Overview
ProductRatingMost Used ByProduct SummaryStarting Price
DigitalOcean
Score 8.7 out of 10
N/A
DigitalOcean is an infrastructure-as-a-service (IaaS) platform from the company of the same name headquartered in New York. It is known for its support of managed Kubernetes clusters and “droplets” feature.
$5
Starting Price Per Month
Google BigQuery
Score 8.7 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)
Pricing
DigitalOceanGoogle BigQuery
Editions & Modules
1GB-16GB
$5.00
Starting Price Per Month
8GB-160GB
$60.00
Starting Price Per Month
Standard edition
$0.04 / slot hour
Enterprise edition
$0.06 / slot hour
Enterprise Plus edition
$0.10 / slot hour
Offerings
Pricing Offerings
DigitalOceanGoogle BigQuery
Free Trial
NoYes
Free/Freemium Version
NoYes
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
DigitalOceanGoogle BigQuery
Features
DigitalOceanGoogle BigQuery
Infrastructure-as-a-Service (IaaS)
Comparison of Infrastructure-as-a-Service (IaaS) features of Product A and Product B
DigitalOcean
9.1
35 Ratings
10% above category average
Google BigQuery
-
Ratings
Service-level Agreement (SLA) uptime9.730 Ratings00 Ratings
Dynamic scaling9.731 Ratings00 Ratings
Elastic load balancing9.023 Ratings00 Ratings
Pre-configured templates10.028 Ratings00 Ratings
Monitoring tools9.734 Ratings00 Ratings
Pre-defined machine images9.032 Ratings00 Ratings
Operating system support8.632 Ratings00 Ratings
Security controls9.031 Ratings00 Ratings
Automation7.54 Ratings00 Ratings
Database-as-a-Service
Comparison of Database-as-a-Service features of Product A and Product B
DigitalOcean
-
Ratings
Google BigQuery
8.4
73 Ratings
3% below category average
Automatic software patching00 Ratings8.017 Ratings
Database scalability00 Ratings9.172 Ratings
Automated backups00 Ratings8.524 Ratings
Database security provisions00 Ratings8.866 Ratings
Monitoring and metrics00 Ratings8.268 Ratings
Automatic host deployment00 Ratings8.013 Ratings
Best Alternatives
DigitalOceanGoogle BigQuery
Small Businesses
Amazon Elastic Compute Cloud (EC2)
Amazon Elastic Compute Cloud (EC2)
Score 8.8 out of 10
IBM Cloudant
IBM Cloudant
Score 7.6 out of 10
Medium-sized Companies
SAP on IBM Cloud
SAP on IBM Cloud
Score 9.0 out of 10
IBM Cloudant
IBM Cloudant
Score 7.6 out of 10
Enterprises
SAP on IBM Cloud
SAP on IBM Cloud
Score 9.0 out of 10
IBM Cloudant
IBM Cloudant
Score 7.6 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
DigitalOceanGoogle BigQuery
Likelihood to Recommend
9.0
(35 ratings)
8.8
(73 ratings)
Likelihood to Renew
9.0
(2 ratings)
7.8
(3 ratings)
Usability
9.0
(9 ratings)
7.7
(5 ratings)
Availability
10.0
(1 ratings)
-
(0 ratings)
Performance
9.0
(1 ratings)
-
(0 ratings)
Support Rating
8.8
(9 ratings)
7.5
(10 ratings)
Contract Terms and Pricing Model
-
(0 ratings)
10.0
(1 ratings)
Product Scalability
10.0
(1 ratings)
-
(0 ratings)
Professional Services
-
(0 ratings)
8.2
(2 ratings)
User Testimonials
DigitalOceanGoogle BigQuery
Likelihood to Recommend
DigitalOcean
DigitalOcean is perfect for hosting client websites, running marketing tools, and managing media storage with Spaces and CDN. The use of Droplets to quickly launch landing pages or WordPress sites for campaigns is a Godsend. It’s great for fast, cheap, and scalable solutions. But for complex microservices or projects needing strict compliance (like HIPAA), DigitalOcean may not always be the best fit, but that depends heavily on your project.
