Google BigQuery vs. Vultr

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Google BigQuery
Score 8.6 out of 10
N/A
Google's BigQuery is part of the Google Cloud Platform, a database-as-a-service (DBaaS) supporting the querying and rapid analysis of enterprise data.
$6.25
per TiB (after the 1st 1 TiB per month, which is free)
Vultr
Score 6.3 out of 10
N/A
Vultr is an independent cloud computing platform on a mission to provide businesses and developers around the world with unrivaled ease of use, price-to-performance, and global reach.
$2.50
per month
Pricing
Google BigQueryVultr
Editions & Modules
Standard edition
$0.04 / slot hour
Enterprise edition
$0.06 / slot hour
Enterprise Plus edition
$0.10 / slot hour
Block Storage
$1
per month
Cloud Compute
$2.50
per month
Object Storage
$5
per month
Kubernetes Engine
$10
per month
Load Balancers
$10
per month
Managed Databases
$15
per month
Optimized Cloud Compute
$28
per month
Cloud GPU
$90
per month
Bare Metal
$120
per month
Offerings
Pricing Offerings
Google BigQueryVultr
Free Trial
YesNo
Free/Freemium Version
YesNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional DetailsPricing is based on specifications chosen in each product category. Bandwidth is also included up to a certain amount per month.
More Pricing Information
Community Pulse
Google BigQueryVultr
Features
Google BigQueryVultr
Database-as-a-Service
Comparison of Database-as-a-Service features of Product A and Product B
Google BigQuery
8.4
71 Ratings
3% below category average
Vultr
-
Ratings
Automatic software patching8.017 Ratings00 Ratings
Database scalability9.270 Ratings00 Ratings
Automated backups8.524 Ratings00 Ratings
Database security provisions8.664 Ratings00 Ratings
Monitoring and metrics8.066 Ratings00 Ratings
Automatic host deployment8.013 Ratings00 Ratings
Infrastructure-as-a-Service (IaaS)
Comparison of Infrastructure-as-a-Service (IaaS) features of Product A and Product B
Google BigQuery
-
Ratings
Vultr
2.7
15 Ratings
99% below category average
Service-level Agreement (SLA) uptime00 Ratings2.614 Ratings
Dynamic scaling00 Ratings2.212 Ratings
Elastic load balancing00 Ratings2.511 Ratings
Pre-configured templates00 Ratings2.713 Ratings
Monitoring tools00 Ratings2.213 Ratings
Pre-defined machine images00 Ratings2.614 Ratings
Operating system support00 Ratings2.815 Ratings
Security controls00 Ratings4.513 Ratings
Automation00 Ratings2.212 Ratings
Best Alternatives
Google BigQueryVultr
Small Businesses
IBM Cloudant
IBM Cloudant
Score 7.4 out of 10
DigitalOcean Droplets
DigitalOcean Droplets
Score 8.7 out of 10
Medium-sized Companies
IBM Cloudant
IBM Cloudant
Score 7.4 out of 10
SAP on IBM Cloud
SAP on IBM Cloud
Score 9.5 out of 10
Enterprises
IBM Cloudant
IBM Cloudant
Score 7.4 out of 10
SAP on IBM Cloud
SAP on IBM Cloud
Score 9.5 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Google BigQueryVultr
Likelihood to Recommend
8.7
(71 ratings)
2.4
(18 ratings)
Likelihood to Renew
8.1
(3 ratings)
-
(0 ratings)
Usability
7.7
(5 ratings)
-
(0 ratings)
Support Rating
7.3
(10 ratings)
-
(0 ratings)
Contract Terms and Pricing Model
10.0
(1 ratings)
-
(0 ratings)
Professional Services
8.2
(2 ratings)
-
(0 ratings)
User Testimonials
Google BigQueryVultr
Likelihood to Recommend
Google
Event-based data can be captured seamlessly from our data layers (and exported to Google BigQuery). When events like page-views, clicks, add-to-cart are tracked, Google BigQuery can help efficiently with running queries to observe patterns in user behaviour. That intermediate step of trying to "untangle" event data is resolved by Google BigQuery. A scenario where it could possibly be less appropriate is when analysing "granular" details (like small changes to a database happening very frequently).
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Vultr
I've been with Vultr over 5 years hosting multiple businesses and email related services. I never experienced a significant outage or data loss. Migration has always been successful as well. Support is top tier and IP reputation is clean. I like the choices of OS, ease of platform use and multiple hosting/ region options.
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Pros
Google
  • Realtime integration with Google Sheets.
  • GSheet data can be linked to a BigQuery table and the data in that sheet is ingested in realtime into BigQuery. It's a live 'sync' which means it supports insertions, deletions, and alterations. The only limitation here is the schema'; this remains static once the table is created.
  • Seamless integration with other GCP products.
  • A simple pipeline might look like this:-
  • GForms -> GSheets -> BigQuery -> Looker
  • It all links up really well and with ease.
  • One instance holds many projects.
  • Separating data into datamarts or datameshes is really easy in BigQuery, since one BigQuery instance can hold multiple projects; which are isolated collections of datasets.
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Vultr
  • Good performance from servers close to me and my customers
  • Ease of spinning up new servers
  • Great service levels of servers I spin up
  • Notifications of issues or downtime come through quickly, allowing me to handle client queries with knowledge in hand.
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Cons
Google
  • Please expand the availability of documentation, tutorials, and community forums to provide developers with comprehensive support and guidance on using Google BigQuery effectively for their projects.
  • If possible, simplify the pricing model and provide clearer cost breakdowns to help users understand and plan for expenses when using Google BigQuery. Also, some cost reduction is welcome.
  • It still misses the process of importing data into Google BigQuery. Probably, by improving compatibility with different data formats and sources and reducing the complexity of data ingestion workflows, it can be made to work.
Read full review
Vultr
