Cloudflare Workers AI Free API Call Process: Registration, Configuration, and Deployment in One Step

I was working on a personal project the other day, and I wanted to build a...AIThe assistant needs to call large models. When I asked about the price, wow, each call to GPT-4o costs a few cents. If our project were to actually run, the API fees alone would amount to several thousand a month.

We wondered if there was any cheaper way.

Then we came across Cloudflare Workers AI. We knew Cloudflare was involved in AI inference before, but we didn't take it seriously, assuming big companies would charge for their products. Turns out, wow, they're giving away 10000 Neurons for free every day.

What does 10000 Neurons mean? It's roughly a few hundred dialogues or a few hundred image generation sessions per day. That's more than enough for one person to play with.

I got really excited and immediately started researching it.

What is Cloudflare Workers AI?

Cloudflare Workers AI, well, I think its...PositioningThat's quite interesting. It's not just a simple model provider; it's an AI inference platform running on Cloudflare's global edge network. With edge nodes in over 300 cities, it's incredibly close to users.

Think about it: when you use OpenAI's API, the request first goes to the US, is processed, and then comes back, taking three to four hundred milliseconds. Cloudflare, on the other hand, is a CDN provider with nodes all over the world. Wherever you are, it runs the model on the nearest node.

The typical response time is less than 100 milliseconds. This difference is noticeable to the user.

What's really cool about it is that its cold start is millisecond-level, and it automatically scales up and down. Even with a sudden surge in traffic, it won't lag, unlike some platforms that are incredibly fast when no one's using it at 3 AM, but then completely crash during peak hours in the daytime.

50+ models, covering all scenarios

There are also many supported models; as of June 2026, there are already more than 50.

For text-based dialogue, Llama 3, Llama 4, Mistral, GLM, Qwen, Gemma, and Deepseek-r1 are all available. For image generation, there are Stable Diffusion, FLUX, and Pixverse. For speech, there's Whisper for speech-to-text, TTS for text-to-speech, and even video generation.

Moreover, its API is designed to be very clean. Regardless of the model used, it uses a unified interface; you only need to change the model name.

env.AI.run(“model name”, {…})

Changing the model with just one line of code is so convenient.

Extremely cheap price, generous free credit limit

I also did the pricing. The free daily allowance is 10000 Neurons, which is more than enough for personal experimentation. If you want to pay, it's $0.011 per 1000 Neurons, which is 60% to 90% cheaper than OpenAI. Plus, it's billed by Neurons, not by tokens, making it more cost-effective for smaller conversations.

Honestly, I think this billing method is quite fair. The number of Neurons you spend differs depending on whether you're having a simple conversation or a very long one, but the difference isn't as outrageous as with token-based billing.

At this point, you might be wondering, how exactly do you use it?

I went through the process myself, let me tell you about it.

Register and create API tokens

The first step is to register a Cloudflare account. If you already have an account, just log in. If not, registering is easy; simply fill in an email address and set a password.

After logging in, click AI in the left menu, then click Workers AI.

Once inside, you'll see a button to create an API token. Click it to generate a token.

Here's a detail to note: the generated token can only be seen once during creation, so be sure to save it. The page will also give you an account ID; you'll need both of these later.

After saving, you'll see a usage example at the bottom of the page, which is the curl command. Just replace it with your account ID and APIKEY. The command looks like this:

curl \
  https://api.cloudflare.com/client/v4/accounts/账户ID/ai/run/@cf/模型ID \
  -H "Authorization: Bearer 刚才生成的APIKEY" \
  -d '{"messages":[{"role":"system","content":"You are a friendly assistant that helps write stories"},{"role":"user","content":"Write a short story about a llama that goes on a journey to find an orange cloud"}]}'

View the list of available models

Then the question arises: where do you find the model ID?

There is a document button on the right side of the Workers AI page.

Click on the "Models" category; that's where you'll find a list of all the models.

Choose the one you want to use, and you can see the model ID by clicking on it.

Test calling the Kimi 2.6 model

I'll use Kimi 2.6 for testing. Copy the model ID and replace it in the curl command, starting with @cf.

The request was sent out, and the result was returned quickly.

The response speed is indeed very fast, smoother than I expected.

To be honest, I just wanted to see what this free credit could do, and it turns out it's pretty powerful. It's perfectly adequate for everyday chatting, content generation, translation, and coding.

And think about it: this thing runs on edge nodes. You deploy an AI application with users all over the country or even the world, and the response speed remains consistent, without having to worry about regional deployment issues. This is incredibly attractive to individual developers and small teams.

Previously, if you wanted to run a model, you either had to buy a GPU and build your own, which was incredibly expensive, or use a GPU instance from a cloud provider, which was complicated to configure. Cloudflare, on the other hand, has it all wrapped up for you. You just write code to call it, and it handles all the messy stuff for you.

I think this line of thinking is correct. AI capabilities will increasingly become like infrastructure such as water and electricity; you don't need to know how the electricity is generated, you can just plug it in and use it. Cloudflare Workers AI takes this "plug and play" concept to an even more extreme level.

Of course, it's not without its drawbacks. The free quota is only 10000 Neurons, so you'll still need to pay if you want to deploy it in a production environment with high traffic. Also, while there are over 50 models, compared to OpenAI's comprehensive ecosystem, some niche or newer models are not supported.

But for personal projects, small tools, or just curiosity-driven experiments, I think it's more than enough.

That's my honest experience. Since you've read this far, if you found it helpful, please like and share it. If you want to receive updates first, you can also follow me! ⭐

Thank you for reading my article. See you next time.