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Build Fast, Scale Faster: Milvus vs. Zilliz Cloud for Production-Ready AI
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Build Fast, Scale Faster: Milvus vs. Zilliz Cloud for Production-Ready AI
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Trying to decide between managing Milvus yourself or switching to Zilliz Cloud? You’re not alone. Join Chris Churilo and Jay Byoun for a candid, technical discussion on when it makes sense to stick with open-source Milvus—and when it’s time to move to the fully managed Zilliz Cloud.
We’ll cover:
- Real-world scaling lessons from teams using Milvus and Zilliz Cloud
- How to evaluate tradeoffs between control, performance, and ease of use
- Benchmarks on cost, latency, and throughput at production scale
- What it takes to migrate from Milvus to Zilliz Cloud (and what you don’t need to worry about)
- Key Differences between Zilliz Cloud (dedicated) and Zilliz Cloud BYOC (deployed in your own VPC)
- When BYOC is the right fit, especially for regulated industries, data sovereignty, or cloud specific compliance needs
Whether you're building a GenAI app, scaling vector search, or rethinking your infra strategy, this session will help you make an informed decision.
Speakers: 🔹 Chris Churilo – VP of Marketing, Zilliz 🔹 Jay Byoun – Solution Architect, Zilliz
Who should attend: Developers, ML engineers, architects, and tech leads working with vector search, embeddings, and GenAI workloads.
0:01 thank you very much for that introduction Chris uh welcome to everyone who has joined the webinar or 0:06 watching us on a recording uh thank you for joining uh we're going to cover a few of the topics like we have been in 0:13 prior months uh giving you a little bit of an overview of our uh Zilla's cloud offering uh and I'd like to go through 0:20 an illustration or a demo and how you can get more help these the series of 0:27 cloud uh monthly technical review uh recordings and webinars are intended for 0:32 a technical to semi-technical to highly technical audience but covering different topics that are of interest to 0:39 developers uh administrators is where we are trying to keep our focus so uh with 0:45 that uh my expectation our expectation for uh those who are joining is that you 0:51 are somewhat aware of vector databases geni use cases vector embeddings and how 0:58 to use you know other database uh vector database products uh there are many in the market uh we are the uh uh creators 1:06 of milvas the most popular open-source database and when we offer that as 1:12 software as a service on the cloud uh we're a company named Zillas so what is 1:18 Zillas cloud um we are uh the the software as a service version of Milvvis 1:25 the most widely adopted vector database um if you are 1:30 a startup or a well-established company or just coming into the into the foray 1:36 of Gen AI use cases you're going to realize pretty soon that a vector database is your friend um and based off 1:43 of that when you can index uh vectors uh that are generated from uh encoding 1:50 models you can then do semantic and similarity search so those are the fundamental ideas behind all of this and 1:57 what we offer is mil zillus cloud and zillus cloud bring your own cloud uh 2:03 that's a service where you wish to host your own hardware on the public cloud 2:09 and we run a few agents and collect telemetry and give you controls on our 2:14 control plane for you to administer the um Zilla's product 2:22 just a little bit of a basic background um vector search is most uh applicable 2:30 where you have content that is unstructured where things are scraped 2:35 from a PDF or a web page or XML files that are converted to PDFs in it you 2:42 have things like uh titles pictures timestamps and they're all meshed in 2:48 together and a lot of the content is just text it's diagrams there's no real 2:54 structure there's no dictionary so to speak and using different uh encoding 3:00 models you can take all this content and you can break it up make it a little bit 3:07 more structured however wherever you have content like a Wikipedia page you 3:13 can index that as a as a vector field and then allow your users to go search 3:19 on it one of the very well-known uh use cases for this is rag retrieval 3:25 augmented generation and we power that using our MILV kernel uh on the Zillas 3:31 cloud so what separates Zillas from Milvvis is 3:38 that we have something known as the Cardinal search engine which is constantly under development it's 10 3:43 times faster than MIV it has better indexing better quantization and uh it has something also known as 3:50 autoindexing uh which sort of obviates the need for specifically selecting an 3:57 inverted file index and having to select your quantization it does a lot of that behind the scenes for you we're a 4:03 cloudnative database so all the scalability all the management is handled for you you don't have to worry 4:10 about running a Kubernetes pods um or running an uh elastic cube cluster or 4:16 any of that we handle all of that on the cloud and it's enterprise ready in the sense that we will give you role-based 4:22 access control private link security we're socku compliant so and there's a 4:28 company Zillas uh backing you for the um you know all the SLAs for support and 4:35 making sure that any tickets are resolved on time i don't want to go too deep into the 4:40 Cardinal search engine we have other items on the agenda but it's important because a lot of our customers come to 4:47 us talk to us because they're