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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Datadog

    Score8.8 out of 10
    N/ADatadog is a monitoring service for IT, Dev and Ops teams who write and run applications at scale, and want to turn the massive amounts of data produced by their apps, tools and services into actionable insight.

    $18

    per month per host

    Google BigQuery

    Score8.8 out of 10
    N/AGoogle'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
    DatadogGoogle BigQuery
    Editions & Modules
    Log Management
    $1.27
    per month (billed annually) per host
    Infrastructure
    $15.00
    per month (billed annually) per host
    Standard
    $18
    per month per host
    Enterprise
    $27
    per month per host
    DevSecOps Pro
    $27
    per month per host
    APM
    $31.00
    per month (billed annually) per host
    DevSecOps Enterprise
    $41
    per month per host
    Standard edition
    $0.04 / slot hour
    Enterprise edition
    $0.06 / slot hour
    Enterprise Plus edition
    $0.10 / slot hour
    Offerings
    Pricing Offerings
    DatadogGoogle BigQuery
    Free Trial
    YesYes
    Free/Freemium Version
    YesYes
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeOptionalNo setup fee
    Additional DetailsDiscount available for annual pricing. Multi-Year/Volume discounts available (500+ hosts/mo).—
    More Pricing Information
    Community Pulse
    DatadogGoogle BigQuery
    Considered Both Products
    Datadog
    No answer on this topic
    Google
    Chose Google BigQuery
    The architecture of ETL was influenced by Data processing component which is Dataproc and there was a need for easy Query console with Access control capabilities with lesser overhead in managing the permission. This made the decision to move with Google BigQuery compare to …
    Incentivized
    Key User Insights
    Would buy again
    94%
    Would buy again
    50 Answers
    98%
    Would buy again
    64 Answers
    Delivers good value for the price
    93%
    Delivers good value for the price
    41 Answers
    97%
    Delivers good value for the price
    56 Answers
    Happy with the feature set
    98%
    Happy with the feature set
    52 Answers
    97%
    Happy with the feature set
    63 Answers
    Lived up to sales and marketing promises
    97%
    Lived up to sales and marketing promises
    32 Answers
    100%
    Lived up to sales and marketing promises
    42 Answers
    Implementation went as expected
    97%
    Implementation went as expected
    36 Answers
    100%
    Implementation went as expected
    59 Answers
    Features
    DatadogGoogle BigQuery
    Monitoring Tasks
    Comparison of Monitoring Tasks features of Datadog and Google BigQuery
    Feature
    Datadog
    7.9
    2 Ratings
    6% below category average
    Google BigQuery
    -
    Ratings
    Remote monitoring8.22 Ratings00 Ratings
    Network device monitoring7.72 Ratings00 Ratings
    Multiple Server Monitoring7.72 Ratings00 Ratings
    Multi-device monitoring7.72 Ratings00 Ratings
    Automated alerts and notifications8.22 Ratings00 Ratings
    Management Tasks
    Comparison of Management Tasks features of Datadog and Google BigQuery
    Feature
    Datadog
    6.1
    1 Ratings
    12% below category average
    Google BigQuery
    -
    Ratings
    Patch Management7.31 Ratings00 Ratings
    Service configuration management6.41 Ratings00 Ratings
    Software and hardware inventory5.51 Ratings00 Ratings
    Policy-based automation5.51 Ratings00 Ratings
    Reporting
    Comparison of Reporting features of Datadog and Google BigQuery
    Feature
    Datadog
    8.2
    2 Ratings
    3% above category average
    Google BigQuery
    -
    Ratings
    Performance data reports8.22 Ratings00 Ratings
    Customizable reporting7.72 Ratings00 Ratings
    Data visualization8.62 Ratings00 Ratings
    Risk analysis8.22 Ratings00 Ratings
    Security
    Comparison of Security features of Datadog and Google BigQuery
    Feature
    Datadog
    6.7
    1 Ratings
    9% below category average
    Google BigQuery
    -
    Ratings
    Data backup and recovery6.41 Ratings00 Ratings
    Antivirus and malware management7.31 Ratings00 Ratings
    Administrator access control6.41 Ratings00 Ratings
    Database-as-a-Service
    Comparison of Database-as-a-Service features of Datadog and Google BigQuery
    Feature
    Datadog
    -
    Ratings
    Google BigQuery
    8.5
    80 Ratings
    1% above category average
    Automatic software patching00 Ratings8.017 Ratings
    Database scalability00 Ratings9.079 Ratings
    Automated backups00 Ratings8.524 Ratings
    Database security provisions00 Ratings8.873 Ratings
    Monitoring and metrics00 Ratings8.675 Ratings
    Automatic host deployment00 Ratings8.013 Ratings
    Best Alternatives
    DatadogGoogle BigQuery
    Small Businesses
    Amazon CloudWatch
    Score7.8 out of 10
    MongoDB Atlas
    Score7.8 out of 10
    Medium-sized Companies
    LogicMonitor
    Score8.9 out of 10
    Azure Database
    Score8.8 out of 10
    Enterprises
    ManageEngine Site24x7
    Score10 out of 10
    Google Cloud SQL
    Score8.7 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    DatadogGoogle BigQuery
    Likelihood to Recommend
    8.9
    (65 ratings)
    9.0
    (79 ratings)
    Likelihood to Renew
    4.3
    (2 ratings)
    8.1
    (5 ratings)
    Usability
    8.7
    (44 ratings)
    6.6
    (6 ratings)
    Availability
    -
    (0 ratings)
    7.3
    (1 ratings)
    Performance
    -
    (0 ratings)
    6.4
    (1 ratings)
    Support Rating
    5.0
    (7 ratings)
    4.8
    (11 ratings)
    Implementation Rating
    1.0
    (1 ratings)
    -
    (0 ratings)
    Configurability
    -
    (0 ratings)
    6.4
    (1 ratings)
    Contract Terms and Pricing Model
    -
    (0 ratings)
    10.0
    (1 ratings)
    Ease of integration
    -
    (0 ratings)
    7.3
    (1 ratings)
    Product Scalability
    -
    (0 ratings)
    7.3
    (1 ratings)
    Professional Services
    -
    (0 ratings)
    8.2
    (2 ratings)
    User Testimonials
    DatadogGoogle BigQuery
    Likelihood to Recommend
    Datadog
    Datadog may be better suited for teams that have a more out-of-the-box infrastructure, on the primary platforms Datadog supports. You may also have better results if you have a bigger team dedicated to devops and/or a bigger budget. We found that trying to adapt it to our use case (small team, .NET on AWS Fargate) wasn't feasible. We continually ran into roadblocks that required us to dig through documentation (and at times, having to figure out some documentation was wrong), go back and forth with support, and in my opinion, waste money on excessive and unintended usages due to opaque pricing models and inaccurate usage reports, as well as broken/non-functional rate sampling controls.
    Incentivized
    Read full review
    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).
    Incentivized
    Read full review
    Pros
    Datadog
    • The thing which Datadog does really well, one of them are its broad range of services integrations and features which makes it one step observability solution for all. We can monitor all types of our application, infrastructure, hosts, databases etc with Datadog.
    • Its custom dashboard feature which helps us to visualize the data in a better way . It supports different types of charts through those charts we can create our dashboard more attractive.
    • Its AI powered alerting capability though that we can easily identify the root cause and also it has a low noise alerting capability which means it correlated the similar type of issues.
    Incentivized
    Read full review
    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.
    Incentivized
    Read full review
    Cons
    Datadog
    • Alert windows cause lag in notifications (e.g. if the alert window is X errors in 1 hour, we won't get alerted until the end of the 1 hour range)
    • I would appreciate more supportive examples for how to filter and view metrics in the explorer
    • I would like a more clear interface for metrics that are missing in a time frame, rather than only showing tags/etc. for metrics that were collected within the currently viewed time frame
    Incentivized
    Read full review
    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.
    Incentivized
    Read full review
    Likelihood to Renew
    Datadog
    Definitely will not revisit after our issues and, in my opinion, poor support.
    Incentivized
    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.
    Incentivized
    Read full review
    Usability
    Datadog
    There are so many features that it can be hard to figure out where you need to go for your own use case. For example, RUM monitoring us buried in a "Digital Experience" sidebar setting when this is one of our key use cases that I sometimes struggle to find in the application. It appears that ECS + Fargate monitoring was recently released which is great because we had to build a lambda reporting solution for ephemeral task monitoring. But this new feature was never on my radar until I starting clicking around the application.
    Incentivized
    Read full review
    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.
    Incentivized
    Read full review
    Reliability and Availability
    Datadog
    No answers on this topic
    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.
    Incentivized
    Read full review
    Performance
    Datadog
    No answers on this topic
    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.
    Incentivized
    Read full review
    Support Rating
    Datadog
    The support team usually gets it right. We did have a rather complicate issue setting up monitoring on a domain controller. However, they are usually responsive and helpful over chat. The downside would be I don’t think they have any phone support. If that is important to you this might not be a good fit.
    Incentivized
    Read full review
    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.
    Incentivized
    Read full review
    Implementation Rating
    Datadog
    Documentation was difficult to work through, rollout was catastrophic (completely outage)
    Incentivized
    Read full review
    Google
    No answers on this topic
    Alternatives Considered
    Datadog
    Our logs are very important, and Datadog manages them exceptionally well. We frequently use Datadog services for our investigations. Use case: Monitor your apps, infrastructure, APIs, and user experience.


