Free AI API options in 2026: limits, costs, and alternatives

What is a free AI API?

An AI API lets your application request model output, such as a generated answer, an embedding, or an image. The provider runs the model; your code decides what to send and how to use the result. A free tier lowers the cost of those first experiments.

It does not remove the rest of the application work. For a support assistant, someone must decide which documents it can read, how to handle missing information, who can authorize actions, and where a conversation goes when it needs a human. That work still exists even when the inference bill is zero.

I would separate three meanings of "free" before choosing:

The following details were checked against provider documentation on September 8, 2026. They are not a hands-on performance ranking.

Four free AI API access options

Google Gemini API

What it offers: Google's API pricing page lists free input and output for selected models, including Gemini 3.8 Flash at the time checked. AI Studio is the web interface for experimenting and managing access; the API is what your application calls.

Why consider it: It is a direct route to Google's models, with a published free-versus-paid comparison. Start with a model whose capabilities fit your workload rather than assuming every image, audio, or advanced feature shares the same allowance.

Limits and costs: Google states that free-tier content is used to improve its products, subject to the linked terms. Review those terms before sending private material. Its rate-limit documentation describes project-level limits across requests and tokens, not a universal "60 requests per minute" entitlement. Check your active limits in AI Studio.

My take: Use synthetic prompts to establish whether the model can do your task, then evaluate the paid configuration and data handling you would actually deploy.

Hugging Face Inference Providers

Hugging Face's billing documentation illustrates usage monitoring. The screenshot is a vendor example, not a free-account allowance or a benchmark from this article.

What it offers: Hugging Face routes requests to supported inference providers. Its current pricing lists $0.10 in monthly credits for free users, explicitly subject to change. This is not unlimited free inference for every model on the Hub.

Why consider it: Routed access is useful for trying supported models without separately integrating each provider's billing and API. Still check the actual model and task you want to run.

Limits and costs: Monthly credits apply to requests routed through Hugging Face. With a custom provider key, the provider bills you directly and those credits do not apply. Additional routed usage requires purchased credits.

My take: Treat the allowance as a small experiment budget. Separately check the model's license, supported provider, and deployment requirements before deciding it fits your application.

OpenRouter

What it offers: OpenRouter provides access to models through a common API. Its limits documentation distinguishes free variants, whose IDs end in :free, from paid models. A free variant is not a promise of free access to every model in the catalog.

Why consider it: A common interface can reduce the code needed to compare models. It does not make their outputs, tool support, or context limits identical.

Limits and costs: Free variants have a 20-request-per-minute limit. The documented daily allowance is 50 requests when lifetime credit purchases are below $10, or 1,000 after at least $10 in purchases. Provider capacity can also limit requests. Check fallback settings so a free-model test cannot silently become a paid-model experiment.

My take: Record the exact model and provider used for each result. Otherwise a comparison can mix different configurations and tell you little about what your app will do tomorrow.

Puter.js

What it offers: Puter's user-pays model assigns cloud and AI usage to each user's Puter account. Users start with a monthly allowance and can upgrade when it runs out. As the developer, your own use of the app also consumes your account's resources.

Why consider it: Puter.js can support frontend applications without handing your users a developer-owned model API key. The AI tutorial shows JavaScript access to model capabilities.

Limits and costs: The important distinction is who pays, not whether compute has a cost. Users must be comfortable with the account and allowance model. Do not describe it as an unlimited free API budget for a company-operated service.

My take: Try the actual sign-in and allowance-exhaustion experience before building a product around it. Billing convenience for the developer should not become a surprise for the user.

What to test before relying on a free AI API

Start with a small, fixed set of examples that resembles the job. For support, that should include an answerable question, a question missing an essential detail, a request outside policy, and a request for a human. Decide the expected behavior before looking at the output.

Keep API keys out of frontend bundles and public repositories unless the provider explicitly supplies a client-safe mechanism. Do not solve a quota problem by repeatedly creating keys or accounts. For a real application, establish authentication, logging, usage controls, and a failure path alongside the model call.

Switching providers also needs testing. A familiar request format can hide differences in output shape, structured-output support, tool calls, or error behavior. Preserve a few representative tests so a provider change does not quietly change the application.

Evaluate a ready-made teammate through eesel CLI

If the intended result is a support teammate, model selection is only one part of the job. eesel's helpdesk teammate supplies a role-specific alternative that you connect to your approved knowledge and support workflow. The CLI lets you operate that teammate from a terminal, a script, or a coding agent such as Claude Code, Cursor, or Codex.

