The Ultimate Guide to Free AI API Keys: 6 Platforms You Need to Know

Sign up at openrouter.ai, create one key from the dashboard, and you can call many model IDs behind the same header. Stick to the free filter in the Models tab. Paid routes return failures if you have no credits. The UI may show generous limits for free rows; upstream providers still enforce RPM/TPM fair use.

In code, treat the model ID as part of your configuration surface, not a magic string buried in a prompt file. When a teammate pastes a trending model from social media, your allow list should reject paid SKUs before they hit production. Log the model ID with the HTTP status on failures so “402 payment required” is obvious in your structured logs.

Google AI Studio exposes Gemini-family capabilities, including vision, text-to-text, and text-to-speech depending on what the product offers for your project. Click Get API key, create or select a project, name the key, copy it into your env, then watch burn in the Usage tab.

Split “playground” projects from “shared hackathon” projects early. Nothing is slower than five developers sharing one quota pool and blaming the model when the real issue is one PDF stress test eating the daily budget. Rotate keys after interns leave the same way you rotate Wi‑Fi when the neighbor guesses the password.

At build.nvidia.com, Explore groups models by job type, reasoning, vision, speech, biology, and more, so you can discover endpoints you would not have searched by name. Many cards expose View code with a snippet plus a path to generate a key; you can also pick a model (e.g. Qwen 2.5 Coder) and hit Generate key for a faster loop.

Free access is rate-limited but enough to compare latency and output style before you rent GPUs elsewhere. Expect different keys per model during experiments, label them in your secret manager so revocation under pressure does not take the wrong service offline.

Groq Cloud lives at console.groq.com. This is not xAI’s Grok. It is built for fast inference on supported models; create keys with optional expiration, revoke when you are done, and read Usage for cost and activity.

Model availability and quotas move; treat supported models as data that can change weekly. If you are streaming tokens to a UI, add client-side backoff so a bug does not hammer the API overnight. Free tiers forgive mistakes slowly.

From github.com/marketplace/models (Marketplace → Models) you can try open- and closed-weight models behind the same UX. Pick one (e.g. DeepSeek V3), click Use this model, create a personal access token in the dialog, scroll to generate it, and send it as the bearer credential. Free routes are rate-limited, so plan for throttling.

PATs are powerful: if you reuse the same token for Models and for unrelated admin scripts, you increase blast radius. Prefer fine-grained tokens when offered, store them in one backend service, and delete them when the experiment ends. Your future self audits fewer “mystery tokens” in GitHub token settings.

In the Cloudflare dashboard, open Workers AI → Models: filter by task capability or author, launch the LLM playground, or scroll the model page for API schemas with embedded test calls you can paste into a Worker. If another vendor’s quota bites first, Workers AI is a pragmatic fallback when you already ship on Cloudflare.

The payoff is operational: the same team that owns caching and routing owns inference deployment. You still need to read Workers AI quotas and model cards, edge proximity does not delete capacity planning, but you avoid shipping a second vendor just to try a small model.