really impressed with MILV they want to run mil on many machines they want replicas they 4:54 want a lot of scalability but when they come to the Zilla's cloud uh they get the same API functionality they get to 5:01 use the same APIs uh for REST uh but they also get this autoindexing 5:07 algorithm we have better quantization uh it's in the cardinal search engine it's 5:12 faster we outperform Milvvis by 50% capacity increase um and we get up to 10 5:18 times uh performance boost so moving off of MILV coming to the cloud version of 5:24 Milvas which is Zillas uh actually has a benefit not just in operational 5:29 simplicity but also having better cost uh profile uh for your admin team 5:37 we're enterprise ready we have security we are on three public clouds uh very 5:42 resilient so we are able to do replication within a region um we allow 5:47 you to have bring your own cloud so everything that large enterprises and 5:53 startups that have seed funding or first few rounds of funding they can they can 5:59 alleviate any fears uh they might have about running something all on their own we have physically isolated and 6:05 dedicated clusters uh no direct VPC access IP allow lists and even SSO 6:12 support we plug with uh plug in very nicely and play with Microsoft enter ID 6:18 um technical support is included in a subscription uh you can have multi-year commits that's in the realm of sales uh 6:26 fully managed service benefits and SLA commitments and guarantees now we get to 6:31 the fun topic and fun part of this uh June uh monthly webinar uh there are a 6:38 couple of things handful of things that I have decided would be very good for me to cover uh since in the last 24 hours I 6:46 have some test harness code and I want to talk about our scalable architecture 6:53 i have a cluster that I created and it's been running for about uh 24 hours and 6:59 if we look at the last hour I have been throwing a lot of data at it uh my test 7:06 harness which I have set up in my development environment 7:13 as you can see here this is Visual Studio Code and I had this code 7:19 written so we can write a 100,000 vectors at a time 5,000 batches with a 7:25 certain number of workers and we connect to uh my running instance and I will get I will cover all that um I have 7:32 saturated this what I want to talk about is the fact that you can have a cluster and you can control how you uh you know 7:41 the capacity beyond which it's going to scale all on its own and I want you to notice how linear this is we don't ask 7:48 you to double the capacity now a little bit of background around this a CU is a 7:54 compute unit and there are different types of compute units we have a performance optimized capacity optimized 8:00 and extended capacity and the this is the order in which you allocate the compute units you get the most bang for 8:07 the buck the fastest performance with performance optimized uh we price our vector uh in the size of 768 and I'm 8:16 using 372 dimension vectors so I can saturate this cluster faster now note 8:21 that you don't have to pay for double the capacity you can go up two CUS at a time in other words these are compute 8:27 units and and you you pay a little bit more for for as you go up uh by the hour these are dedicated clusters as opposed 8:34 to using serverless uh where you don't get the same level of performance 8:39 consistently throughout the operation of your cluster um right now I have a 8:44 cluster which I started off with one CU and I saturated it enough uh such that I 8:50 was alerted by the system you can see this thread and I kept writing data to 8:55 it and it automatically moved up to 2CU um now I continued to write the data and 9:02 in the last hour if we look at the metrics um this is the last 10-minute chart because I 9:09 haven't really been doing anything in the last hour I've really saturated this but you can see that it goes up and down 9:14 up and down that's because various services that are ingesting the data are also then running compaction building 9:21 the index so it requires that those services be activated as we ingest as we 9:26 build the index and then as we release memory uh we are able to alleviate the pressure off of the cluster what I'm 9:32 going to do is have my test harness run the next batch of inserting more data 9:37 and I'm now going to allow for this cluster to have a relief valve i'm going to say you know what you can scale up to 9:44 four CUS once you hit this capacity threshold which is a very simple calculation around memory so we're going 9:51 to come back and revisit this but this is our very powerful automatically scaling u cluster ability and this just 9:59 tells you what's going on and um uh we we set the autoscale over here there is 10:05 slight jitter when this cluster does upscale and you are notified by email 10:12 uh when this when this happens so I did receive a couple of alerts we're going to revisit this for sure um 10:20 we're always also moving on we improve uh how we have our our our UI i'm really 10:28 proud of the fact that our engineering team um just going to close this tab here 10:34 and I'm going to cover intuitive collections UI you see unlike other 10:40 databases uh and some might have this feature when you create a collection 10:45 when you operate this database this entire user interface acts almost 10:50 like an integrated development environment you don't have to run everything with pias of course while we 10:57 do have an API playground which lets you administer this database um you can create new partitions inside