Datadog

I like the unified view of synthetics for API tests and APM for code traces. I can set up a synthetic test to hit our login endpoint every minute from 5 different global locations, and if latency spikes, I can click a button to see the exact SQL query or downstream microservice that caused the delay. CI/CD integration for quality gates is also top tier, and we can block deployment if the Datadog performance baseline isn't met.

All in one, easy to leverage one vendor for many services

It's valuable due to the level of integration between the different products within the product. I enjoy the abundant documentation and especially the learning center. The UI is more user friendly than other observability platforms.

The log rehydration cost and the general pricing for custom metrics is painful. We often have to sample our traces aggressively (only keeping 1% of success logs) to keep costs down, which sometimes means we miss the context on rare edge case bugs. Also, the UI for building complex dashboards can be overwhelming for new hires compared to simpler tools.

Billing - line items / too many SKUs. All volume based and by range. Its also easy to turn on many things and then get a surprise bill at the end. also for how much our startup pays annually, i feel like the discounts should be larger / for any overages should be at the price we negotiated not list price

It's difficult for us to accurately estimate the change in cost/billing when adding a product or making changes to an existing one, so we sometimes get a surprise when the bill comes. The Datadog Agent Autodiscovery does not support every product that you support through other means, such as Gunicorn. I think you should emphasize new products (or products in the pipeline) more so that customers don't go to other vendors for them, such as Incident automation.