Large Language Models (LLMs) are transforming the way developers build apps—powering everything from text summarization pipelines to agent-driven control flows. But any developer who has tried to move from a promising prototype to a production-ready app knows: incorporating LLM inference into your application is far from straightforward.

While the models themselves are powerful, the inference infrastructure can be a real bottleneck. Here are the most common challenges developers face today—and how they can be addressed.

1. The Model Name Maze

When working with multiple inference providers, model naming is inconsistent and confusing:

• Ollama might call a model llama3.2:3b

• Hugging Face Hub lists the same model as meta-llama/Llama-3.2-3B-Instruct

  

This inconsistency makes your code fragile. You either hardcode provider-specific model names or maintain conditional logic just to support multiple environments.

2. Making Your App Portable

Developers need their apps to run seamlessly on a MacBook during development and scale to cloud infrastructure in production.

In reality, local development and production rarely align. Inference hardware differs between local and cloud inference, forcing you to use different models and inference software stacks. Cloud environments require precise configuration and sometimes different models entirely.

Without a smart abstraction layer, you end up with two separate setups—slowing down iteration and increasing the risk of “it works only on my machine” issues.

3. Switching Between Inference Providers

Cloud inference options are expanding — Together.ai, SambaNova, Hugging Face and others—but each comes with:

• Different APIs

• Different pricing

• Different model availability

Developers often want the flexibility to switch providers to optimize for cost, speed, or availability. Today, that usually means rewriting parts of your app or juggling multiple SDKs.

4. The Cost of Serverless Inference

Serverless GPU inference in the cloud is a game-changer for scaling, but it comes with a downside: cost.

• Running every development experiment in the cloud burns GPU hours unnecessarily.

• Teams are increasingly turning to local inference to avoid racking up large bills during prototyping.

5. Latency During Development

Beyond cost, latency is another reason developers prefer local inference.

• Serverless endpoints often have cold starts and network latency.

• Iterating on prompts or debugging AI behavior becomes painfully slow if every request has to cross the internet.

This is why many teams start on local inference, then only scale to cloud for production workloads.

How Tower Solves These Problems

The latest release of Tower.dev (blog + “pip install tower -U”) directly addresses these pain points with a unified LLM inference experience:

1. Smart Model Name Resolution: Use simple model family names like "llama3.2" in your code. Tower automatically resolves them to the right model

   

2. Local-to-Cloud Portability: Prototype locally with zero cloud cost. Deploy the same code to the Tower cloud with serverless GPUs—no rewrites needed.
   
   

3. Seamless Provider Switching: Switch between Hugging Face Hub, Together.ai, or other supported providers without touching application code.
   
   

4. Cost, Latency, Accuracy Optimization: Use local inference during development to save on GPU costs and cut latency. Move to serverless inference in production for scalability and access to better model variants.

If you’re building LLM-powered applications, now is the time to simplify your workflow. 

1. Read the Tower announcement 

2. Sign up for Tower Beta

3. Get the latest SDK version (“pip install tower -U”)