InfluxDB Cloud Serverless

What do you like best about the product?

What I liked most about InfluxDb was its performance when handling large volumes of time-series data. We used it for monitoring and metrics, and it was reliable for collecting and querying data frequently. The integrations also made it fairly easy to fit into our existing tech stack. I found the UI and overall workflow reasonably straightforward once I got familiar with it, although there is a bit of a learning curve initially. The main drawback for us was pricing. As our data volume and retention needs increased, the cost became harder to justify. Overall, I think it's technically strong, particularly for performance and time series workloads, but I'd recommend carefully evaluating the long-term cost before committing at scale.

What do you dislike about the product?

The biggest issue for me was the pricing as usage scaled. In the beginning, the platform worked well for our needs, but as our data volume and retention requirements increased, the costs became harder to justify as I mentioned in the previous question. So, concluding with pricing and some hit on the learning curve.

What problems is the product solving and how is that benefiting you?

We mainly used Influx DB to handle our time series data and system metrics without putting too much load on our main database. The main problem it solved was reliability in monitoring, performance, and viewing historical trends. One click and you know what you are looking at the historical points.

Recommendations to others considering the product:

I would recommend carefully evaluating the long-term cost before committing at scale.

What do you like best about the product?

I like how InfluxDB fits well for time-based tracking data. For a use case for POS data, it was straightforward to store things like sales and transaction metrics.

What do you dislike about the product?

It took me time to get used to the data model compared to a traditional database. Understanding how to structure tags and fields took some time.

What problems is the product solving and how is that benefiting you?

It helps me with storing and analyzing for POS-based use cases. I can easily track activity over time and query it for different time ranges.

My main use case for InfluxDB is to inject all my performance data and visualize it as a data source into Grafana.

My data comes from JMeter from a plugin, which is inserting data to InfluxDB, and then I have InfluxDB as a data source in Grafana for visualizing and creating dashboards for real-time visualization. It records the data every second and injects and stores the data in InfluxDB.

The best features InfluxDB offers include segregating data into different buckets, allowing you to create your own bucket and filter out different data parameters, which helps retain it for an extended period. It also has its own built-in dashboarding mechanism where you can create dashboards.

I use the buckets because I have created different buckets based on my requirements, injecting my data into the particular bucket and then storing and configuring this bucket in Grafana dashboard so that I can have that as a data source and visualize data correctly. For the InfluxDB dashboard, it is not primarily used, but the Grafana dashboarding is primarily used.

Prior to InfluxDB usage, real-time data was not visualized and monitored. After the introduction of InfluxDB in the entire framework, the organization is benefiting a lot in terms of monitoring and understanding performance data in real-time. Because it is a time-series database, it solves a lot of problems of having the data collected every second and visualizing every minute detail in between so that I can get into the exact performance issues and troubleshoot accordingly.

If the dashboarding facility can be improved in terms of the visualization parameters and the amount of support available from the community, as well as having smarter alerts to send those alerts to Slack channels for the required action to be taken, that would be greatly beneficial.

I have been using InfluxDB for four years.

InfluxDB is stable.

Its scalability is good enough and I can scale it to the required level.

Customer support is fair enough but can be improved.

This is the first solution I have used.

My experience with pricing, setup cost, and licensing was smooth, and I recommend everyone to go for this particular offering.

I was just a buyer and user, not involved in the implementation team.

It has reduced a lot of time in terms of troubleshooting because the way it produces the data on a time-series basis allows me to collect and store the data for future reference. There was a use case where we had down to one of the memory and CPU usage for a particular server, and based on those observations, we got into taking the heap dump and visualizing different data so that we could get to the exact root cause for the particular performance issue.

I did purchase InfluxDB through the AWS Marketplace.

Before choosing InfluxDB, I evaluated other options including New Relic and other APM tools, but I believe Grafana and InfluxDB are top-notch.

It is good to start with InfluxDB to stabilize your data, visualize your data, and have the data stored on a time-series basis. I would recommend everyone to get into InfluxDB and start using it.

The interview was very useful and meaningful. My review rating for InfluxDB is 7 out of 10.

Hybrid Cloud

Amazon Web Services (AWS)

My main use case for InfluxDB is storing a time series database and analyzing data changes over time. I use InfluxDB to store my infrastructure and application metrics, logs, events, and data, and I use it for monitoring and observability stacks.

The best features that InfluxDB offers, in my opinion, are storing time series data with a timestamp and measurements similar to tables, and it provides tagging to index and metadata, along with different fields and retention policies.

