POC is easy. Production is where data projects get expensive and fragile

Most teams think the hard part of AI is getting the model to work. It’s not.

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Today, you can build a surprisingly capable AI prototype in days. With modern tooling and code assistants, an engineer can spin up a chatbot, a recommendation engine, or a document assistant faster than ever.

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The demo goes well, people get excited, and teams decide to deploy it in production. Then comes the real challenge: running it.

Because the moment AI touches real users, “Does it work?” is not the main concern anymore. It becomes:

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Can we trust it? Can we afford it? Can we operate it reliably?

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That’s where many AI projects start to fail.

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This isn’t new to LLMs. Teams have been hitting this wall for years with ML and data systems.

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Why POCs are deceptively successful

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Prototypes create confidence because they operate in controlled conditions:

• Small datasets
• Limited traffic
• No strict uptime requirements
• Minimal security constraints
• Few edge cases

If something breaks, someone restarts it. If outputs drift, prompts get tweaked. If latency spikes, nobody notices.

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A prototype only has to work once. During the live demo. We often joke when the live demo crashes, but that’s a good reminder of the needed amount of work before it can reach production.

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Because production has to work every time.

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What looked simple in development often becomes complex once reliability, scale, and cost enter the picture. This is where teams discover that a few AI engineers aren’t enough. They need experts from several horizons: SRE, Cloud, Software.

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The Production reality shock

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When AI moves from demo to deployment, it becomes an operational challenge.

Several pressure points tend to surface at the same time.

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Costs

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Early usage rarely reflects real-world behaviour. Once adoption grows, costs can move in unexpected ways:

• Token consumption increases with user activity
• Embedding pipelines run continuously
• Vector databases need to scale
• Inference workloads grow
• Retries multiply during failures
• Logging and tracing add overhead

Individually, each component seems manageable. Together, they can materially change the economics of the system.

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Many teams don’t realize their unit economics are misaligned until usage becomes meaningful, at which point the architecture is already in place. And when teams start comparing the costs vs the created value, they realise the economics don’t work.

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Reliability is harder than accuracy

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Traditional software is largely deterministic. Given the same input, you expect the same output.

AI systems introduce probability into environments that typically expect predictability.

You start seeing behaviors like:

• Latency
• Non-deterministic responses
• Dependency on external model providers
• Rate limiting

Traditional systems tend to fail loudly: alerts trigger, requests error, dashboards light up. Data, ML, and AI systems fail quietly. Responses become inconsistent. Quality drifts slowly enough that it can take time before anyone notices.

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Subtle failures are harder to detect and harder to debug.

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Observability is frequently an afterthought

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Many teams invest heavily in building the capability but far less in understanding how it behaves once deployed.

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Common gaps include:

• No prompt or request tracing
• Limited cost attribution
• Sparse quality metrics
• No structured feedback loops
• Minimal visibility into model behavior over time

This is not just for AI projects. Many software teams invest in observability only after facing production incidents that impacted users’ trust.

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Without observability, teams are left guessing. And you can’t improve what you can’t see. Don’t say everything is logged in ClickHouse if you have no way of structurally accessing the data.

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Engineers become operators

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One of the quieter shifts happens within the engineering team itself as more time gets pulled into operational work:

• Investigating cost spikes
• Tweaking prompts under pressure
• Reprocessing failed jobs

This isn’t a sign of poor engineering, it’s just a reflection of how operationally demanding production AI can be when the supporting infrastructure isn’t designed with these realities in mind.

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The root cause: Optimizing for the demo

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Most teams optimize for what the organization values early on:

• Speed to demo
• Visible innovation
• Stakeholder excitement
• Competitive pressure

A successful prototype proves possibility. It answers the question, “Can we build this?”. Production asks a different set of questions:

• Can it run reliably?
• Is it economically sustainable?
• Can the team operate it without constant intervention?
• Do we understand how it behaves?

A demo only rewards momentum.

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Production AI is an infrastructure problem

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In practice, many of the hardest data & AI problems are infrastructural.

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Production-ready systems typically require:

• Architecture designed with scale in mind
• Cost visibility from early stages
• Evaluation mechanisms to track output quality
• Guardrails and fallback paths
• Monitoring that goes beyond uptime
• Clear ownership within the team

Even if progress feels slightly slower at the start, when these elements are treated as foundational, teams tend to move faster later.

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Because redesigning systems under production pressure is rarely easy.

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When should teams think about production?

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Early.

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Speed still matters, and experimentation is valuable. But a small amount of production thinking early on can prevent large structural changes later.

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Simple questions help:

• If usage grows 10x, what breaks first?
• Do we understand the cost drivers?
• How will we detect quality drift?
• What happens when a dependency fails?

You don’t need every answer immediately. But designing with these questions in view tends to produce more resilient systems.

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And deploy fast.

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Don’t spend six months optimizing a prompt before doing a full blown launch. Get a few users willing to try an experimental feature. Get feedback. Understand the required operational load early and include improvements in your backlog. Build confidence.

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Closing thought

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Building an AI demo is more accessible than ever. That’s a positive shift. It enables teams to explore ideas quickly and learn faster.

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But the gap between prototype and production remains significant.

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Anyone can build a demo.

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Running AI reliably, economically, and at scale is a different kind of engineering challenge.

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And increasingly, that’s where long-term value is determined. Not by what the system can do in a controlled environment, but by how well it performs in the real one.

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AI initiatives shouldn’t be driven by PR or hype, but by durable value. Ensure you are building something valuable, iterate, and discover the production requirements.

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