.  I mean, our company is transitioning from cloud first to cloud smart?

Will hybrid become more of a viable option? Because we know that, public clouds are very expensive. And as you mentioned in the previous podcast, it’s not like you have very variable kinds of workloads.

What are your thoughts there?

Sanjeev: I think hybrid is coming back, to some extent. I worry about saying that that does not mean things are moving too on premises. We will still seek out migrations, but it’s not gonna be the winner takes it all, everything must move to the cloud. 

On premises, deployments are actually starting to look pretty attractive because some of these things that I talked about, like containerized services, some of the things that we put on a cloud native are now available on premises. 

So I now have an option where I can take the latest and the greatest binary from the cloud, but run it on the data that sits within, my safe and secure or seemingly safe and secure on premises, environment.

So if I have happened to be in an environment where I am very skeptical about moving to the cloud for various reasons, security being one of them, but it could be latency, it could be regulations, I can now take advantage of the cloud operating model on premises, which, you even see that in, announcements from Dell, for instance. 

Dell with RedHat, OpenShift is now saying that we give you the entire AI ecosystem so you don’t have to move your data to the cloud. The second point I also want to make is that one lesson that we are learning very, very quickly is that taking your data to AI is not a good idea. What is good, what is a better idea is to bring your AI to data. 

If you are going to train a model, which is still in early stages of an end user training their own AI model. We are still doing right and some fine tuning. But if NVIDIA Grasshopper becomes really cheap and Intel, AMD, Qualcomm, ARM based, all these become commodity for training and inference, then a lot of these workloads will move to the edge or to on premises because now we have because the cost has gone down. 

In such a case, we want to move AI to where the data already exists. You know to train an AI model, we need a lot of data for it to be of value. Where is that data?

If it’s in the cloud, okay fine. Train it there. But if it’s if it never moved to the cloud for whatever reasons, let’s bring AI to where the data is, train it there. And once the model is ready, if we can have a smaller model, I can even put it on an edge device. Like Androids, I saw another announcement just yesterday how Samsung and Google Cloud are now collaborating to run some of Google’s models on these Android phones. 

Vinay: We talked about costs. Cost is kind of a big driver in this. I mean, one way that those typical hyperscalers are kind of optimizing the cost is through these multi-year contracts.

So this means committed spend, which seems closer to the CapEx model than the Opex one. But you don’t own the asset. 

So if you have the committed spend, because that’s kind of what the enterprise can say is, like, okay. Let’s do a contract with Hyperscaler A, and then we’ll commit to using, you know, x amount of $1,000,000 or whatever, right, in the next 3 years.  

And then you got to pay for it even if you don’t use it. 

But then it feels a little bit like it’s a bit of a CapEx model. But then you don’t own it. You don’t own the asset. 

So because it’s not an operational expense that you can actually just turn down, right, if your company is trying to cut costs. So, I mean, I don’t know if you have any experience in this, but what are the implications for companies who got themselves in these contracts? 

Are we saying we chose cloud first as our direction, and we’ll just continue down that road, right, understanding that it costs more. Or, as you mentioned, since we can do the cloud model through our own environments, right, we have the cloud operating model.

We have all the tool sets today to do that. Are the enterprises going to look at that and say, okay. We signed that contract, but we have to start looking at alternatives.

Like, you know, you mentioned Edge, right, on prem colocation,  managed service providers, or any other alternative environments. What do you think? 

Sanjeev: Reserved capacity is a conundrum. But when you don’t have too many choices, then okay, fine reserved capacity is how we saved cost. If you’ve reserved our capacity for 1 year or 3 years, then we got a favorable pricing. 

But now I’m also starting to hear how difficult it is to estimate what that reserve capacity should be. For example, many of my clients will tell me that I had expected to spend x amount of money over 3 years, but with these new use cases, I’ve already in 6 months, I’ve already spent that money. So I’m running hot on my budget. I’ve exceeded it. 