With tagging and metadata in InfluxDB, I can identify which logs or metrics are stored, such as CPU or networking data, and by using the time series data with timestamps, I know when this data or logs arrived.

InfluxDB positively impacts my organization because I have optimized my database storage, and by using powerful queries, I am able to find my data logs efficiently and also manage logging.

InfluxDB can be improved by addressing issues such as performance drops, queries being more difficult to understand, and its high memory usage. InfluxDB is not suitable for relational databases like MySQL and Postgres, and these aspects can be improved.

Sometimes, the setup is more challenging in InfluxDB. If it is not done properly, then I probably have to do it again.

I have been using InfluxDB for six months.

I would advise others looking into using InfluxDB that if they want to store a time series database for monitoring, logging, and event-based tasks, and also want long-term retention of the database with powerful queries to find, search, and explore the data, and desire a push-based mechanism, they can use InfluxDB. InfluxDB also provides connections with Prometheus and other tools. I would rate InfluxDB an eight out of ten.

Public Cloud

Amazon Web Services (AWS)

My main use case for InfluxDB has been mostly for monitoring and analyzing the time-series data related to system metrics, and also tracking logs and API performance. In my current role, I use it to track trends and anomalies in the system's health, while I am also able to help identify performance issues early and support root-cause analysis.

In my current role, I have used InfluxDB to monitor API responses, time, and server CPU usage in real-time. For example, I have set up continuous queries in InfluxDB to aggregate metrics such as average response time per minute and CPU load per server. This data feeds into the dashboards and then alerts the team when thresholds are breached, such as a spike in response time or CPU usage above eighty percent. When an alert triggers, I analyze the time-series data in InfluxDB to identify patterns or anomalies, which also helps pinpoint root causes quickly, such as a specific API endpoint. I have used this method for proactive monitoring, which reduces downtime and improves system reliability.

One scenario that really stands out is when we noticed intermittent spikes in API response time, which was affecting user experiences. Using InfluxDB, I was able to quickly analyze the time-series data, which correlated these spikes with specific backend processing runtime at the same time. This insight helped me identify a resource connection issue on certain servers. When we optimized the scheduling of those processes, it stabilized the response time and improved overall system reliability. I leverage InfluxDB as a core part of my monitoring workflow by continuously collecting and aggregating system metrics. This approach ensured that we maintain a balance between adding new features and keeping the system stable and performant.

The best InfluxDB features I think are its high-performance time-series storage and also a powerful query language and built-in support for down-sampling and continuous queries and real-time alerting, scalability and clustering options, and also the integrations with visualization tools. These are the features that help deliver a reliable, scalable solution.

I lean more on the query language because it gives me the most control and flexibility to analyze the data in-depth. While real-time alerting is more important for immediate notification, we have the ability to write complex queries with Flux, which allows me to explore data patterns and perform detailed root-cause analysis. The clustering is also valuable for scalability and high availability, but in my day-to-day work, the query language is the tool I use mostly to extract meaningful insights and drive decisions.

InfluxDB has had a significant positive impact on my organization. It has helped by enabling real-time visibility into system performance and user behavior. It helped our organization to quickly identify and resolve performance bottlenecks, which reduced downtime and improved user experience. This also has the ability to build custom dashboards and perform detailed time-series analysis, which has empowered both technical teams and business stakeholders to make data-driven decisions faster. This is how it has improved operational efficiency and allowed us to proactively address issues before they affect customers. Overall, InfluxDB has played a key role in enhancing system reliability and supporting our goal of delivering a seamless, high-quality product.

One thing I appreciate about InfluxDB is its balance between performance and ease of use, especially with Flux making complex queries accessible. However, I do wish the documentation and community resources around Flux were more extensive and beginner-friendly. Additionally, InfluxDB handles time-series data well, but deeper native support for anomaly detection or machine learning integrations would be great. Overall, it is a strong platform, and these enhancements could really make it even more powerful for data-driven teams.

InfluxDB is a strong platform, but there are a few areas where it could improve to better serve users and businesses. I would start with expanding and simplifying the documentation and community resources around its query language. It would help new users onboard faster and use the tool more effectively. Secondly, deeper native support for advanced analytics through machine learning integrations would add significant value by automating insights. The next thing I see is that enhancing the user experience around alerting, making it more intuitive and customizable, could really improve operational responsiveness. Lastly, better multi-tenant and role-based access control would really help organizations manage their security and collaboration more effectively. These improvements would make InfluxDB even more powerful and user-friendly for diverse teams.