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Google
Google BigQuery is great for being the central datastore and entry point of data if you're on GCP. It seamlessly integrates with other Google products, meaning you can ingest data from other Google products with ease and little technical knowledge, and all of it is near real-time. Being serverless, BigQuery will scale with you, which means you don't have to worry about contention or spikes in demand/storage. This can, however, mean your costs can run away quickly or mount up at short notice.
Read full review
Pros
DigitalOcean
  • DigitalOcean provides some of the best cost-to-value services available
  • The DigitalOcean cloud console is very intuitive and easy to navigate
  • DigitalOcean has great support for Docker and other dev ops tools like Terraform.
  • DigitalOcean iterates quickly and provides cutting edge features for organizations that want to keep up with the latest and greatest dev ops tooling
  • DigitalOcean has a great developer community and numerous support docs/tutorials
Read full review
Google
  • First and foremost - Google BigQuery is great at quickly analyzing large amounts of data, which helps us understand things like customer behavior or product performance without waiting for a long time.
  • It is very easy to use. Anyone in our team can easily ask questions about our data using simple language, like asking ChatGPT a question. This means everyone can find important information from our data without needing to be a data expert.
  • It plays nicely with other tools we use, so we can seamlessly connect it with things like Google Cloud Storage for storing data or Data Studio for creating visual reports. This makes our work smoother and helps us collaborate better across different tasks.
Read full review
Cons
DigitalOcean
  • Some products/services available on other Cloud providers aren't available, but they seem to be catching up as they add new products like Managed SQL DBs.
  • While they have FreeBSD droplets (VMs), support for *BSD OSs is limited. I.e. the new monitoring agent only works on Linux.
  • There are no regions available on South America.
  • They don't seem to offer enterprise-level products, even basic ones as Windows Server, MS SQL Server, Oracle products, etc.
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Google
  • It is challenging to predict costs due to BigQuery's pay-per-query pricing model. User-friendly cost estimation tools, along with improved budget alerting features, could help users better manage and predict expenses.
  • The BigQuery interface is less intuitive. A more user-friendly interface, enhanced documentation, and built-in tutorial systems could make BigQuery more accessible to a broader audience.
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Likelihood to Renew
DigitalOcean
I've been very happy with it for my purposes and I plan to continue to use DigitalOcean for the foreseeable future!
Read full review
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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Usability
DigitalOcean
I honestly can't think of an easier way to set up and maintain your own server. Being able to set up a server in minutes and have fully control is awesome. The UX is incredibly intuitive for first-time users as well so there's no reason to be intimidated when it comes to giving DigitalOcean a shot.
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Google
web UI is easy and convenient. Many RDBMS clients such as aqua data studio, Dbeaver data grid, and others connect. Range of well-documented APIs available. The range of features keeps expanding, increasing similar features to traditional RDBMS such as Oracle and DB2
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Reliability and Availability
DigitalOcean
Have not found a single second of down time myself. Superior availability.
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Google
No answers on this topic
Performance
DigitalOcean
Very quick response and high performance, you have to fine tune configurations on your machines though.
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Google
No answers on this topic
Support Rating
DigitalOcean
They have always been fast, and the process has been straight-forward. I haven't had to use it enough to be frustrated with it, to be honest, and when I have an issue they fix it. As with all support, I wish it felt more human, but they are doing aces.
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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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Alternatives Considered
DigitalOcean
DigitalOcean is an inexpensive product as compared to other products available in the market. The UI is easy and the beginner can also understand the UI with the step by step guide. It provides a lot of custom features and the user needs to pay only for what they are using. Amazon has a complex UI and is on the expensive side. DigitalOcean is simple to use and is easily manageable and the servers can easily be set up without additional cost and such.
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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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Contract Terms and Pricing Model
DigitalOcean
No answers on this topic
Google
None so far. Very satisfied with the transparency on contract terms and pricing model.
Read full review
Scalability
DigitalOcean
Great scalability, you can start with small plans and move up to premium features at a very good price.
Read full review
Google
No answers on this topic
Professional Services
DigitalOcean
No answers on this topic
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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Return on Investment
DigitalOcean
  • Positive - Elastic computer instances make it possible to pay for only for what you need.
  • Positive - Competitive pricing - some of the products that DigitalOcean offers are much cheaper than those offered by competitors.
  • Negative - Having to go to other cloud computing platforms for more specific, advanced services like Computer Vision optimized services, GPU cloud compute instances, etc...
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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.
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.