collections 11:04 you can create new indexes uh drop collections describe collections This is available but this still has a 11:11 programmatic component to it if you are getting started with vector databases and you want to operate things in a in a 11:18 very free flowing way you just want to explore things this is one of the best UIs around 11:25 now you can see that I created this uh vectors 372 this is the collection that I'm using uh 11:32 for my cluster to to saturate it and uh right now I am at 86% capacity but I'm 11:39 allowing it to go up to four we're going to revisit this if we take a look at this create collection capability we've 11:45 made some improvements over time but at this point where we stand you can actually create a brand new collection 11:52 inside of a database and if I create a test 02 collection I can give it a test 11:59 data for inference or test data for new model selection 12:06 and here uh right here we give you a lot of flexibility we also have a very 12:12 friendly UI very intuitive UI where you can hover over fields to find out uh the 12:17 kind of capabilities that we provide you can also have your primary tree automatically generated and we require 12:24 at least one vector field and you can select the dimension i'm going to put something that's sort of middle of the 12:30 middle of the pack 1536 there are uh models that work with smaller dimensions 12:36 also and recently what we have done is given you the ability to select full 12:41 text search why can't I click on full text search that's because I don't have 12:46 um a varchar field in other words the collection requires that if I have a 12:52 description of let's say a product catalog and every product has a description I can give it a varchar 12:58 field and as you can tell with every field that you add um what you can also add is a 13:05 corresponding uh attribute for which you can provide a value so in the case of a float vector you have dimension in the 13:11 case of varchars you have max length i'm going to say that well perhaps uh 4,000 characters is is is a good description 13:18 now let's go forward and uh provide a description desk vector 13:26 and without going into too much detail I'm going to talk about you know uh sparse float vectors when you have text 13:32 when you do want to do semantic search you use dense vectors when you want to do a a more of a lexical analysis like 13:39 in the lucine library we use sparse vectors because the the uh we have that 13:44 many more uh indexes in the array that we want to have positionally what is 13:49 active because we cannot have you know a zero for for all of the positions for 13:55 which we don't get a term um as of late what's what's become very popular is the 14:01 BM25 function and that's what we support a function is used in full text search to convert tokenized items to sparse 14:07 vectors with relevance scores if you remember how the page rank algorithm was developed it was about relevance scores 14:12 for a page how many other pages led into it based off of search terms bm25 is 14:18 another score almost like TF term frequency inverse document frequency and 14:23 I'm not going to go too deep into this uh rather involved theoretical topic i would encourage uh anyone watching this 14:30 or live here to please uh take a look at that but we can now add full text search 14:36 based off of the description this is the score and what sparse vector field and 14:41 it's done immediately you don't have to do anything programmatically you can start using full text search after this 14:47 uh with the examples that we provide in our uh on our website so I can save it 14:53 and this is a very powerful feature i feel you can do a lot more uh with Zillas and I can now create this 14:59 collection um I'm not insert interested in inserting data 15:05 so that was a little uh a little uh demonstration I wanted to do what I 15:12 sorry about that what I also want to highlight as we were looking at my test 15:17 harness which is now done inserting more data into this is if you look at this URI my endpoint on the vector database 15:25 and I've got this token which I'm going to invalidate as soon as this call is over um 15:31 we get this information from here uh this is not collection specific or database specific this is uh the cluster 15:39 URI and the token then uh empowers you to connect to the cluster and create collections create indexes insert data 15:47 if you notice this this is a secure endpoint um our driver the PIM Milvas 15:54 library uh handles this for you if you've also been uh testing a lot of the 15:59 code uh outside of our cloud environment that is maybe in Milvas you just have to 16:04 provide a new URI and new token and everything is going to work just as it 16:09 was before all the insertion all the search all the hybrid search 16:17 excuse me so this uh leads us into talking about some security features 16:23 that we provide for you on the cloud i'm not going to go into the mathematical or theoretical explanation of how 16:28 everything is built but the important thing to note is we support private endpoint and you can set this up as long 16:35 um as you are uh able to do so uh I chose uh Google cloud to run this 16:41 cluster but we also support this on AWS um and Azure the IP access list is 16:48 something that is very very powerful in when you have a VPC uh sorry VPN 16:54 connection uh or you know the specific set of IPs in your organization so we have allow lists uh with which you can 17:00 do this of course I want to be able to reach this cluster from