One main issue I found with IBM Instana Observability was the customer support, which was very poor when we needed some features to be enabled or integrated into IBM Instana Observability sensors. They used to take a long time to respond and implement the features we requested, and customer support was not really great. They were slow to integrate new features, whereas competitors were providing better features at a faster pace. IBM Instana Observability is not really stable; I would say it is stable approximately 95 percent of the time.
The interface of IBM Instana Observability is really slow, especially when there is a lot of data and when there are a lot of traces. The interface almost hangs up. The alerting integrations in IBM Instana Observability are mostly reliable, though it depends on the filter I put. The problem is that the filter is somewhat complex to integrate. I had many false positives with IBM Instana Observability. Then I fixed the filters and it became more accurate on the errors reported. Also, errors gradually decreased from around 10 to 500 errors per 100 calls to one or two.
The tool should offer more LDR AI models. The monitoring is just a status, but the next level of a monitoring solution is to prevent an accident or stop an accident status. The solution should be to prevent accidents. Previously, monitoring might have been scanning and alerting and then sending information to the IT manager. The lack of support is a human resource problem.
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Application Performance Monitoring (APM) and Observability help improve the efficiency of applications by providing visibility and insights into system performance.
Application Performance Monitoring and Observability involve tracking and analyzing the performance of applications and infrastructure. APM focuses on detecting and diagnosing performance issues, while Observability emphasizes gaining insight into the internal state of systems. By combining these approaches, IT teams can ensure...
One main issue I found with IBM Instana Observability was the customer support, which was very poor when we needed some features to be enabled or integrated into IBM Instana Observability sensors. They used to take a long time to respond and implement the features we requested, and customer support was not really great. They were slow to integrate new features, whereas competitors were providing better features at a faster pace. IBM Instana Observability is not really stable; I would say it is stable approximately 95 percent of the time.
The interface of IBM Instana Observability is really slow, especially when there is a lot of data and when there are a lot of traces. The interface almost hangs up. The alerting integrations in IBM Instana Observability are mostly reliable, though it depends on the filter I put. The problem is that the filter is somewhat complex to integrate. I had many false positives with IBM Instana Observability. Then I fixed the filters and it became more accurate on the errors reported. Also, errors gradually decreased from around 10 to 500 errors per 100 calls to one or two.
The tool should offer more LDR AI models. The monitoring is just a status, but the next level of a monitoring solution is to prevent an accident or stop an accident status. The solution should be to prevent accidents. Previously, monitoring might have been scanning and alerting and then sending information to the IT manager. The lack of support is a human resource problem.
The solution should include log management and improve reporting and integration with Cognos.