The CLI uses the same agent and workspace as the dashboard. Your coding agent can inspect the setup and run a bounded evaluation; it is not creating a separate chatbot inside the coding session. You also do not pass a Gemini or OpenRouter free API key to eesel to make its work free. These are separate services and billing accounts.

Inspect the workspace before testing

For the example below, sign in to an existing eesel workspace and choose or create a separate test agent in the dashboard. Replace TEST_AGENT_ID with that agent's ID. Use only knowledge approved for this test, and keep write actions off or set to require approval.

With Node.js 18.17 or newer, run:

npx @eesel/cli login
npx @eesel/cli whoami
npx @eesel/cli agents
npx @eesel/cli --agent TEST_AGENT_ID status
npx @eesel/cli --agent TEST_AGENT_ID integrations
npx @eesel/cli --agent TEST_AGENT_ID instructions
npx @eesel/cli --agent TEST_AGENT_ID automations

Check that you selected the correct workspace and agent. Review what sources are connected, whether content is ready, and whether existing automations are already enabled. A test agent is not an isolated sandbox if it has permission to act on a live helpdesk.

The CLI reference documents these commands and their JSON output. Single results are JSON; lists emit one object per line. A coding agent can use that output to report missing setup rather than guessing from your prompt.

Compare a concrete support task

A useful first comparison is whether the teammate follows a small support policy. Create a local plain-text file named trial-cancellation-policy.txt containing this fictional policy:

Customers can cancel a trial from Settings > Billing. Cancellation stops renewal but does not delete their account. Account deletion needs a separate request. Do not claim to have cancelled or deleted anything when only explaining the steps.

Upload that file to the selected agent, then start a fresh conversation:

npx @eesel/cli --agent TEST_AGENT_ID files upload ./trial-cancellation-policy.txt
npx @eesel/cli --agent TEST_AGENT_ID new --name "cancellation-evaluation"
npx @eesel/cli --agent TEST_AGENT_ID chat "Using the uploaded trial cancellation policy, draft an answer here to this fictional customer: If I cancel my trial, will you delete my account too? Explain the distinction and cite the source. Do not change an account or send a message."

This is an illustrative test, not a customer-account run performed for this article. Check that the upload succeeded and that the answer uses the intended file. Uploading knowledge does not set standing instructions or grant action permissions. The new command creates a conversation, not another agent.

The expected answer distinguishes cancellation from deletion without claiming either action happened. Next, ask where cancellation happens and what to do about deletion. For a fair comparison with your API prototype, provide the same policy and grade against the same expected behavior.

Before any chat test, enforce permissions in settings. The "do not change" sentence is not a security control. Chat can invoke tools and run billable work, so avoid bulk tests until you understand the setup and cost.

Let a coding agent help with review

You can give your coding agent a scoped request:

Use eesel CLI to inspect test agent TEST_AGENT_ID. Check the uploaded cancellation policy and run the fictional questions above. Summarize incorrect answers, missing source references, and any held approvals. Do not change instructions, automations, or permissions.

Then inspect the records directly:

npx @eesel/cli --agent TEST_AGENT_ID activity
npx @eesel/cli --agent TEST_AGENT_ID approvals
npx @eesel/cli billing

A held approval is not a completed action. Billing reports current state rather than estimating how much engineering time or future support work you will save. The CLI's --dry-run previews a server write call; it does not simulate answer quality.

A correct terminal response is only the first test. Before customer use, verify the real helpdesk trigger, destination, private-versus-public reply behavior, and escalation path. That remains necessary whether you build with a model API or use an eesel teammate.

Budget for the job, not just the model call

eesel is not a free model API. Its current pricing lists regular support tickets and chat-session tasks at $0.40 each, with no platform fee or monthly minimum on default pay-as-you-go. Tasks are billed regardless of outcome; messages within the same task are not separately charged. Enterprise adds a platform fee.

Signup includes $50 of usage plus two blog generations, subject to the trial's limits. A spend cap can pause new work. Neither a trial nor a cap makes production support unlimited and free.

For your own API application, include retrieval, storage, hosting, monitoring, and human maintenance when estimating cost. For a ready-made teammate, include task usage and the time spent configuring and reviewing it. Neither route is automatically cheaper for every project.

This eesel dashboard example shows a helpdesk teammate. The CLI gives technical colleagues access to the same workspace rather than replacing the dashboard.

Use a free AI API when you need to build and control the application yourself. If you mainly need support work handled, try eesel and use the CLI to evaluate one policy with a few difficult questions. Keep the decision grounded in the answers and workflow you can verify.