Monte Carlo Data

Training builds a model from scratch; fine tuning adapts one you already have. See the tradeoffs in cost, data, and results.

Nothing changed in your prompt, but your outputs did. Learn how to spot the warning signs early and stop bad data from spreading downstream.

At the end of Part 3, your monitors were live and routed, and we said something was going to fire. Well, something just did. Maybe it came in as a Slack message or an email, or maybe a new row at the top of the alerts feed. Before you go heads-down and start debugging, let’s …

In Part 2 of this four-part series, we worked out what’s worth monitoring on your particular agent, and in what order. At this point, the traces are arriving in Monte Carlo. You can already do more than just watch them flow in. You can open any conversation, run an evaluation on it, and get a …

ETL and orchestration is one of the most varied integration categories our customers ask for.  Alongside the mainstream tools we cover natively (Airflow, dbt, Fivetran, Azure Data Factory, and more), enterprises run dozens of others: commercial platforms, cloud-native services, and homegrown schedulers.  Every one of them runs pipelines that feed what’s downstream: the warehouse, the …

Four years ago, Monte Carlo shipped circuit breakers to stop broken pipelines before bad data hit a dashboard. In 2026, the same idea has a new job: stopping an agent before it turns bad data into a confident answer. In electrical engineering, a circuit breaker exists because prevention beats detection. You do not want an …

Every table you monitor has a rhythm. Here's how AI learns it, what happens when that rhythm shifts, and how to build a detector that keeps up.

Whether you're monitoring a handful of models or managing AI at enterprise scale, you need AI observability tools. Let's dive into it.

There’s a clear narrative sweeping the software world these days: AI is getting dramatically cheaper. Or, to be more specific, inference is getting cheaper.  Various researchers have been quantifying this:  Stanford’s AI Index, for example, tracked the cost of querying a GPT-3.5-equivalent model falling from $20.00 per million tokens in late 2022 to $0.07 by …

Inside the Monte Carlo platform, our Troubleshooting Agent (TSA) works behind the scenes to analyze data and AI incidents, pinpointing root causes and suggesting fixes in real time. To keep TSA—and our other production agents—running efficiently, we rely on the Reinforcement Loop, an automated monitoring system designed to continuously evaluate agent performance, catch subtle inefficiencies, …

The discussion around enterprise AI often focuses on the choice between open-weight models, like Meta’s Llama and models from Mistral, vs. closed frontier models, such as Anthropic’s Claude and OpenAI’s GPT models.  While closed frontier models promise state-of-the-art reasoning without managing infrastructure,  open-weight models promise sovereignty, portability, and freedom from vendor lock-in. Public commentary has …