Hey everyone, welcome back to My Weird Prompts. It is February twenty-fifth, twenty-twenty-six, and we are diving back into the digital trenches. I am Corn, and I am here with my brother, the man who has probably spent more time looking at GitHub documentation this week than looking at the actual sun. Herman, how are the eyes holding up?
Herman Poppleberry here. And you are not wrong, Corn. My retinas are basically vibrating at the frequency of a high-refresh-rate monitor at this point. Although, to be fair, the documentation for some of these new A I gateway projects is actually quite illuminating in its own right. It is a very exciting time to be an engineer in this space because we are finally moving past the "wild west" phase of just hitting A P I endpoints and moving into the "civil engineering" phase of building robust, scalable infrastructure.
It really is. And today we have a fantastic prompt from Daniel that is going to let us dive right into the deep end of that technical pool. Daniel is asking about A I gateways, proxies, and middleware. He is specifically interested in projects available for developers to integrate into their own stacks right now, whether that is through Docker containers or as direct dependencies. He wants to talk about L L M routing, Model Context Protocol aggregation, and redundancy.
This is such a timely topic, Daniel. We were just talking about the arc of deprecation in episode eight hundred and eight, discussing how Anthropic and Google handle their model lifecycles differently. One of the biggest pain points we identified there was the friction of constantly updating your application code every time a model version changes, a provider goes down, or a new, cheaper model is released. A I gateways are essentially the architectural answer to that problem. They are the "Nginx" of the A I era.
It is the decoupling of the application logic from the underlying model provider. Instead of your app talking directly to OpenAI or Anthropic or a local Llama instance, it talks to a middle layer that handles the complexity for you. Now, Herman, for someone like Daniel who is looking for specific projects to actually implement in twenty-twenty-six, where do we even start? The landscape has exploded in the last year.
It really has. If we are looking at the heavy hitters in the open source space right now, we have to start with Lite L L M. If you are a developer and you are not at least aware of Lite L L M, you are probably working twice as hard as you need to. It is arguably the most popular project in this category because of its sheer versatility. You can use it as a Python library, which makes it a direct dependency in your project, or you can run it as a standalone proxy server using Docker.
I have seen a lot of people moving toward the proxy server approach recently, especially in larger enterprise setups. Why do you think that is? Is it just about language agnosticism, or is there more to it?
That is a big part of it. If you run the Lite L L M proxy in a Docker container, your application can be written in Go, Rust, JavaScript, or whatever you like. It provides an OpenAI-compatible endpoint. So, your app thinks it is talking to OpenAI, but Lite L L M is actually translating those requests on the fly to over one hundred different L L Ms. But the real power, and this addresses Daniel's point about redundancy, is in the load balancing and fallbacks.
Let's talk about that redundancy piece because that is critical for production apps. How does a gateway like Lite L L M actually handle a scenario where, say, Claude three point five Sonnet is suddenly returning five hundred errors or hitting a rate limit?
This is where it gets really cool. In your Lite L L M configuration file—usually a simple Y A M L file—you can define a list of models and priorities. You can set it up so that if your primary model fails, it automatically retries with a secondary model. For example, if G P T four o is down, it can immediately fail over to Claude three point five or Gemini one point five Pro. You can even set up "cooldown periods" so it doesn't keep hammering a provider that is clearly struggling. It handles all that logic at the infrastructure level, so your application code stays clean. It just gets a successful response back, even if the backend provider had to change mid-request.
That is huge for reliability. But I am curious about the performance side of things. If we are adding a whole extra layer, a proxy in a Docker container, aren't we introducing significant latency? In a world where we are fighting for every millisecond in L L M responses, does that middle layer become a bottleneck?
That is a common concern, but the reality is that the overhead of the proxy itself is usually negligible compared to the actual inference time of the L L M. We are talking about a few milliseconds of processing time versus hundreds or thousands of milliseconds for the model to generate tokens. Projects like Lite L L M are built on top of high-performance frameworks like Fast A P I and Starlette, so they handle concurrency very well. In fact, you might actually save time by using a gateway because it can handle things like request queuing and smarter load balancing across multiple A P I keys or accounts.