  • The response times for support tickets is too high and a quicker response time would be appreciated
  • Availability of the VPS solution in Middle east and Africa would help
  • Abilito to connect SIP trunks would make it more easy to run active communictaion platforms on Vultr
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Likelihood to Renew
Google
We have to use this product as its a 3rd party supplier choice to utilise this product for their data side backend so will not be likely we will move away from this product in the future unless the 3rd party supplier decides to change data vendors.
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Vultr
We’ve been extremely satisfied with the service for many years. After trying other providers, we’ve found nothing that matches the reliability and performance—so we’re not likely to switch anytime soon.
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Usability
Google
I think overall it is easy to use. I haven't done anything from the development side but an more of an end user of reporting tables built in Google BigQuery. I connect data visualization tools like Tableau or Power BI to the BigQuery reporting tables to analyze trends and create complex dashboards.
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Vultr
easy to use and configure. great bang for the buck. I need an affordable solution to host in the cloud data from systems installed at our client's site with the ability to drill down and change the configuration remotely. Vultr enabled us to do that in an efficient and affordable way.
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Reliability and Availability
Google
I have never had any significant issues with Google Big Query. It always seems to be up and running properly when I need it. I cannot recall any times where I received any kind of application errors or unplanned outages. If there were any they were resolved quickly by my IT team so I didn't notice them.
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Vultr
No answers on this topic
Performance
Google
I think Google Big Query's performance is in the acceptable range. Sometimes larger datasets are somewhat sluggish to load but for most of our applications it performs at a reasonable speed. We do have some reports that include a lot of complex calculations and others that run on granular store level data that so sometimes take a bit longer to load which can be frustrating.
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Vultr
No answers on this topic
Support Rating
Google
BigQuery can be difficult to support because it is so solid as a product. Many of the issues you will see are related to your own data sets, however you may see issues importing data and managing jobs. If this occurs, it can be a challenge to get to speak to the correct person who can help you.
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Vultr
Because of the 24 hour turnaround timing with servers I need immediate reply
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Implementation Rating
Google
No answers on this topic
Vultr
Nothing in particular to share that I did not already discuss
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Alternatives Considered
Google
PowerBI can connect to GA4 for example but the data processing is more complicated and it takes longer to create dashboards. Azure is great once the data import has been configured but it's not an easy task for small businesses as it is with BigQuery.
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Vultr
Linode is a more old-school offering. Linode pricing model and infrastructure rely on classic Virtual Machines. What we like about Vultr is that they offer the same at the front, but in the back, the machines are much more flexible and can be tailor-made to our needs, which of course also impacts the costs of running the infrastructure.
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Contract Terms and Pricing Model
Google
None so far. Very satisfied with the transparency on contract terms and pricing model.
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Vultr
Pricing is fair and did not increase
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Scalability
Google
We have continued to expand out use of Google Big Query over the years. I'd say its flexibility and scalability is actually quite good. It also integrates well with other tools like Tableau and Power BI. It has served the needs of multiple data sources across multiple departments within my company.
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Vultr
No answers on this topic
Professional Services
Google
Google Support has kindly provide individual support and consultants to assist with the integration work. In the circumstance where the consultants are not present to support with the work, Google Support Helpline will always be available to answer to the queries without having to wait for more than 3 days.
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Vultr
No answers on this topic
Return on Investment
Google
  • Previously, running complex queries on our on-premise data warehouse could take hours. Google BigQuery processes the same queries in minutes. We estimate it saves our team at least 25% of their time.
  • We can target our marketing campaigns very easily and understand our customer behaviour. It lets us personalize marketing campaigns and product recommendations and experience at least a 20% improvement in overall campaign performance.
  • Now, we only pay for the resources we use. Saved $1 million annually on data infrastructure and data storage costs compared to our previous solution.
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Vultr
  • We have experienced a 200% return on using Vultr products with a 100% Customer Satisfaction rating.
  • Vultr gives us the regions to deploy where our small business customers are located.
  • Vultr has helped us scale up our company and our cloud solutions.
Read full review
ScreenShots