Some companies tell me that we had excess capacity available. And if I’m not mistaken, I don’t track this space very well. So, Vinay, remind me, I thought if you had excess capacity, you could make it available on the marketplace, but I think AWS has shut that down recently. 

Vinay: Yeah, I’m not, I’m not sure. What I know is, I just read in the news that some large company had bought a lot of capacity. And then, they didn’t use it all, but then they actually managed to get somehow a discount into their next contract so that unused credit was kind of moved into the next one. 

Sanjeev: What really amazes me about reserve capacity, very interesting case, slightly tangential to what we are discussing, but still very relevant. I was looking at some benchmark numbers where somebody had compared the cost of running a workload on premises with a cloud, but then when I dug deeper, I was like, wait. The cloud cost that you are comparing it with is a reserved instance cost. 

It’s actually more than three years. So you cannot take that number because you’re paying for three years, so you cannot take that number at a point in time and then compare it to on-prem because that’s already discounted in the cloud. And so people are playing all kinds of, like, interesting tricks with this concept. 

Vinay: In a way, if people are moving more towards diversified environments, right, that will take care of vendor lock in.  Most of the analysts are kind of talking about the blast radius of potential incidents, right, there’s more vendor lock in, you have potential large scale events that can affect a lot of companies, and also to improve navigability of the regulatory landscape.  

Because if you are in control of your environment, it’s not just cloud first, but you can decide where to deploy things based on regulation, then you have more control.

We’ve seen new data protection laws, like last year, India, Indonesia, Saudi Arabia, Vietnam. Have you seen any effect on the data protection law in India, for example, on all these enterprises doing business? I mean, maybe it’s too early to say. 

Sanjeev: I think it’s too early. If I’m not mistaken, in India, it’s still an act. So I’m not sure if it’s already a law…

Vinay: It’s a law. It’s been a law since last year. I think since October. They tried, they tried for several years, and they didn’t manage. But then last year, it became a law.

So looking forward to ‘24, right, what can we expect? We talked about more data protection regulations. We talked about, we can expect governments expanding, privacy regulations based on the fact that the three main hyperscalers are now offering, quote, unquote sovereign clouds. I mean, is that evidence that regulations have some teeth to them?

Sanjeev: I see GDPR finally is starting to show its teeth after many years of not being that proactive. But now I see that that is happening quite a bit. 

Talking about what’s gonna happen in, in near future, I have to say, we are seeing a very interesting trade off between having control over your workloads by bringing in the best of breed tools versus going to a cloud provider and taking their integrated offering where you don’t have full control, but you get a few things in your favor.

For example, Microsoft last year introduced this concept of Microsoft Fabric. I feel that Microsoft Fabric is made of large pieces. You know, the database, the application environment, ingestion, ETL, data transformation, and catalog, a lot of these things are plugged in. What I’m starting to see is that a lot of customers in the current economic times, where there’s a lot of pressure on containing cost and accountability are saying that how do we simplify our technical infrastructure? 

Do we really need to integrate the best of breed products, which actually provides very deep, technology advantages, but I have to pay to integrate these?

And then if something breaks, I have no idea. Did it break in Fire Trend? Did it break in DBT, in Looker, in Snowflake? Why is my dashboard showing me the wrong information? So if I go to Microsoft Fabric, then maybe even from a regulation point of view, it is one neck to choke as opposed to five different ones.

So it’s a trade off, but then welcome to where they’re locking. 

Vinay: I totally agree. I mean, it’s probably simple as you say to get a platform where you don’t have to take the cost of integration. It’s the vendor that has already taken the cost of the integration, and you’re getting it packaged, so to speak, and then you have this one neck one neck to squeeze. So looking at the economic environment, I mean, you mentioned it earlier on this call. 

We had big investments in tech during the COVID years.  Then after that, companies started to cut down on costs. ‘23 was a tough year. And the question is, will ‘24 be better?