That is a good point. If you have five different OpenAI A P I keys to bypass rate limits, the gateway can rotate them for you. You don't have to build that logic into your app. Now, Daniel also mentioned another project type, the all-in-one A I gateways. I have been seeing a lot of buzz around One A P I. Have you looked into that one much lately?
Yes, One A P I is another very strong contender, particularly popular in the self-hosted community. It is a Go-based project, which makes it incredibly fast and efficient as a Docker container. What I like about One A P I is its focus on the management interface. It gives you a very clean dashboard to manage your channels, your tokens, and your usage statistics across dozens of different providers. If you are running a small team or a company and you want to give everyone access to A I without handing out individual A P I keys, One A P I is a fantastic way to centralize that. It even has a built-in "quota" system where you can limit how many "credits" a specific user or application can spend.
It seems like the difference between Lite L L M and One A P I is almost a matter of philosophy. Lite L L M feels very developer-centric, very focused on the Python ecosystem and programmatic configuration. One A P I feels more like a piece of infrastructure you set up and manage via a U I.
That is a fair assessment. Though Lite L L M has been adding more U I features recently, One A P I was built with that administrative layer as a core priority from day one. There is also a project called Portkey, which has an open-source gateway that is very impressive. They focus heavily on what they call the "A I Gateway," which handles things like request retries, timeouts, and even caching. Caching is another huge piece of this puzzle for developers. If two users ask the exact same question, why pay for the tokens and wait for the latency a second time? The gateway can just serve the cached response.
I think we should dig into that caching aspect a bit more. Is it as simple as a key-value store of the prompt and the response? Because with L L Ms, even a one-character difference in the prompt can change everything.
Most of these gateways offer simple exact-match caching, but some are starting to look at semantic caching. That is where it gets more complex because you have to run an embedding model to see if the new prompt is semantically similar enough to a previous one to justify using the cache. Usually, for a developer looking for a straightforward Docker setup, a standard exact-match cache using Redis is the way to go. Lite L L M and Portkey both support Redis backends for this, which makes it very easy to scale. If you are running a R A G system—Retrieval Augmented Generation—where the prompts are often very similar, caching can save you thirty to forty percent on your A P I bill.
Okay, so we have Lite L L M for the Python-heavy or highly configurable stacks, One A P I for a more managed infrastructure feel, and Portkey for a focus on observability and caching. But Daniel also asked about something that is really on the bleeding edge right now: M C P aggregation. For those who aren't familiar, M C P is the Model Context Protocol, which Anthropic released to standardize how A I models interact with local and remote data sources and tools. Herman, how are gateways evolving to handle this?
This is where the conversation shifts from just routing prompts to routing capabilities. M C P is a game changer because it allows you to build servers that expose specific tools, like a database search tool or a GitHub integration tool, and any M C P-compatible client can use them. The problem Daniel is pointing out is that as you build more of these M C P servers, your application has to manage all those connections.
Right, if you have ten different M C P servers for different data sources—one for Slack, one for Postgres, one for Google Drive—you don't want your main application to have to maintain ten separate persistent connections and figure out which tool belongs to which server.
Precisely. We are seeing the emergence of M C P proxies or aggregators. The goal here is to have a single gateway that sits between your L L M and all your M C P servers. You point your L L M to this one gateway, and the gateway says, "I have fifty different tools available across these twelve backend servers." When the L L M calls a tool, the gateway routes that call to the correct server, gets the result, and passes it back. It is essentially a unified interface for the model's entire toolset.
Are there specific projects Daniel should look at for this right now? I know it is still a rapidly evolving space.
It is very early. There is a project called M C P Proxy that is starting to gain some traction. There are also efforts within the Lite L L M community to add M C P support directly into the gateway. The idea would be that your Lite L L M configuration wouldn't just include model endpoints, but also M C P server endpoints. So, your app would send a request to Lite L L M, and Lite L L M would not only choose the best model but also attach the relevant tool definitions from your aggregated M C P servers. It makes the "agentic" workflow much easier to manage.
