Running a network of autonomous AI agents 24/7 may seem like an easy way to scale operations, but the reality is that costs can appear in unexpected places.

I recently conducted a stress test based on real SaaS workflows using Claude Code to estimate the requirements for running a small autonomous team of 8 agents continuously over 90 days. The projected infrastructure cost came out to approximately $14,000 per quarter.

The cost drivers extend beyond just model usage and include:
• Context windows that grow over time and require reprocessing every loop
• Repeated file scanning and redundant token usage
• Tool calls that fail and need to retry under load
• Memory and state syncing across long-running workflows

At scale, these small inefficiencies can compound rapidly. A single poorly optimized loop can significantly increase costs without enhancing output. This is often underestimated by teams when building agent systems from scratch.

This insight is part of the reason I developed Sistava. The aim was to eliminate much of the infrastructure work that underpins these systems, such as caching, execution structure, and state handling.

In the same setup I tested, the equivalent cost on Sistava is under $5,000 per quarter.

The takeaway is clear: Building agents is becoming easier, but running them efficiently at scale remains a challenge.

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