Logs are essential for understanding what’s happening across your environment, but handling vast amounts of log data can be complex and costly.
Logging as a service (LaaS) refers to log management and analytics service deployed via SaaS – so the solution is deployed on the vendor’s infrastructure. Unlike self-hosted log management solutions, the cloud-native architectures of LaaS platforms enables seamless scalability and little manual maintenance.
At Logz.io we’ve introduced an AI Agent in the Open 360™ platform that automatically delves into your stack, fine-tunes your workflows and enables conversation directly with your log management systems and data. This innovative capability is based on generative AI and designed to immediately accelerate and enhance your observability practices.
Through the power of the AI Agent, you can:
Leverage an automated chat interface to converse with your log management data to enable and inform investigation.
Automate manual log querying to empower in-depth analysis and enact intuitive, targeted response.
Enable less experienced analysts on your teams responsible for log management to immediately benefit from deep system insights and the inputs of experienced teammates.
Logz.io offers numerous ways to reduce noisy data that needlessly drives up your overall costs. Our Data Optimization Hub allows users to easily identify and filter out unneeded data in a single UI.
Additionally, customers can distribute their data without significantly sacrificing availability to our cost-efficient Cold Tier for storage, depending on the data’s value and use case. Customers can also translate high-volume, lower value logs into more useful metrics through LogMetrics.
This way, we help enable an intelligent data pipeline that specifically empowers customers to:
Within Logz.io’s Log Management solutions are numerous capabilities that measurably reduce Mean Time to Response (MTTR) from production incidents or service degradations. Our querying time is 4-5 times faster with empty cache than open source solutions, which helps users get answers about their environment quickly.
One of today’s biggest log management challenges is the pervasive issue of too much noisy and useless data, driving up investigation times and increasing MTTR. Logz.io’s unique, AI-backed Data Optimization Hub makes it easy to remove noisy log data that obscures critical insights needed to troubleshoot quickly. Customers can further utilize Logz.io’s self-service tools or direct advice from our Support Engineers to identify and remove noisy data.
Logz.io Log Management is also fully integrated with our AI Agent technology that enables you to converse directly with your data and reduce MTTR by getting to the bottom of issues much faster.
As cloud-native technologies grow in popularity and add complexity to your environment, ease of integration is a key concern. Open source logging tools (think Fluentd, FluentBit, and OpenSearch) are helpful in this regard because they’re purpose-built to integrate with cloud-native environments.
Community-driven innovation, constant improvement at the hands of millions of developers, lack of upfront cost, avoiding vendor lock-in and integration with numerous observability backends are among the reasons why companies could choose open source.
However, open source does have its drawback for log management. While the software itself is free, there can be significant costs to maintaining and managing open source tools on your own. As your data volumes grow, you may need to add and manage more components in the pipeline, which can create growing costs in engineering maintenance hours.
Logz.io log management and analytics service is deployed via SaaS – managed, scaled, supported and optimized on our highly-resilient AWS cloud infrastructure. Unlike self-hosted log management solutions, the cloud-native architectures of Logging as a Service (LaaS) platforms provide seamless scalability and eliminates manual maintenance.
A fully-managed SaaS provides all the support you need for your specific use cases — something open source or DIY/self-hosted solutions may not support, or not without significant customization. Additionally, open source technology licensing changes occur frequently, creating potential challenges. These are all important factors to consider when considering a log management SaaS or open source/DIY options.
Going back to the MTTR discussion, correlating logs with your other telemetry types (including metrics and traces) helps provide critical context when events, production issues or system latency are impacting your environment. The combination of observing logs, metrics and traces in your environment and having the ability to correlate them will put you on the path to full stack observability.
Finding these root causes of issues and quickly understanding how changes in production affect surrounding services are major reasons for telemetry correlation. By cross-referencing new deployments and configurations against your observability data, you can get to the bottom of issues faster and discover your path to resolution.