Why I don’t use AI model aggregators

DEEP DIVE
Why I don’t use AI model aggregators

People love the idea of an “everything app.”

For example, one place to read your email, check your DMs, skim Slack, and pretend you’re “on top of things.”

In practice, almost none of these unified hubs worked outside of China.

Now, AI model aggregators are trying to do the same for LLMs.

These platforms (Poe, Mammoth, You.com,etc.) promise one subscription to use GPT-5, Claude, Gemini, Grok, Llama, or any model really.

Roughly $10/month to access models that cost $20 each individually.

It sounds like a smart deal.

But I don’t use them, and here is why.

Long-term memory & context actually matter

Here’s a confession: I hate prompt engineering.

The moment I’m writing a 600-word prompt to “unlock the model’s full potential,” I stop feeling productive.

I feel like I’m writing documentation for a robot who should already understand me.

The good news is that over the last six to eight months, model quality improved to a point where long prompts feel unnecessary.

And that happened because OpenAI, Anthropic, and others added two major features:

• Persistent long-term memory

• Thread-level context that compounds over time

This is now the single biggest reason I stick with one LLM platform instead of hopping around.

Aggregator platforms try to replicate this with “Projects” or cloud-saved context, but those systems don’t behave like built-in memory.

They run through the model’s API layer, which means:

• No native long-term memory

• No deep recall between threads

• No evolving personalization

• No cross-session grounding

The API can pass context you give it. It cannot access or reconstruct the internal memory systems ChatGPT or Claude use within their own apps.

And yes, this is a hard limitation today. Claude, ChatGPT, Gemini, etc. do not expose native memory through their APIs, so aggregators cannot “fake” it.

This alone is worth the extra $10–$15/month to me.

Feature parity gaps that compound over time

Aggregators move fast, but the model labs ship features that can’t be recreated.

Let’s take web search, for example.

LLM APIs cannot perform a full web search on their own.

They can retrieve URLs you send them but they cannot initiate a real-time search engine query.

This is why ChatGPT and Gemini handle search natively: they integrate with their own partner search infrastructure.

Model hubs can’t access that. So what do they do?

They bolt on their own custom search workflows. Usually a blend of:

• Bing API or a cheaper search API

• A scraper layer

• Their own reranking

• A simple retrieval step before the LLM call

It works. But it’s not the same as the proprietary search integrations inside ChatGPT or Gemini, which operate closer to the metal and use more complex ranking + retrieval pipelines.

In plain English:

Search quality inside aggregators is almost always weaker and less consistent than first-party search.

And search is only one example.

Aggregators also can’t match:

• Deep Search

• Canvas

• Agent Mode

• Meeting summaries

• File workspaces

• Built-in tools (vision, voice, interactive UI features)

None of these ship through the standard API.

And the gap compounds every month because each lab keeps adding app-only features.

You can ignore this today, but the tradeoffs get bigger over time.

Long-term platform risk

Let’s talk about business models.

OpenAI has ~800M weekly active users.

Only ~5% pay.

Even then, their consumer business is not profitable.

Now compare that to AI aggregators.

An aggregator charges $10/month and then pays for:

• Every API call

• Every heavy query

• Every hallucinated multi-step chain that costs 10× more than it should

• Every user who treats it like an unlimited buffet

API costs did drop over the last year, but usage climbed.

Most aggregators hide behind “unlimited messages” and then add hourly limits to premium models. That’s the same economics as a gym membership:

Just like gyms: they make money from people who don’t show up.

The moment users start prompting more, the model cost rises above the subscription price.

If that happens across a meaningful slice of their user base, the economics break.

And when your entire workflow (search, chats, projects, memory) sits inside a middleman platform, the risk becomes obvious:

If the aggregator folds, you lose everything.

So I stick to one LLM platform, and pay the extra $10/month, because it buys me reliability, context, and features that aggregators simply can’t deliver.