anywhere but you can add uh a cider notation IP address 17:07 and we will uh save that for you and that becomes uh you know a sort of a a 17:13 gateway to getting into this uh there are other uh safety mechanisms uh the 17:19 other one is we have TLS by default which I talked about there's IP allow list private link we allow and arbback 17:24 with privileges arbback stands for uh rolebased access control it's a very 17:29 standard concept uh almost all databases have it and u first you have to of 17:35 course understand the privilege model and this is these are our privileges it's been very well thought out uh I'm 17:42 very impressed and proud of our engineering team for how they've they've gone about um the kind of privileges you 17:48 can have tied to a specific role um within the system so if you have a 17:53 readonly privilege what are you allowed to do well you can see this checkbox you cannot create a partition you cannot 18:00 drop partitions so obviously you can't change anything of course when you're the admin you can do pretty much 18:05 anything uh we have database level permissions and we have cluster level permissions so as you can see with 18:11 readonly you can do only so much with read write you can maybe do a little bit more and with cluster admin you can do a 18:17 lot more um so as far as if you are a large enterprise or small enterprise you 18:23 have PII data you don't want people to be able to uh you know uh get in and and 18:30 make changes to the data you can uh use the privilege model and let me just 18:35 cover something very briefly users and roles are at not necessarily any one level um sorry 18:44 uh the roles I can create a new cluster role and assign privileges you know very 18:49 fine uh grain permissions especially on on this so so collections uh you can 18:55 select well first you select the database and you can select specifically on which collection you want to provide 19:03 what what kind of a uh what kind of privileges so we have a very rich UI now 19:09 there are database systems in which you know you have to do this using a SQL like language um you know describe roles 19:16 assign XYZ type of role with specific privileges to a uh uh to a securable 19:21 type object we allow you to do this of course very much using our API playground but also from here 19:29 all right moving on uh I just want to talk a little bit about what recently we 19:35 have added to the to the cloud side of things and a real brief note and I will belabor this point um whenever we 19:42 release a new version of MIV we allow it to battle harden we make sure that our 19:48 user community can download it test it provide their feedback and only then do we bring uh those features and those uh 19:55 improvements into the cloud we don't want our existing customer base and new 20:00 customer base to be surprised by any kind of issues tickets that that go unresolved um so while I will cover some 20:07 of this we will not be hosting uh Milvas 2.6 on the cloud uh because you can see that if I was to create uh a brand new 20:15 cluster um a dedicated cluster um you know it's it's actually going to have um 20:22 version 2.5 which is our latest release i did that yesterday for this cluster 20:27 and uh it's Milvas 2.5.x we give you the latest software uh we don't force you to 20:32 upgrade um but we don't uh prematurely release anything onto our cloud offering 20:40 so now that we've been talking about uh scaling let's just let's just go back very quickly and see where we are and as 20:47 you can see in the last hour uh we we were at very high capacity 20:53 right we were up to 86% but that suddenly changed why are we down to 36 21:00 that's because the cluster automatically upscaled to four compute units and let 21:05 me see if I received I did receive a notification about it 21:11 okay this is the prior one in the morning and this is just now while I was speaking uh with this audience 21:17 so this is our very highly scalable automatically we manage things for you 21:22 type of architecture of course it is going to come with a higher cost but my hope is that if you're ingesting more 21:28 and more data uh you've got a a growing business and we congratulate our community for that all right so let's 21:35 talk a little bit about recently added features i've picked these up from our uh documentation around release notes uh 21:44 it's simply you know every quarter um we improve 21:49 uh the product we we have some more release notes you can go find them yourself from a a simple search engine 21:55 uh search and I picked these up some of these from here i think they're they're worth a mention uh migrations are very 22:01 important uh to our users this is private preview but this is something very important to our user community um 22:09 I'm not going to go too deep into this uh I can cover other topics also but 22:14 what we allow uh is uh that without having to bring down your cluster or 22:20 stop uh taking rights you can do uh direct data transfer there are some limits here uh what are the 22:27 prerequisites what is the mechanism it's all documented um and if you want to be 22:33 able to do it we have uh very simple you know instructions and some illustrations 22:40 on what you have to do step by step i've been really impressed by this and you know our users are asking more and more 22:46 about it and then starting to use these new features how you can monitor what's happening 22:53 and by the way um this is you know an asynchronous process so while it's going on you can actually watch it which is 22:58 very similar