From a performance perspective, enhancing InfluxDB scalability for very high cardinality data sets would be beneficial as some use cases generate massive volumes of unique time-series. Improving the query optimization to reduce latency on complex queries would also help maintain responsiveness. On the integration side, expanding the native connectors to popular cloud platforms and data tools such as AWS services, BI platforms, and machine learning would be great. These improvements would make InfluxDB more adaptable and performant.

I have been using InfluxDB for at least three to four years.

InfluxDB has proven to be very stable in our environment. We have used it to support mission-critical systems with continuous data ingestion and real-time analytics, and it is stable.

InfluxDB is highly scalable, which is one of its key strengths. It can handle large volumes of time-series data and with high ingestion rates, making it suitable for enterprise-scale deployments. This ensures consistent performance as data grows. Additionally, its retention policies and down-sampling features help manage stored data while maintaining query efficiency. In my experience, InfluxDB's scalability has allowed us to grow our monitoring and analytics capabilities without major re-architecture.

Customer support was really solid and responsive. In my experience, especially with enterprise deployments, having reliable support is crucial for maintaining uptime and resolving issues quickly. The InfluxDB support team was knowledgeable and helped us troubleshoot complex problems efficiently. They also provided guidance on best practices for scaling and optimizing performance. This support helped us avoid prolonged downtime and ensured smooth operation, which was important for our mission-critical systems. Overall, the support experience gave us confidence in using InfluxDB at scale.

Positive

Before InfluxDB, we used traditional relational databases and some open-source time-series tools which lacked the scalability and real-time capabilities. For example, we initially relied on PostgreSQL for time-series data, but it struggled with high ingestion rates and complex queries on large data sets. We switched to InfluxDB because it is purpose-built for time-series data, offering better performance. This switch has really improved our ability to handle large volumes of metrics and logs efficiently, reduced query latency, and simplified our data architecture, which was critical for supporting real-time monitoring and analytics use cases.

We have seen a clear return on investment with InfluxDB. One specific metric I would like to share is related to our operational efficiency, where we have been automating real-time monitoring and alerting on system metrics using InfluxDB. We reduced manual incident detection time by about forty percent. This has allowed our team to proactively address issues faster, improving system uptime and reducing downtime cost. Additionally, automating these processes reduced the need for manual monitoring efforts, saving roughly twenty percent of the analytics team's time, which we redirected to higher-value tasks. These improvements translated into both cost savings and better service reliability, directly impacting business outcomes.

My experience with InfluxDB pricing and licensing has been generally positive, based on some considerations. Pricing is based on data volume, retention, and features, which really makes it scalable but requires careful planning to avoid unexpected costs. Cost management also involves monitoring data ingestion rates and retention policies closely to balance storage cost with business needs. The licensing terms are flexible enough to accommodate growth, but it is important to align with usage patterns to maximize ROI. Overall, the investment in InfluxDB has been justified by the reliability and insights it delivers, but it is important to have a clear cost strategy.

Before choosing InfluxDB, we evaluated several other time-series database options such as TimescaleDB and OpenTSDB. Prometheus was really strong for monitoring, but it lacked long-term storage and advanced querying capabilities we needed. TimescaleDB offered good SQL compatibility, but it did not scale as well for our high ingestion rates. OpenTSDB was considered, but it had more complex setups and maintenance overhead. InfluxDB stood out because of its balance of scalability, ease of use, rich query language, and strong community and enterprise support. This evaluation process helped us select the best fit for our specific business and technical requirements.

My advice for others looking into using InfluxDB would be to clearly define their time-series data use cases upfront to ensure that InfluxDB fits their needs, especially for high-frequency metrics or event data. Also, plan for scalability by evaluating whether the open-source or enterprise version fits their expected data volume and query load. Additionally, set up the proper monitoring and alerting on InfluxDB clusters to catch issues. Finally, engage with the community and support channels to stay updated on best practices and new features. From my experience, these steps helped ensure a smooth implementation and long-term success with InfluxDB.

InfluxDB is a strong choice for time-series, especially when you really need real-time insights and efficient storage of high-volume metrics. The flexibility of a query language such as Flux allows for powerful data analysis, but it also does require some learning investment. From a product perspective, balancing advanced features with ease of use is important. Overall, InfluxDB can deliver great value if you align it well with your business needs and user experiences, and if you plan for scalability and ongoing maintenance. This approach ensures the product stays useful and relevant over time, which is critical for any data platform. I would rate my overall experience with InfluxDB as an eight out of ten.

Hybrid Cloud

Amazon Web Services (AWS)