# Which AI agent observability platform actually holds up for enterprise teams running agents at scale?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Hi G2, I keep noticing platform and data teams ask a version of the same question once they're past a handful of pilot agents in the <a class="a a--md" elv="true" href="https://www.g2.com/categories/ai-agent-observability">AI Agent Observability</a> space: which of these tools was actually built for enterprise scale, not just a single developer's local setup. Reliability, pricing at volume, and fitting into an existing data stack all seem to matter a lot more once you're past the proof of concept.</p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/monte-carlo/reviews"><strong>Monte Carlo</strong></a>: enterprise reviewers like that it plugs into a Snowflake or Databricks setup they already trust for data reliability, and extends that same monitoring into agent behavior. A few describe the agent observability side as newer than the core data product, but already useful if you're already on the platform.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/arize-ax/reviews"><strong>Arize AX</strong></a>: larger teams point to tracing, evaluation, and prompt experimentation living in one place as worth the setup effort. The recurring complaint at this segment is that enterprise pricing gets steep, and the learning curve is real for anyone outside engineering.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/langsmith/reviews"><strong>LangSmith</strong></a>: enterprise users describe searching millions of traces in seconds and relying on it for reliability and compliance work. Pricing that scales with trace volume came up more than once as something that's hard to forecast once usage grows past early testing.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/braintrust-2024-12-22/reviews"><strong>Braintrust</strong></a>: reviewers at this size cited the eval and tracing loop cutting debugging time by a meaningful percentage before release. Usage-based pricing on data processed and retained was the main friction point for high-volume production programs.</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">For teams that have actually gone past a pilot, how much of the enterprise pricing pain comes from trace and eval volume versus seat count? And has anyone paired Monte Carlo's data side with a separate agent tracing tool, or did the combined agent monitoring end up covering enough on its own?</p>

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## Comments
### Comment 1

&lt;p&gt;Trace volume is probably where the enterprise story gets interesting. Capturing everything sounds ideal until millions of routine traces make storage expensive and finding the failure that actually matters harder. I’d want to know whether teams end up sampling aggressively in production and, if so, how they decide what must be retained for debugging and compliance without losing the context behind rare agent failures.&lt;/p&gt;

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