to how we allow you to do bulk import for very large terabytes and pabytes of data you can kick off a 23:05 process and then take a look at what's going on and we do you know um have various 23:12 stages there's you can monitor sync lag you can of course stop the data sync and then the phase three is uh you you 23:19 switch to your new collection uh to a new cluster 23:25 another feature that's been around for maybe a couple of months is uh this alerting now the alerting part that's 23:32 improved as it's it's very much policy based and what I can show you here is that we have project alerts so if you 23:41 look here we've got a bunch of projects and each project allows you to have multiple clusters so if you're testing 23:46 if you're if you're just developing if you have uh user acceptance testing if you have data that you know is has PII 23:53 so you're only playing with uh very um you know insecure data or data you source and you can have a separate 24:00 project for that different users can be invited to those projects an alert allows you to say well I have an alert 24:07 about uh you know something concerning and the metric that you choose it's 24:13 quite flexible is uh bulk write QPS uh cluster write performance you have to 24:19 have an enterprise subscription to be able to use this and you say well once this reaches over this threshold or it's 24:25 below something and the duration is maybe a minute um then based off it 24:30 picks up all the clusters these are all the clusters in our in our project but you can take out clusters and it will 24:37 email uh people that are within my organization or by by um by a role uh 24:44 this is not a group this is a role and there are so many different touch points by which you can actually hit someone 24:49 and say "Hey I have something to tell you about." Um this is a very awesome feature that I'm hoping more and more of 24:55 our customers will adopt and be able to self-manage a lot of the uh uh at least 25:00 uh you know be able to capture uh what they want to in terms of alerts and management 25:07 and the next one which has been around but now we only we support uh BYOC this 25:13 is going to be a huge driver for our business it's going to be huge driver for our uh community it's going to empower a lot of our customers to be 25:20 able to have the kind of control they want over the hardware when they utilize 25:26 the software as a service part um of our product and host their own machines be 25:32 able to select EBS profile i mean EBS is elastic block uh you know uh hard drives 25:38 that you attach but it can happen in any cloud uh we support deploy this by on 25:44 GCP we give you uh the Terraform scripts and uh all the prerequisites that are 25:51 required for you to have virtual machines that are provisioned by yourself and how you can connect and 25:58 have a control plane such that the machines and all the resources can 26:03 send telemetry and alerts and everything uh to the Zilla's cloud plane by the way 26:08 when I say that word cloud plane I'm talking about about this part this is how you control 26:16 um the Zilla's software executing clusters the services um and with BYOC 26:23 you know right here this cluster is not BYOC so I cannot I'm not in control of the hardware all I can ask for it uh is 26:30 to is to scale up so we enable this and because this is in this entire section 26:36 here we have deploy BYOC on AWS this covers a lot of ground as time goes on 26:43 we will be making this self-directed uh for our customers to do this on even 26:48 Azure down the line and AWS so there was a time when we had to work very closely 26:54 with our customers and their administration team and their development uh team uh to do a lot of 26:59 the work for them this does require higher uh upfront commitment in terms of the investment that our customers make 27:06 with us uh but once that partnership is established uh you know we have provided 27:12 you with a lot of resources on how to get started and and be in control it's it shows the level of maturity that 27:20 Zillas has uh uh you know reached in enabling uh vector databases as part of 27:28 your integrated genai solution and many of our competitors are actually behind us in this capability 27:34 um so one last point I wanted to make which was about Milbus 2.6 is we only 27:40 have a release candidate one we're not going to make anyone a guinea pig we're going to have our user community and 27:47 internal tests run through this but what's coming is absolutely state-of-the-art we're going to use 27:53 Woodpecker for uh the write ahead log that's what W means uh there's going to 27:59 be much better ingestion and streaming we're going to merge some of our services we're not going to run as many 28:05 different uh pods in the Kubernetes world it's going to become one coordinator and one of the most 28:12 important things we're now releasing a rabbit quantization a one bit quantization and of course our customers 28:19 on Zillas Cloud could choose this but they don't have to it's going to be a part of the autoindexing um we are 28:25 releasing this uh pretty much this documentation is like a blog post very theoretical and how this exactly works 28:32 is something you can find out about of course with YouTube videos um but as far 28:37 as uh enabling this is concerned this will be ready for you to to to enjoy and 28:42 get uh cost benefits performance benefits out of uh this will change we're going to be adding more uh as 28:49 version 2.6.1 and 2.6.2 come out we were going to battle harden things uh