That sounds like the holy grail of A I architecture. A single point of entry for both the intelligence and the tools that intelligence can use. But doesn't that create a massive security risk? If you have one gateway that has access to your databases, your GitHub, and your cloud infrastructure via M C P, and that gateway is also exposed to an L L M that might be susceptible to prompt injection... that feels like a lot of power in one place.
You are hitting on a vital point, Corn. We actually talked about this in episode six hundred and seventy-one when we discussed securing model weights and the overall A I attack surface. When you centralize everything into a gateway, that gateway becomes the most sensitive piece of your infrastructure. It is the keys to the kingdom. If a developer is setting this up in a Docker stack, they need to be incredibly careful about how that gateway is exposed.
So, what are the best practices there? If Daniel is putting this in a Docker Compose file, what should he be thinking about beyond just getting it to work?
First and foremost, the gateway should never be directly exposed to the public internet unless it has a very robust authentication layer. Most of these projects support A P I key authentication for the gateway itself. You should use that. Secondly, you should use Docker's internal networking to ensure that only your application container can talk to the gateway container. And for the M C P side of things, you want to implement the principle of least privilege. Each M C P server should only have the bare minimum permissions it needs to do its job. Don't give an M C P server full admin access to your database if it only needs to read from one table.
That makes sense. It is the same old security principles, just applied to a new type of traffic. I want to go back to the redundancy piece for a second. Daniel mentioned L L M routing. We talked about failover, but what about smart routing based on the complexity of the task? For example, if a user asks a simple question, I want to route it to a cheap model like Haiku or G P T four o mini. If it is a complex coding task, I want it to go to Opus or G P T four o. Can these gateways handle that automatically?
Yes, and this is where it gets really interesting for cost management. Lite L L M has a feature for this, and there are other projects like Martian that specialize in this kind of model routing. The idea is to have a small, very fast classifier model that looks at the incoming prompt and decides which backend model is best suited to handle it. Some gateways allow you to define rules based on the prompt content or metadata. For instance, if the prompt contains the word "code," route to a specific model. Or if the character count is over a certain limit, route to a model with a larger context window.
That is a great way to optimize spend. It's almost like the gateway is acting as a pre-processor. It analyzes the intent before the expensive model even sees it.
And it is not just about cost. It is also about latency. If you can answer thirty percent of your queries with a model that is ten times faster and fifty times cheaper, your user experience improves dramatically. The gateway makes this transparent to the user. They just see a fast, accurate response every time.
We have covered a lot of ground here, from Lite L L M and One A P I to the newer M C P aggregation ideas. Before we go any further, I think we should take a quick breath.
Good idea. I could talk about this for hours, but I know we have more to get through.
Dorothy: Herman? Herman, bubbeleh, are you there?
Oh... Mum? Mum, I am actually recording the show right now. Can I call you back?
Dorothy: Oh, I am so sorry, sweetheart. I didn't realize. I just wanted to remind you, you have that dentist appointment tomorrow morning at nine. Don't forget, you know how they are about the cancellation fee. And I made some of that vegetable soup you like, I left a container by your door. Just make sure you put it in the fridge right away, don't let it sit out.
Okay, Mum. Thank you. I will check the soup. I have to go now, we are live on air.
Dorothy: Oh, okay! Hello to Corn! You boys have a nice talk about your computers. Bye-bye!
Hi Dorothy! Thanks for the soup!
Sorry about that, everyone. My mother... she has a sixth sense for calling exactly when I am in the middle of a technical deep dive.
Honestly, it is charming. And she is right about that soup, her vegetable soup is legendary. But back to the world of A I proxies. Before the interruption, we were talking about smart routing. I want to pivot slightly to the developer experience. If Daniel is looking to integrate these as a dependency versus a container, how should he make that choice? When does it make sense to just import a library versus spinning up a whole new service in his stack?