Google BigQuery Screenshots

Screenshot of Migrating data warehouses to BigQuery - Features a streamlined migration path from Netezza, Oracle, Redshift, Teradata, or Snowflake to BigQuery using the fully managed BigQuery Migration Service.Screenshot of bringing any data into BigQuery - Data files can be uploaded from local sources, Google Drive, or Cloud Storage buckets, using BigQuery Data Transfer Service (DTS), Cloud Data Fusion plugins, by replicating data from relational databases with Datastream for BigQuery, or by leveraging Google's data integration partnerships.Screenshot of generative AI use cases with BigQuery and Gemini models - Data pipelines that blend structured data, unstructured data and generative AI models together can be built to create a new class of analytical applications. BigQuery integrates with Gemini 1.0 Pro using Vertex AI. The Gemini 1.0 Pro model is designed for higher input/output scale and better result quality across a wide range of tasks like text summarization and sentiment analysis. It can be accessed using simple SQL statements or BigQuery’s embedded DataFrame API from right inside the BigQuery console.Screenshot of insights derived from images, documents, and audio files, combined with structured data - Unstructured data represents a large portion of untapped enterprise data. However, it can be challenging to interpret, making it difficult to extract meaningful insights from it. Leveraging the power of BigLake, users can derive insights from images, documents, and audio files using a broad range of AI models including Vertex AI’s vision, document processing, and speech-to-text APIs, open-source TensorFlow Hub models, or custom models.Screenshot of event-driven analysis - Built-in streaming capabilities automatically ingest streaming data and make it immediately available to query. This allows users to make business decisions based on the freshest data. Or Dataflow can be used to enable simplified streaming data pipelines.Screenshot of predicting business outcomes AI/ML - Predictive analytics can be used to streamline operations, boost revenue, and mitigate risk. BigQuery ML democratizes the use of ML by empowering data analysts to build and run models using existing business intelligence tools and spreadsheets.

Vultr Screenshots

Screenshot of Vultr's control panel helps users spend less time managing infrastructure.Screenshot of Vultr's services offer additional configuration and inside of the simplified control panel.Screenshot of a display of when peak activity happens on an application. The server health graphs provide insight from the moment the server is created.Screenshot of Vultr's interface, which allows users to deploy high performance servers worldwide from any device.Screenshot of how to reach the 24/7/365 technical support team that is available through Vultr's ticketing system.