    Key features:


    Logs, metrics, and APM (Application Performance Monitoring)


    Real-time alerting and dashboards


    Supports Kubernetes, AWS, GCP, and other integrations


    RUM (Real User Monitoring) and Synthetics





    ✅ Best for backend, server, and distributed systems monitoring.
    Incentivized
    Read full review
    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.
    Incentivized
    Read full review
    Contract Terms and Pricing Model
    Datadog
    No answers on this topic
    Google
    None so far. Very satisfied with the transparency on contract terms and pricing model.
    Read full review
    Scalability
    Datadog
    No answers on this topic
    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.
    Incentivized
    Read full review
    Professional Services
    Datadog
    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.
    Read full review
    Return on Investment
    Datadog
    • Saved us (time & money) from developing our own monitoring utilities that would pale in comparison
    • Alerts allow us to remedy issues before our customers even know about them
    • Tracking resource usage over time allows us to better plan for future needs, before it becomes a pain-point.
    Incentivized
    Read full review
    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.
    Incentivized
    Read full review
    ScreenShots

    Datadog Screenshots

    Screenshot of the out-of-the-box and customizable monitoring dashboards.Screenshot of Datadog's collaboration features, where users can discuss issues in-context with production data, annotate changes and notify their teams, see who responded to that alert before, and discover what was done to fix it.Screenshot of where Datadog unifies traces, metrics, and logs—the three pillars of observability.Screenshot of some of Datadog's 400+ built-in integrations.Screenshot of Datadog's Service Map, which decomposes an application into all its component services and draws the observed dependencies between these services in real timeScreenshot of centralized log data, pulled from any source.

    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.