That is a fundamental architectural question. If you are building a simple Python script or a small, self-contained application, using something like the Lite L L M library as a direct dependency is incredibly easy. You just pip install it, and you have instant access to all those providers with a single A P I format. It keeps your deployment simple and doesn't require you to manage a separate Docker container or worry about network latency between services.
But I assume there are limits to that as the project grows?
Once you move into a microservices architecture, or if you have multiple applications that all need to share the same A I infrastructure, the containerized proxy is the way to go. By running the Lite L L M proxy or One A P I as a standalone service in your Docker stack, you centralize your A P I key management, your rate limiting, and your logs. If you need to update your model routing logic, you do it in one place, and every service in your stack benefits immediately. Plus, as we mentioned, it makes your stack language-agnostic. Your frontend team writing in TypeScript and your backend team writing in Go can both use the exact same A I gateway.
That centralization also seems important for observability. If you have five different apps calling A I models, it is a nightmare to track total spend and performance across all of them if they are all making direct calls.
Oh, absolutely. Observability is one of the biggest "hidden" benefits of using a gateway. Most of these projects integrate directly with tools like LangSmith, Helicone, or even standard Prometheus and Grafana setups. You can get a single dashboard that shows you exactly how many tokens you are using, what your average latency is, and which models are failing most often. For a developer like Daniel, who is working in technology communications and A I automation, that kind of data is gold. It allows you to prove the R O I of your A I initiatives and catch issues before they affect users.
Let's talk about the middleware aspect. Daniel mentioned middleware specifically. In the context of A I, what does that actually look like? Is it just about modifying the prompt on the way in, or is there more to it?
It can be both. A I middleware is often used for things like P I I masking, where you automatically scrub personally identifiable information from a prompt before it ever leaves your infrastructure. This is huge for compliance. You can also use middleware for prompt injection detection. There are specialized projects like Lakera Guard or Giskard that act as a security layer, scanning incoming prompts for malicious patterns and blocking them at the gateway.
I have also seen people using middleware for "prompt enrichment." For example, automatically hitting a vector database to find relevant context and injecting that into the prompt before it goes to the L L M.
Yes, though that is starting to blur the line into what we call R A G, or Retrieval-Augmented Generation. But doing it at the gateway level is an interesting approach because it means your application doesn't even have to know about the vector database. It just sends a question, and the gateway handles the retrieval and the final prompt construction. It is a very powerful way to simplify application code. You could even have middleware that translates the response into a different language or formats it into a specific J S O N schema before it returns to the app.
It sounds like we are moving toward a world where the L L M itself is just one small component of a much larger, more complex "A I operating system." The gateway is the kernel that manages the resources and the communication between all the different parts.
That is a very apt way to look at it. We are seeing this shift from "chat" to "do," which we explored in episode seven hundred and ninety-five. When you move to agentic A I, the complexity of managing all those moving parts becomes the primary challenge. A robust gateway isn't just a nice-to-have anymore; it's a foundational requirement. If your agent needs to call five different tools and three different models to complete a task, you need a central nervous system to coordinate that.
So, if Daniel is looking to get started this weekend, what is his "hello world" for an A I gateway?
I would say the easiest path is to pull the Lite L L M Docker image. Create a simple configuration file with one or two of his existing A P I keys, and point a basic script at the Lite L L M endpoint instead of the OpenAI endpoint. Once he sees that working, he can start playing with the more advanced features like load balancing or adding an M C P server to the mix. It is one of those things where once you set it up, you'll wonder how you ever lived without it.
I think it's also worth mentioning that for someone like Daniel, who is an active open-source developer, these projects are great places to contribute. Because the field is moving so fast, there are always new providers to add, new M C P features to implement, and better ways to handle observability.
Definitely. The Lite L L M team in particular is incredibly responsive to the community. They are shipping updates almost daily. It is a great example of the kind of high-velocity open-source development that is driving this whole A I revolution forward. And because it's written in Python, it's very accessible for a lot of developers to jump in and add a new provider or a custom middleware hook.
