We performed a comparison between Datadog and Sentry based on our users’ reviews in five categories. After reading all of the collected data, you can find our conclusion below.
Features: Datadog offers useful features like dashboards, reporting, error reporting, log centralization, ease of use and setup, logs, and analysis, while Sentry excels in accuracy, integration with tools, error management, user-friendliness, and providing a rich context for error logs. Datadog requires improvements in usability, integration, SSL security, customization flexibility, documentation, and local support. Sentry could enhance issue automation, tracking capabilities, integration, pricing, and visual UX for administrators.
Service and Support: Datadog's customer service is highly praised for its availability and promptness, earning positive reviews. Sentry's customer service has limited feedback, but customers appreciate the helpfulness of the community support and documentation.
Ease of Deployment: Users generally find the initial setup for Datadog to be simple and uncomplicated, with some receiving help from service providers or technical support. However, a few users did find it complicated and needed to make further adjustments. Setting up Sentry initially is also easy and straightforward, offering various options. However, smaller companies may take up to three months for onboarding, and configuring a self-hosted server can be more difficult.
Pricing: The cost of setting up Datadog is subjective, with differing opinions among users. Some find it costly, while others find it reasonable. Users recommend trying the free plan before opting for a paid subscription. The pricing structure, particularly for log analytics and traffic-based expenses, can be perplexing. Sentry provides a free plan for initial projects and has affordable pricing for the paid version. Although some users find the license expensive, they believe it is worthwhile.
ROI: Users have reported different levels of ROI when using Datadog, with some highlighting the time saved and improved visibility into potential issues. Sentry has demonstrated favorable financial outcomes and advantages.
Comparison Results: Datadog is the preferred choice in comparison to Sentry. Users find Datadog easy to use and set up, appreciating its dashboards, reporting capabilities, error reporting, and log centralization. It is also praised for its user-friendliness for development teams and wide range of integrations. Datadog offers flexibility, observability, and additional features like AI and ML capabilities.
"The solution has helped out organization gain improved visibility."
"Having a wealth of information has helped us investigate outages, and having historical data helps us tune our system."
"The most valuable aspect is for us to have everything in one place."
"I have found error reporting and log centralization the most valuable features. Overall, Datadog provides a full package solution."
"Profiling has been made easier."
"Its logs are most valuable."
"The solution is sufficiently stable."
"The service catalog helped improve our organization by giving a good view of the flow for our microservices applications."
"Sentry is more accurate than some other tools such as Datadog because it has more integration with Slack, GitLab, Jira, or other ticketing tools."
"The solution is user-friendly."
"The most valuable feature is the ability to create and assign rules and give access to particular users."
"Sentry is a pretty stable product... Sentry's documentation is pretty straightforward and neat."
"The stability is very good for Sentry and in general works well."
"It's a great visibility tool for the developer team."
"Great for capturing application performance metrics and error logs."
"Its initial setup process is relatively straightforward."
"We would like to see some versioning system for the Synthetic Tests so that we could have a backup of our tests since they are time-consuming to make and very easy to damage in a moment of error."
"The solution needs to integrate AI tools."
"The pricing model could be simplified as it feels a bit outdated, especially when you look at the billing model of compute instances vs the containers instances."
"More granular control over dashboard sharing. Timeboard sharing."
"Lacks some flexibility in the customization."
"The way data is represented can be limiting. When I first tried it out a long time ago, you could graph a metric and another metric, and they'd overlay, but you couldn't take the ratio between the two."
"While I like the ease of use, when compared with Tenable Nessus they could still improve their usability."
"Deploying the agents is still very manual."
"It would be nice if the product provided a map showing the users’ geographic location."
"The settings for an administrator are complex."
"Its debugging feature needs to be faster."
"I would like to have alert policies and alert conditions enhanced in the next release."
"We cannot restrict particular columns on particular data. It would be helpful if that feature was improved."
"Lacks user metric tracking and the ability to create more dashboards."
"The log centralization and analysis could be improved in Sentry."
"The price could be lowered."
Datadog is ranked 1st in Application Performance Monitoring (APM) and Observability with 137 reviews while Sentry is ranked 8th in Application Performance Monitoring (APM) and Observability with 11 reviews. Datadog is rated 8.6, while Sentry is rated 8.6. The top reviewer of Datadog writes "Very good RUM, synthetics, and infrastructure host maps". On the other hand, the top reviewer of Sentry writes "An easy-to-use solution that has a good dashboard, performs well, and provides flexible pricing". Datadog is most compared with Dynatrace, Azure Monitor, New Relic, AWS X-Ray and Wazuh, whereas Sentry is most compared with Azure Monitor, Grafana, Elastic Observability, New Relic and Prometheus. See our Datadog vs. Sentry report.
See our list of best Application Performance Monitoring (APM) and Observability vendors.
We monitor all Application Performance Monitoring (APM) and Observability reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.