Mahmoud Zalt

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The AI infrastructure landscape is shifting faster than most realize, and if you are building an AI platform today, the old playbook is already breaking. 🚀

The assumption was that we’d always be dependent on proprietary APIs from OpenAI, Anthropic, or Google. That is no longer true.

Open-weight models are rapidly closing the performance gap at a fraction of the cost. Look at the massive strides from DeepSeek, Qwen, and Llama. This completely changes the unit economics for us as founders.

When your platform runs autonomous agents handling complex, high-volume workloads, relying exclusively on third-party APIs is a massive margin killer. We are tying our scale to someone else's pricing. To survive and protect our margins, we have to stop renting intelligence and start owning our infrastructure.

That is exactly the path I am navigating with Sistava.com. Running a stress-test simulation of 500 AI employees for 3 months made it crystal clear: the pure cloud-hosted loop scales your costs faster than your product.

The real bottleneck now isn't software; it's hardware. GPUs are expensive, supply is tight, and transitioning from cloud inference to bare metal requires serious architecture strategy.

If you are a founder currently designing agent infrastructure or trying to optimize your compute loops, let’s connect. I'm always open to trading notes, data, and lessons from the trenches.

In the era of autonomous AI agents, observability is no longer just an operational concern. It has become a core part of the system itself.

If you're building agentic applications, a production-grade observability architecture should include:

⭕ An open observability stack
⭕ End-to-end data capture
⭕ Correlation IDs across every workflow
⭕ Evaluation of decisions, not just outputs
⭕ Monitoring beyond infrastructure metrics
⭕ Closed feedback loops that feed operational data back into agents

The last point is the most important.

Observability is no longer only about debugging. It's the feedback layer that enables agents to learn, adapt, and improve over time.

Before designing the agent, design the observability layer underneath it.

Prompts can improve what agents say and do. Observability is what enables continuous improvement at scale.

Detailed breakdown sistava.com/en/insights/observability-first-ai-…

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.

👉 Hire AI Employees sistava.com

What I learned from the first 100 activated users of Sistava.com, just 6 weeks after launching.

The most important signal wasn’t the total usage volume. It was what people chose to automate first.

As a solo founder myself, I’m living the same grind, so seeing how other founders actually delegated to AI right out of the gate was fascinating.

📊 Here is the exact breakdown of the work they handed off:

* 43% Marketing: Content creation, distribution, and execution.
* 31% Sales: Cold outreach, follow-ups, and pipeline management.
* 17% Operations: Admin, coordination, and internal workflows.
* 9% Support & Finance: Customer tickets and ad-hoc tasks.