Free LLM chat and translation APIs on Cloudflare Workers AI need no credit card

A free tier you can build against

A tutorial published on dev.to walks developers through standing up two working AI services on Cloudflare Workers AI using only the platform's free allocation: an LLM chat endpoint and a translation API covering about 100 languages. According to the post, no credit card or paid API key is required, and the author estimates the whole setup takes around five minutes.

Cloudflare meters Workers AI usage in "neurons," and the free plan includes a daily allocation that, per the author, is enough for prototypes, demos and low-traffic tools. The post cautions that the limits are set by Cloudflare and can change, so anyone following along should check the current pricing page.

What the two templates do

The chat template exposes a single POST endpoint backed by Meta's Llama 3.1 8B Instruct model. A request carries a message, optional conversation history and an optional system prompt, and the worker responds with the model's reply. History is trimmed to the most recent twenty messages before the request is assembled, and a default system prompt is applied when none is supplied. The worker sets permissive CORS headers and supports an optional bearer-token check driven by an API_KEY secret.

The translation template wraps Meta's m2m100 1.2B model and accepts either a GET request with query parameters or a POST with a JSON body, returning the translated text along with the target language.

Both workers are minimal — a wrangler.toml plus a worker.js — and hinge on one configuration detail: the [ai] binding in wrangler.toml, which exposes Cloudflare's inference API inside the worker as an env.AI object.

Deployment in three commands

According to the post, deployment amounts to placing the files in a folder, running npx wrangler login, then npx wrangler deploy. Each worker lands on a workers.dev subdomain and can be called immediately with curl — one example posts a question to the chat worker, another translates a phrase into French through the translation worker. The author distributes the pair as an MIT-licensed download named cf-ai-starter-lite, with the full code also printed in the post.

Practical lessons from the build

The tutorial closes with field notes that apply well beyond this specific setup.

Small open models need narrow instructions. The author found that an 8B-parameter model drifts when given vague guidance, and that short, explicit constraints — a sentence cap, or a requirement to reply only with JSON — noticeably improve results.

Authentication should be added early. An unprotected workers.dev URL can exhaust the free allocation quickly if discovered, which is why the chat worker supports an optional bearer token set through a wrangler secret.

CORS has to include the OPTIONS preflight. Without it, browser requests fail even when the same call succeeds from curl.

Testing should hit the real API. The download ships a test script that calls Workers AI over REST using an account ID and token; the author argues that mocks would have missed quirks in actual model output.

An optional paid pack

The free download works on its own, but the author also sells a $7 expansion that adds an OpenAI-compatible chat-completions proxy with streaming, a batch summarizer, a JSON extractor with schema retry, semantic search built on BGE embeddings, a prompt library and a setup guide. The post states that the author is not affiliated with Cloudflare and that the article was written with AI assistance.

Why it matters

The usual first step in adding an LLM to a side project is obtaining a paid API key, and that is precisely the friction this setup removes. A free daily neuron allocation, combined with two small, permissively licensed workers, gives developers a working chat and translation backend in about the time it takes to sign up elsewhere. The pattern is worth copying even outside Cloudflare: a two-file endpoint with CORS handling, optional auth and trimmed conversation history is a sensible reference architecture for any prototype.

The caveats are real — quotas can change, an 8B model is not a frontier model, and a free allocation will not absorb production traffic. But for demos, hacks and low-traffic tools, the price is difficult to beat, and the edge-runtime approach keeps inference colocated with the rest of your code.