Operations | Monitoring | ITSM | DevOps | Cloud

From manual monitoring to proactive optimization, learn how Tipalti used AutoOps to save 10% annual costs. For a global payables automation leader like Tipalti, where financial transactions are the lifeblood of the business, infrastructure performance isn't just a technical goal; it's a core business requirement. Managing a complex ecosystem of databases, including Postgres, SQL Server, MongoDB, Kafka, and Elasticsearch, with a lean team of four engineers demands efficiency and powerful tooling.

When application performance monitoring detects a spike in latency or error rates, the immediate challenge is determining the underlying cause. APM logs address this by correlating performance metrics with the specific log events that occurred at the same time. Instead of switching between monitoring dashboards and manually searching through log files, APM log correlation consolidates both views.

Building a great developer experience is about more than just the code. It’s about creating a unified ecosystem where your tools work together seamlessly. That’s been the vision behind our work on the Mezmo MCP Server, and I’m excited to share it with you. At its core, the MCP Server is a universal remote for your data pipeline.

When assessing SaaS observability solutions, customers often explore features that are built into the platform, but there ia a whole collection of deployable libraries across all SaaS vendors. In Coralogix, we lead the way in deployable assets, with 4400+ alerts, dashboards, parsing rules, metric generation rules and more. But why should you care about these deployable assets, and why do they accelerate insight generation so profoundly?

Service Level Objectives (SLOs) are a cornerstone of modern software engineering. Defining alerts around SLOs has become standard practice, but many of the common patterns in use today miss the early signals that can tell a customer before an SLO breach has occurred.

When a web service slows down or errors spike, metrics can tell you what changed (active connections rise, error rate increases), but the root cause can sometimes be found in your logs (which IPs are hammering POST endpoints, 4XX/5XX occurrences). Put the two together and you get the full observability picture. Time-series metric trends to spot incidents, and line-level details to fix them fast.