there's going to 28:55 be phrase matching um but of course we can always do another webinar on all of that right about now I'm at time i thank 29:03 you for joining and letting me walk you through some of the arbback security uh 29:08 scalable architecture and other features thank you very much that was really great Roit and so where can they um 29:16 reach out to you like where do you like kind of hang out i hang out at Zillas my 29:21 email address with my first name as displayed.ast name atzillas.com 29:26 uh we have uh quite a quite a how can I say very competent and smart team we 29:32 have a sales team you can send us your questions don't be shy about anything there are no no stupid questions with us 29:38 uh we can educate you about vectors vector databases mil running mil uh by 29:44 yourself on your laptop um how to set up IDEs i can help you with all of those uh 29:49 different topics so you can uh you can find roit in our discord channel you can also just reach us on our website we 29:55 have a contact sales form we also have uh free office hours that you can set up sometime so lots of ways to be able to 30:03 reach out to uh RoIit and he can uh further walk you through uh all these capabilities when you're ready to get 30:09 started with Zillas so um we will uh pres we will make sure that we um 30:15 provide all these materials to everybody at the end of the session but before we sign off RoIP what is like some sage 30:23 advice you have to give to people when they get started you cover so many things so maybe like one or two points 30:28 that you'd really want to leave our audience with if you have a bonafide use case and you 30:36 are a business you're a startup uh you ask me what can they do to get started I would say just get started uh don't be 30:42 don't be uh shy about reaching out to us if that's what you need start with Milvvis if that's what you need of 30:48 course I think that Zillas is a much superior enterprise product for you to actually run your business um and any 30:56 resource we have on our website is for you to consume uh start there uh find 31:01 the blog posts find YouTube videos uh talk to us and land some data find the 31:07 data that you want to work with and be ready uh to to upload it um you know we 31:12 have migrations if you are already running one of with one of our competitors if you're running with Milvvis you can extract from MilV you 31:20 can download backup files by using our MilVIS backup tool and just simply upload it we'll create a collection for 31:25 you and guess what one of the most amazing things about MILV and Zillas is we didn't create a new library for you 31:32 to use i apologize we you use the same library the only thing you have to do is 31:37 change the endpoint URL provide us with a token provide us with a token and off you go all of your code is going to work 31:44 exactly the same that's right you always want to just be able to write once right and then not have to rewrite anything 31:51 right and if you use uh you know uh Python Golang it's once you write the 31:56 code it's going to work against Milvas it's going to work the same in in Milvas uh sorry Zillas on the cloud and BYOC so 32:04 get started get some data up and up and running um and if you are just learning 32:09 we also welcome you into the the Genai community and the vector database community there are so many different 32:14 resources of course hugging face is the is the best known one from where you can get models and data sets but of course 32:21 there's also data.gov there are so many data sets that that are available here for you to just you 32:28 know um satiate your curiosity uh download something convert that to 32:33 vectors using uh uh uh uh embedding models and get started with a vector database and you will actually uh wow 32:39 yourself with what's possible that's amazing uh well uh thank you so much 32:44 once again for joining us and I suggest that you reach out to Rohead and then 32:50 don't forget we do have a free trial with Zillow so you don't have to pay for it and it's actually pretty powerful we 32:56 actually provide I think up to two collections of uh a million or half a 33:01 half million in each collection of um of vector embedding so it's um there's a 33:06 lot you can do there up to five collections yeah so there it's plenty for you to be able to to get started 33:12 with and then as uh RoIP mentioned you know with any of our products write once and it's easy to bring that over to our 33:20 dedicated cluster when you're ready that's right thank you so much everybody 33:25 and we can't wait to hear what you build and we look forward to seeing you again soon bye-bye 33:32 thank you
Meet the Speaker
Join the session for live Q&A with the speaker
Chris Churilo
VP of Marketing
Chris Churilo is the VP of Marketing & Community at Zilliz where she leads all community, developer relations, and marketing efforts. Prior to Zilliz, Chris was a founding member of the InfluxData’s go to market efforts and helped propel the time series database platform to dominance in the market. In earlier roles she defined and designed a SaaS monitoring solution at Centroid, and prior to that she was the VP of product management at iPass and was the LOB for several cloud services that required her to track the business and operational metrics and analytics to help identify and resolve issues.
Jay Byoun
Solutions Architect, Zilliz
Solutions Architect at Zilliz. Previously Staff Solutions Engineer at Pinecone. Previous background in software engineering and Blockchain architecture