Designing for Small Language Models vs LLMs: Architectural Trade-offs in 2025

For the last few years, “add AI” has often meant “call a big LLM API”.

That default has been useful. Large language models made it possible to ship prototypes quickly, experiment with new experiences, and explore use cases faster than we could have with classical ML.

By 2025, that default is starting to hurt architecture.

Teams are discovering that not every problem needs a huge general model sitting behind a single gateway. The trade-offs around latency, cost, privacy, and operability look very different when you have the option to run small language models (SLMs) close to your data and workloads.

Designing for SLMs vs LLMs is no longer just a model choice. It is an architectural choice.

In this article, we look at how that choice reshapes systems.

Why Small Models Are Back on the Table in 2025

For a while, the story seemed simple: bigger models performed better, so we tried to use them for everything.

Now, several forces are pushing teams to consider smaller models again:

• Latency expectations – Users expect AI assistance to feel like part of the UI, not a blocking step. Even 1–2 seconds can be too slow in tight loops.
• Cost pressure – Per-token pricing and high inference costs add up quickly at scale, especially for internal tools or low-margin products.
• Data locality and privacy – Some data cannot leave a region, a VPC, or even a device. Shipping it to a central LLM endpoint is not acceptable.
• Specialisation – Many use cases need narrow competence more than broad generality. A focused model can do one job extremely well.

At the same time, tooling and hardware have improved:

• Quantisation, distillation, and efficient architectures make small models more capable.
• Libraries and runtimes make it possible to deploy SLMs on commodity hardware or even edge devices.

The result: we now have a meaningful choice between big shared LLMs and smaller, specialised models – and that choice shows up directly in system design.

The Architectural Shape of LLM-Centric Systems

When you design around a large, general LLM, the architecture tends to take a familiar shape:

• A central AI gateway or orchestration service.
• One or more LLM providers behind that gateway.
• Application services calling into the gateway via a small number of well-defined prompts and tools.

This brings some clear advantages:

• One place to integrate and govern prompts, tools, and providers.
• Shared capabilities – classification, summarisation, generation – that multiple products can reuse.
• Fast experimentation with new use cases without changing the underlying infrastructure too much.

But there are structural downsides:

• The gateway becomes a choke point for latency and reliability.
• Everything depends on a few high-latency, high-cost calls.
• It is tempting to push more and more work into the LLM, even when other patterns would be a better fit.

In practice, we see LLM-centric architectures gravitate toward:

• Thick orchestration layers – complex prompt flows and toolchains encoded in a single service.
• Broad permissions – the LLM often sees more data and has more capabilities than any single user would.
• Coupling across domains – many products depend on the same small set of prompts and models.

For genuinely open-ended problems, this is often the right trade-off. But when the problem is narrower, it can be more architecture than you need.

The Architectural Shape of SLM-Centric Systems

When you design around small language models, the architecture often looks more modular and closer to classic service design.

Instead of a single central LLM gateway, you get multiple model-powered components, each responsible for a specific job:

• An on-device or edge model for classification or ranking.
• A small model embedded inside a service for field extraction or normalisation.
• A domain-tuned model that scores, routes, or enriches events before they hit the core.

This changes several aspects of the system:

• Latency budget – SLMs can run close to the workload. That makes it easier to keep interactions under strict latency targets.
• Failure domains – A problem in one model affects a local capability, not the entire organisation’s AI layer.
• Data boundaries – More processing can happen without shipping data across regions or trust zones.

However, you trade some things away:

• You lose a single, universal “brain” that can handle arbitrary instructions.
• You take on more model lifecycle management: versioning, monitoring, and updating multiple small models.
• You need clearer contracts between models and services to avoid hidden coupling.

Architecturally, SLM-centric systems feel closer to “functions with learned behaviour” than to one giant reasoning engine.

Key Trade-offs: Small Models vs Large LLMs

From an architecture point of view, the trade-offs are not “which model is better?” but which model shape makes the system healthier for this problem?

Some axes to consider:

Latency and Interactivity

• LLMs:
  • Higher latency per call.
  • More tolerance needed in UX and back-end timeouts.
• SLMs:
  • Often fast enough to sit directly in request paths.
  • Enable richer, more interactive experiences.

Cost and Scale

• LLMs:
  • Expensive at high volume.
  • Good for low-volume, high-value operations.
• SLMs:
  • Cheaper to run once deployed.
  • Better for background tasks and high-frequency flows.

Data and Privacy

• LLMs:
  • Centralised processing; careful with what is sent.
  • Rely on provider guarantees and controls.
• SLMs:
  • Process more data in place.
  • Easier to respect strict data locality or residency requirements.

Operability and Blast Radius

• LLMs:
  • One path to monitor, but also one large blast radius.
  • Prompt changes can affect many products at once.
• SLMs:
  • More components to manage.
  • Issues are often more contained within a specific service.

A useful default in 2025 is:

Use large LLMs for genuinely open-ended reasoning and exploration. Use small models for specific, repeatable decisions on tight latency or strict data boundaries.

Designing Hybrid Stacks: Small-First, Large-When-Needed

Most real systems will not be “SLM-only” or “LLM-only”. They will be hybrids.

A common pattern looks like this:

1. SLMs close to the workload
   • Classify, route, rank, or enrich data at the edge.
   • Keep business-critical flows within latency and privacy limits.
2. LLM layer for complex reasoning
   • Handle ambiguous requests, long-context tasks, or cross-domain synthesis.
   • Used more sparingly, where its marginal value justifies latency and cost.
3. Clear contracts between layers
   • The SLM layer outputs structured signals (scores, labels, routes).
   • The LLM layer treats those as inputs, not hidden behaviour.

This hybrid approach changes some design habits:

• You think more in terms of pipelines than single-shot prompts.
• You invest in metrics and feedback loops that tell you when to move logic out of the LLM and into a smaller, specialised model.
• You treat the big LLM as a powerful but scarce resource, not the default answer to every problem.

Platform and Team Implications

Architectural choices around SLMs vs LLMs show up in how teams and platforms are structured.

Some implications we see:

• Platform teams need to support both “call the big LLM gateway” and “deploy a small model as a service or library”.
• Product teams need guidance on when they can safely embed models in their own services and when they should go through central capabilities.
• Governance has to adapt:
  • Central policies for what data can reach which models.
  • Standards for logging, monitoring, and rollback when models change.

The more you invest in these foundations, the easier it becomes to choose the right model for each job without creating a fragmented, fragile system.

How We Think About These Trade-offs

At Fentrex, we look at SLM vs LLM choices through an architecture lens first:

• What are the latency and availability needs of this flow?
• What are the data boundaries and regulatory constraints?
• How often will we change the behaviour? Can we encode it in code or configuration instead of a general-purpose model?
• What is the blast radius if this component behaves unexpectedly?

Large LLMs remain critical for some problems. Small models are quietly becoming the better fit for many others.

The goal is not to be “pro small model” or “pro LLM”. The goal is to build systems that remain understandable, operable, and sustainable as AI becomes part of the core architecture, not a sidecar.

Questions to Ask About Your Current Architecture

If you are reviewing your own systems, a few questions can uncover where SLMs vs LLMs matter:

• Where are we using a large LLM today mostly because it was the easiest way to get started?
• Which flows would benefit from lower latency or lower cost if we moved parts of the logic into a small model?
• Where are we shipping more data to a central model than we are comfortable with?
• Which parts of our AI behaviour could be turned into clear, testable contracts between services, instead of being buried in prompts?

Answering these honestly gives you a roadmap for restructuring AI-heavy systems in 2025 – not just around better models, but around better architecture.