Logz.io Open 360 Observability Platform (Subscription)

My main use case for Logz.io is to better understand the logs that we have, providing smarter observability. We generally use the Elastic Stack, and Logz.io integrates very well with it, and they even have their own observability platform; so log management and observability is mainly what I use it for day-to-day.

A specific example of how I use Logz.io for log management or observability involves our observability needs, where it helps us to trace the logs very effectively. It offers an out-of-the-box experience that is very easy to configure, allowing the team to debug issues in their applications easily, see how the data or the API call flows from one end to another, and identify bottlenecks in the application. On the log management front, it simplifies log management very well, especially with the AI capabilities it comes with out-of-the-box, analyzing the logs and mainly helping us in optimizing the cost of how we store the logs.

Recent AI integrations in Logz.io have really changed the game here because they provide very specific, actionable insights, saving our team from spending time digging through the logs, which can consist of millions and millions of lines. The AI agent does that work, unblocking our team so we just interact with the agent, ask it questions, and get more insights about the logs, helping us reduce our downtimes and increasing uptime, service availability, and overall performance.

Logz.io has positively impacted my organization by greatly improving the meantime to recover from issues; we experience less downtime now and have a clearer understanding of our codebase, aiding new team members in diagnosing errors more efficiently. The immediate response we get from Logz.io about ongoing issues enhances our understanding of incidents, with fixes and suggestions available, resulting in improved MTTR, better uptime, and happier customers who can carry out transactions and access their profiles seamlessly without disruptions.

Customer satisfaction scores before were around 60%, and now it has increased to over 75%; additionally, the uptime we had was below 99%, and now it stands at more than 99.5%, indicating we are performing much better.

The best features Logz.io offers, particularly in log management, include the AI integration, which immediately conducts root cause analysis based on issues without needing a lot of manual setup. The agents are very trained on real-world data, enabling them to truly understand what is happening in the application, the traces of the logs, and the flow, where the issues lie, and to come up with conclusions. If we have follow-up questions, we can just ask the AI instead of going through the logs, enhancing our productivity and allowing us to investigate issues faster and resolve things more quickly.

I can illustrate how the AI root cause analysis has changed my team's workflow by comparing it from before and now. Previously, we were very heavy on transaction data, and if there was a payment failure, it took a lot of time to properly investigate because of the numerous services that use that payment information or logs. Now, with the AI agent available, we do not need to deep dive into logs, and the manual activity has significantly reduced. This enables us to obtain relevant information within two or three minutes of the incident, and it is quite real-time, providing triggers for root cause analysis that automatically gives us insights and suggests what we need to do without needing to read through everything repeatedly, leading to quicker resolutions for payment issues.

The other significant element is the Logz.io platform itself, as it was previously similar to Elastic Stack but also includes extra features, making it easier to configure, set up, and everything else. I consider that one of the best features we have, effectively replacing our observability platform end-to-end.

Regarding the accuracy and reliability of Logz.io's AI capabilities, I would say they have been pretty consistent, which is brilliant because a model that does not hallucinate is extremely useful, and the real-world use case is good, so I am quite happy and satisfied with the AI agent.

As for improvements needed in Logz.io, I think cost or pricing is always a consideration, especially since I come from a startup and an Indian company, making us look for products that provide better value for money. Compared to the market, their pricing appears decent but might need reevaluation as we scale further to remain competitive.

The main concern for me is pricing, as feature-wise, I believe Logz.io is on par with other tools available in the market; however, it is not as widely discussed. Their features align with offerings from Elastic Stack and other prominent tools, including functionalities available from Prometheus or Grafana, providing centralized logging and other capabilities in the UI, so while the product direction is solid, improved pricing would immensely benefit us.

I believe we have covered almost everything regarding improvements needed for Logz.io.

I have been using Logz.io for quite a long time; it has been approximately two years since we started using Logz.io.

There has been no downtime with Logz.io, indicating stable performance.

Logz.io has managed our organization's growth well; we have actually scaled down slightly to optimize for cost, which has worked well. However, at this lower scale, we feel it does not deliver the expected value for the money we are paying, reiterating my concern about cost.

I have not had to reach out for customer support from Logz.io, as it has been very easy to set up and work with, thus far requiring no interaction with their support team.

Before using Logz.io, we were on Elastic Stack because it did not offer the same level of integration and required significantly more engineering bandwidth, including my own, for setup, which prompted our decision to switch to Logz.io, providing a better out-of-the-box experience with a similar backend.

We have saved about 60 to 70 percent of the time previously spent investigating issues, which represents a significant improvement. The accuracy of responses has also enhanced, leading to money savings and improved uptime, resulting in happier customers and overall positive metrics, indicating we have seen a return on investment.

My experience with pricing, setup cost, and licensing indicates that we feel we could extract more value from it. While their pricing is quite good compared to the industry, from my company's perspective, we expect better value.

We evaluated other market options before selecting Logz.io, including Dynatrace and DataDog, which were on the pricier side, leading us to conclude that Logz.io works much better for us.

I would rate Logz.io as an 8 out of 10 because it competes very well among the best offerings in the industry currently.

I assign it an 8 out of 10 because I am quite happy with the product and very satisfied with what I receive, but it would reach a 10 if, from an organizational standpoint, we could extract more value for money from it.

I have not explored much regarding Logz.io's governance or security, as I trust they comply with standards like SOC 2; however, with AI, different standards are emerging that we still need to address, and we have not done so yet, as our focus has been on foundational issues.

Logz.io is deployed in my organization directly, as we use their service through a consumption model, specifically on Azure, which is a public cloud. We use Azure only as our cloud provider. We did not purchase Logz.io through the Azure Marketplace.

My advice for others considering using Logz.io is to assess their use case. For smaller organizations like mine that seek value and scalability without extensive engineering bandwidth, Logz.io stands out as a better market solution since it allows engineering teams to focus on creative tasks rather than spending excess time on setups, offering logs and insights out-of-the-box, and the AI agent greatly optimizes the engineering team's availability.