We have talked a lot about the technical side, but I want to touch on the strategic side for a moment. For a company or a developer, does using a gateway increase or decrease your vendor lock-in? On one hand, you are no longer locked into OpenAI's S D K. But on the other hand, you are now dependent on this gateway project.
That is a classic architectural trade-off. However, in this case, I would argue that the gateway significantly decreases your overall risk. If you are using an open-source gateway like Lite L L M or One A P I, you own that infrastructure. If the project maintainers disappear tomorrow, the code is still there, and it is still compatible with the standard OpenAI A P I format. You have the freedom to switch backend providers in minutes. That flexibility is worth the small overhead of managing the gateway. You are essentially betting on an open standard rather than a proprietary silo.
It’s the difference between being locked into a single proprietary A P I and being "locked into" an open-source standard that gives you access to everything. I know which one I would choose.
And as we saw with the recent model deprecations we discussed in episode eight hundred and eight, that ability to pivot quickly is becoming a survival skill for A I companies. If a model you rely on is suddenly retired or its behavior changes significantly, having a gateway allows you to test and deploy a replacement without a full application deployment cycle. You can do A B testing at the gateway level—send ten percent of traffic to the new model and see how it performs before committing.
We should probably also mention the Model Context Protocol again in this context. Because M C P is an open standard, it further reinforces this move away from vendor lock-in. If more providers and tool-builders adopt M C P, the gateway becomes the universal connector that makes everything play nice together.
Yes, and that is why I am so excited about the aggregation aspect. Imagine a world where you can just "plug in" a new data source or a new capability to your gateway, and every A I agent in your organization suddenly knows how to use it. No custom integration code, no complex authentication flow for every single app. Just a unified, secure, and observable interface for intelligence. We are talking about a plug-and-play architecture for A I capabilities.
It really feels like we are building the plumbing for the future. It might not be as flashy as a new model with a trillion parameters, but this infrastructure is what is going to make A I actually useful and reliable in the real world. You can't have a skyscraper without a solid foundation and reliable pipes.
Well said, Corn. It is the boring stuff that makes the exciting stuff possible. And for nerds like us, the boring stuff is actually the most exciting part! I mean, who doesn't love a well-configured load balancer?
Guaranteed. Now, before we wrap up, let's give Daniel some concrete takeaways. If he is looking for a container for his Docker stack, Lite L L M or One A P I are his best bets for general L L M routing and redundancy. If he wants to dive into the latest M C P aggregation, he should look at the emerging tools like M C P Proxy or keep a close eye on the Lite L L M M C P integrations. And for observability and caching, Portkey or Helicone are excellent middleware options to consider.
That is a solid roadmap. And Daniel, as you are building this out, we would love to hear how it goes. Your prompts always push us to look at the practical, "how do we actually build this" side of things, and we really appreciate that. It keeps us grounded in the reality of development.
And to all our listeners, if you are finding these deep dives helpful, please take a moment to leave us a review on Spotify or Apple Podcasts. It really helps the show reach more people who are trying to navigate this crazy A I landscape. We are all learning this together in real-time.
It really does make a huge difference. And if you want to search our back catalog for more on these topics, like our episode on the Model Context Protocol or our deep dive into A I security, you can find everything at myweirdprompts dot com. We have a full archive there, plus an R S S feed for subscribers.
You can also reach us at show at myweirdprompts dot com if you have your own prompts or feedback. We love hearing from the community, even if it's just to tell us what kind of soup you're eating.
And a quick shout out to Suno for our show music. It is amazing what those models can do these days.
It really is. Alright, I think that covers it for today. Herman, go check on that soup before it gets cold! I can practically smell the carrots from here.
On my way, Corn. I don't want to hear about it from Mum later! She has a very long memory when it comes to neglected soup.
Thanks for listening to My Weird Prompts. We will see you in the next one.
Goodbye everyone! Stay curious!
