Your AI product works great in demos and local testing. Then you put it into production only to see it fail spectacularly.

In case you are wondering why there are so many toy projects around AI but not a single successful enterprise product till now, checkout the below code. This is from none otherthan the LlamaIndex (and others, such as Langchain, are not much different either).

from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.core import Settings

documents = SimpleDirectoryReader("./data").load_data()

Settings.chunk_size = 512
Settings.chunk_overlap = 50

index = VectorStoreIndex.from_documents(documents)

query_engine = index.as_query_engine(similarity_top_k=4)

That is all the code that is needed to perform similarity search (Top K) on your private data files. Simple isnt’t it? So you will come across half dozen YouTube videos claiming to have solved the private AI.

Except, it doesn’t work in production/enterprise scenarios. The reason, scalability.

The first rule of dealing with files in enterprise scnearios: never read a file into memory all at once.

You would not find Streaming API anywhere in these AI libraries today. Nor would a Startup hurrying to build an MVP take time to implement one. Leading to massive technical debt. Streaming APIs allow for the efficient processing of large datasets by reading files in chunks, instead of loading them all into memory at once. Without this, handling large data volumes becomes a nightmare. But because it takes time and effort to implement, it’s often ignored during MVP development, leading to significant technical debt.

The Cost of Ignoring Scalability

Ever wonder what happened to that startup that raised millions on their MVP but failed to scale? It’s simple—problems like the one in this code multiply, and what was once a quick and easy solution starts to collapse under the weight of production demands.

For instance, as the dataset grows, trying to load all the documents into memory at once can lead to performance bottlenecks, memory overloads, and crashes. In the real world, scalability is crucial. But many of these libraries—designed for fast prototyping—fail to consider these challenges.

Building for the Long Haul

When building products, especially AI solutions, always keep scalability in mind. Don’t just build for the MVP phase. Think about how your system will handle the growth of data, traffic, and complexity. Consider whether your codebase is sustainable or if it’s accumulating “technical debt” that will haunt you later.

So, how to avoid technical debt, at the MVP stage?

All startups build MVPs based on open source APIs directly or indirectly. Looking for scalability issues when picking the open source API for your next MVP can greatly improve your success rate for product scale in later growth stages.

How to Spot an API That Will Fail to Scale?

Open source allows one to build MVPs with ease. However, as you evaluate open source APIs for your startup product or MVP, there are several red flags that can indicate the API may not be able to handle enterprise-level demands in the later growth stages.

Here are some key signs to watch out for:

1. No Support for Asynchronous Operations

• Red Flag: The API operates in a strictly synchronous manner, meaning it processes requests one at a time and doesn’t allow for parallel processing or background jobs.
• Why It’s a Problem: Asynchronous operations, such as queueing or batching tasks, are essential for handling large volumes of data and processing requests in parallel. If an API doesn’t support this, it will likely fail under the weight of concurrent requests or large datasets, leading to slower response times or system crashes as the load increases.

2. Single-Point Processing and Lack of Caching

• Red Flag: The API doesn’t leverage caching or distributed processing for frequently accessed data or repeated queries.
• Why It’s a Problem: APIs that don’t use caching strategies for repeated tasks, such as similarity searches on static datasets, put unnecessary strain on resources by recalculating results each time. As data grows, this can dramatically increase processing time and load. A scalable API will use in-memory caching or have built-in mechanisms for distributed caching across servers to optimize repeated operations.

3. Limited Data Handling Capabilities

• Red Flag: The API doesn’t offer built-in support for streaming large datasets, batch processing, or pagination.
• Why It’s a Problem: As mentioned earlier, trying to load entire datasets into memory all at once is not feasible for large-scale applications. A lack of data streaming, pagination, or efficient file handling means the API will struggle to handle large volumes of data in production environments, causing performance bottlenecks or crashes.

4. Hardcoded Defaults That Don’t Scale

• Red Flag: The API relies on hardcoded settings, such as memory limits, timeout durations, or chunk sizes, that are not configurable or dynamic.
• Why It’s a Problem: An API with hardcoded limits is unlikely to adapt to changing data volumes or workloads. For example, hardcoded chunk sizes (like the Settings.chunk_size in the LlamaIndex code) may work well in small-scale applications but can cause memory issues or slow performance as data grows. Look for APIs that allow you to adjust these parameters based on your needs, ideally offering dynamic scaling based on the system’s load.

5. Lack of Horizontal Scalability

• Red Flag: The API was designed for single-node use without provisions for horizontal scaling (i.e., scaling out across multiple machines or servers).
• Why It’s a Problem: Horizontal scalability is crucial for growing systems that need to handle more traffic or larger datasets. If the API doesn’t support distributing tasks across multiple servers or clusters, it will bottleneck as traffic increases, resulting in slowdowns or downtime. APIs that offer integration with cloud-based services like Kubernetes or offer native support for load balancing and sharding are typically better suited for enterprise use.

6. Poor Documentation and Lack of Clear Best Practices

• Red Flag: The API’s documentation is sparse, unclear, or focused only on basic use cases, without addressing real-world challenges such as performance optimization, scalability, or fault tolerance.
• Why It’s a Problem: If an API doesn’t clearly document how to scale its usage or handle edge cases in production, you’re essentially flying blind when it comes time to deploy it in a more complex, high-demand environment. Comprehensive documentation should cover topics such as performance tuning, error handling, and scalability tips, along with real-world examples.

7. No Active Community or Support Channels

• Red Flag: The API lacks an active user base, community forums, or robust support channels.
• Why It’s a Problem: An active community can be invaluable when troubleshooting performance issues or scaling challenges. Without this support, you might be left to fend for yourself when the API starts to hit its limits. If you see that the API’s maintainers are unresponsive or that it doesn’t have frequent updates addressing performance or scalability issues, it could be a sign that it isn’t future-proof for enterprise environments.

8. Lack of Monitoring and Analytics Tools

• Red Flag: The API does not provide integrated tools for monitoring, logging, or analytics.
• Why It’s a Problem: Without proper monitoring, it’s difficult to identify when performance starts to degrade or when the system is being overloaded. APIs that come with built-in monitoring or offer easy integration with third-party tools like Prometheus, Grafana, or cloud-native monitoring services can help you proactively manage performance and scale effectively.

Final words

As you build your AI solutions, choosing the right API is crucial for ensuring that your product can scale smoothly from a small prototype to a full-fledged enterprise solution. Spotting the red flags early—like a lack of asynchronous processing, poor data handling, or hardcoded settings—will help you avoid the common pitfalls that many AI startups face when their MVPs fail to scale.

By understanding these risks and carefully evaluating the APIs you choose, you can save your team a lot of time and frustration in the long run.

Build with a passion to solve real problems. Please feel free to get in touch to seek help on picking the best architecture, solving scalability issues or building your next MVP for your Startup.