You sort of mentioned Q1, Q2. It’s kind of wait and see, and then we’ll see what happens. But let’s say if you would look further for the whole of ‘24, are we looking at a repeat of 23? Because I guess we will continue to suffer the effects of high inflation. How does this affect cloud spend?

And will you know, by the end of the year, will enterprises have diversified their cloud diet, right, being cloud smart as opposed to cloud first? 

Sanjeev: I don’t see a repeat of 2023. I have very high hopes.

But then my wife called me an insufferable optimist. So this is the reason why I feel 2024 will be different. 2023 was driven by all these geopolitical things we talked about and the financial market. 

At some point, we have to put a kibosh on optimization and start innovating. We cannot just keep optimizing forever.

I feel in 2024, we will see a lot of consolidation happen. Some companies are going to suffer. You talked about job losses. A lot of job losses took place, but then we also overhired, and we had unrealistic expectations.

So there had to be some rationalization of, workforce and I’m I see bad for people, when that happened, it’s even happening in 2024. We’re only 2 weeks into this year, and we’ve seen job losses at Google Cloud, at Amazon, Cloudflare, a bunch of companies, lately. So it’s still happening, but that’s because of this uncertainty.

This is a year when we have to say, okay, we’ve optimized, we’ve cut it down to the bone. Now how do I get more competitive advantage? I have to invest.

And maybe AI is a vehicle that is used to invest, but we’ll turn it around. It’s my sincere hope that this year, we will see more investments because techno we cannot stop technology from marching. 

So all those people who are saying that AI is going to take over our job, AI is going to take over mankind, and EGI is going to subjugate us. I mean, please stop. We don’t know.

Let’s just use AI for the good it can do to our organization. There’ll always be bad actors out there. No matter what is a new technology, we cannot stop it. 

But this is a year to take a hard look at AI and see what are the use cases that I can put it, to make my business more efficient or to improve customer experience or to improve developer productivity. There are just so many there’s no doubt of places where AI can help.

It’s not perfect. Generally, we have been seeing hallucinations. Things will get better just like any other technology.

I’m telling you, 1994 when I first got into the World Wide Web, the whole thing was just coming into existence. People were complaining, well, what the heck is the World Wide Web? It’s just static web pages. It’s just documentation, and look what we can do now on the Internet, pretty much everything. 

So AI will get there. And by the way, we know that with every new technology, the runway to that level gets shorter and shorter from radio to television to the Internet to one word web to now AI. So in another 3 to 4 years, some of the things will be embedded with AI, and it’ll be table stakes. We won’t even think about it.

Like, we don’t think about going to a bank in the middle of the night, which you cannot, to withdraw money. We just go to an ATM machine. It’s just a no brainer. 

So I think we’ll see more from the start of 2024 onwards. 

Vinay: And, actually, based on what you said earlier, if we’re looking at AI being a driver for new investments, then you take AI to where the data is.

It’s not like you’re going to shift and lift to somewhere else. So, basically, if you have your workloads in your on-prem data centers or whatever, then probably you’re looking at augmenting that environment with AI capabilities and getting the benefits of it.

Sanjeev: Correct. 

Vinay: Well, Sanjeev, this is great. Time to wrap up. I mean, it’s always great to have you with us.  So, we’ve looked at 2023, how it has been tough economically. 

And probably much worse was it not for AI driven workloads. And then it remains to see whether enterprises will avoid concentrating their workloads on the hyperscalers, or maybe adopt a more sovereign approach. 

I mean, maybe this taking AI to the data would also match the sovereign approach because, I think I saw somewhere that only 50% of workloads are on the cloud, so there’s still most of it is not in the cloud. 

Sanjeev: I think it’s less than 50. It’s reaching equilibrium of 50%, but I I think, again, it’s very hard to pin it down, but I think, from what I see, like, in the forties, 40 percent rate, 46% is one of the services I saw. And, unless you talk to AWS, they’ll tell you it’s only 15%.

Vinay: Yes. Well, thank you for joining us, Sanjeev. This was very appreciated, and thank you all to our listeners for joining us, and have a great 2024.