The Real Cost of Swapping Infrastructure

Source: Dev.to

The Real Cost of Swapping Infrastructure

I’ve gone through enough infrastructure evaluations as an architect to recognize the moment when the energy leaves the room. It’s not when someone questions the performance numbers or the cost model. It’s when someone pulls up the codebase and starts counting how many services need to change.

The infrastructure might be more reliable, easier to operate, or have better economics, but it doesn’t matter if getting there means touching stable production code across dozens of services. The conversation shifts from “should we do this?” to “can we afford to do this?” and the answer is usually no. That gap between “this is better” and “we can actually adopt this” is where many decisions stall or get turned down.

Architecture discussions tend to follow a familiar pattern. The whiteboard fills up with boxes and arrows, the trade‑offs look reasonable, everyone agrees that you’ll come out the other end better. Then someone asks: how much code do we have to touch?

That question isn’t about features or benchmarks. It’s about risk. Architects evaluate the blast radius of change alongside performance and reliability. Every line of application code that needs to move, every client library that needs to be swapped, every behavior that needs to be re‑learned increases the cost before you can even run a proof of concept.

For systems already in production, touching stable code introduces uncertainty. It stretches review cycles, kicks off regression testing, and makes rollback complicated. Good ideas often don’t make it past this point because weaving them into existing applications costs too much.

Why Code Changes Matter

• Risk amplification – each touched service expands the blast radius.
• Review overhead – larger changes require longer code reviews and more reviewers.
• Testing burden – regression suites must be run across many components, increasing CI time.
• Rollback complexity – undoing a multi‑service change is far more involved than flipping a configuration flag.

This is especially relevant for infrastructure on the hot path. When caching misbehaves, it can take other systems down with it. Teams are rightfully cautious about changes here, even when the infrastructure side of the proposal is compelling.

Teams trust behavior they’ve observed in production—how commands serialize, how errors surface, how retries behave under load. That behavior has been exercised millions of times, hardened by real traffic, load testing, and years of incremental fixes. In practice, this behavior acts as a contract between the app code and the infrastructure.

Contractual Compatibility (RESP)

For cache‑heavy systems built on Redis or Valkey, the contract is often the wire protocol itself – RESP (Redis Serialization Protocol). The application doesn’t depend on “a cache”; it depends on this specific way of talking to one.

When you hold the contract constant and change what sits behind it, the risk potential drops dramatically. Instead of rewriting cache layers or swapping SDKs across services, teams can:

1. Point existing Redis/Valkey clients at a compatible service (e.g., Momento).
2. Authenticate and issue the same commands they already use.

The infrastructure changes. The operational model changes. The application code largely does not.

Reducing Risk with Configuration Changes

Treating the swap as a configuration change rather than a refactor lets teams:

• Observe real production behavior without committing to a full rewrite.
• Rollback easily – simply change the endpoint back.
• Shift evaluation risk from application code to infrastructure, where it’s easier to monitor and reason about.

This approach doesn’t eliminate all risk. RESP compatibility has edges and limitations worth understanding—not every Redis command is supported. However, the shift in risk profile is significant: the bulk of the work becomes an operational concern rather than a code‑base concern.

Takeaways

• “Better” isn’t enough if achieving it requires destabilizing code no one wants to touch.
• Platforms that gain real adoption meet teams where they already are, respecting existing contracts and mental models.
• RESP compatibility exemplifies this philosophy: it lets teams keep the trusted client contract while swapping out the underlying service for benefits like scaling, availability, and reduced operational complexity.
• When evaluation feels reversible, teams engage honestly with trade‑offs instead of inventing reasons to stay put.

In practice, that reversibility often separates interesting technology from technology that actually gets adopted.