Implications of using API keys
This academy page covers whether using API keys and sharing your data can be decoupled.
Answer: yes.
Using an API key with an LLM (Large Language Model) doesn’t mean exposing your private data. The key’s simply a credential that proves you’re authorized to access the service. The real risk is if your key gets leaked, allowing others to use your account.
NodeOps handles API keys so that you don't have to when running your products and services on the Cloud Marketplace.
Are API Keys Safe? What You Really Share With LLM Providers
Once you have API keys, it's kinda on you to protect those keys, understand your provider’s data policy, and choose local models if your context demands maximum security.
When people first experiment with large language models (LLMs), one of the first hurdles they encounter is the mysterious API key. You paste it into your app or notebook, press “run,” and suddenly the AI is at your fingertips. But then comes the concern: Wait… by giving the model my API key, am I also giving away my data?
This is a common misconception. To set the record straight, let’s unpack what API keys are, how they work with LLMs, and why using one doesn’t mean you’re handing over all your private information.
What is an API key?
An API key is like a digital credential. It's a way for services to recognize that you’re authorized to use them. Think of it like a password used by machines.
For example, when your app wants to access an AI model, it includes your API key in the request. The provider checks the key, confirms your account, and then processes your request. Without the key, your request wouldn’t be allowed in.
What happens when you use an LLM API?
When you send a request to an LLM like OpenAI’s GPT or Anthropic’s Claude, two key things go out from your side:
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Your prompt (the input text you want the model to process).
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Your API key (so the system knows it’s you making the request).
The provider’s servers then process your prompt, generate a response, and send it back. That’s the whole exchange, the API key only proves to the AI provider that you’re an authorized user. It determines IF you get access and, if yes, exactly to what.
In the case of LLMs, the API dataload is the prompt you sent. is what the model reads to generate a response.
Data handling: what happens behind the scenes
Understandably, many people worry about what happens to the text they send into an LLM. Do providers store everything you type forever? The answer depends on the provider’s policies.
Some providers may keep temporary logs for debugging or abuse detection. Others allow you to opt out of data retention entirely.
Reputable companies (such as OpenAI, Anthropic, or Cohere) publish clear policies on how your prompts are handled—for example, see OpenAPI, and Anthropic.
That means you’re not “giving away your data” by default. Instead, your data is processed according to transparent rules, and in many cases, you can control how much is stored.
The real risk: leaking your API key
If there’s a genuine danger, it’s not that the provider steals your data when you use their API. The real risk is much simpler: someone else getting hold of your API key.
Imagine leaving your Netflix password on a public forum. Strangers could start watching shows under your account—racking up usage while you foot the bill. API keys work the same way.
If you accidentally publish your API key online, say by posting it in a ChatGPT conversation where the data is collected, someone else could use it to send requests on your account. That could burn through your usage allowance, cost you money, or even trigger rate limits that block your access.
Best practices for safe API key usage
So how do you protect yourself? A few simple habits go a long way:
- ❌ Don't: Paste secrets into code, chats, or sites that others access
- ✅ Do: Use environment variables or secure vaults to store secrets
If you want to keep your data your own, then it's on you to know your provider’s policy.
Always think about your use case. For casual exploration, cloud APIs are convenient and safe. But for highly sensitive or regulated data, you may prefer running a local model where nothing leaves your servers.