Your Free AI Tier Is Shared. Build the Gate.

This week, DEV is arguing about who reviews AI output (discussion). The community keeps asking the same question. My answer is different. Review the boundary first, not the output. The output is visible. The boundary is not. That is where the risk hides.

Agents get the memory debates. The gateway gets none.

A free AI tier is a shared service. It has a budget, a concurrency ceiling, and no SLA. Treat it that way. Put a gateway between your app and the model. The gateway owns the budget, the queue, and the breaker.

MonkeyCode is an open source project. It offers free model access and a free server option. The free tier gives you a 10M token monthly budget. That number is a constraint, not a feature. Design around it before you build on it. Disclosure: This article was prepared as part of MonkeyCode's product outreach.

Think of the free tier as a water pipe. The pipe has a fixed diameter and a monthly meter. Your app is a set of open taps. Without a valve, the meter empties fast and the pipe floods. The gateway is the valve.

Direct calls look simpler. They are simpler for one request. They fail at the tenth. The gateway absorbs the variance. Your app never sees a 429. Your app never sees an empty budget.

Constraints

Three constraints define the design. First, the 10M token budget is monthly. It does not reset daily. It does not roll over. Second, the free server serializes work. Concurrency of one is a safe assumption. Third, there is no SLA. The endpoint can stall, throttle, or return 429 at any moment.

These constraints are not bugs. They are the contract. A good architecture reads the contract. Then it shapes the data flow around it.

Data flow

The flow has six stages. The client sends a prompt to the gateway. The gateway checks the token budget. It enqueues the request. A single worker drains the queue. The worker calls the model endpoint. The response returns to the client.

Add two escape paths. When the budget is empty, the gateway returns a fallback answer. When the breaker is open, it skips the endpoint entirely. Both paths keep the client alive.

The queue is the shock absorber. It decouples your request rate from the model's tolerance. That decoupling is the whole point.

Failure domains

Four failures will hurt you. Token exhaustion is silent. The request passes the HTTP layer, then dies at the budget check. Queue backlog is slow. Two hundred requests with one worker means two hundred waits. Endpoint stall is sticky. A hung request holds the worker forever. Partial output is sneaky. A token cap cuts a response mid-sentence.

Each failure needs a different response. Exhaustion needs a fallback. Backlog needs a timeout. Stalls need a watchdog. Partial output needs validation. The queue hides all four from your users. That is good and bad.

The gateway

Here is a minimal gateway in Node.js. It runs with no dependencies. It implements the budget, the queue, and the breaker.

// gateway.js — minimal free-tier AI gateway
// AI_ENDPOINT=... AI_KEY=... node gateway.js

const ENDPOINT = process.env.AI_ENDPOINT;
const KEY = process.env.AI_KEY;
const MONTHLY_BUDGET = 10_000_000; // tokens per month
const MAX_CONCURRENCY = 1;         // free tiers serialize

const state = {
  usedTokens: 0,
  failures: 0,
  queue: [],
  busy: false,
  open: false,
};

function canSpend(tokens) {
  return state.usedTokens + tokens <= MONTHLY_BUDGET;
}

async function callModel(prompt) {
  const res = await fetch(ENDPOINT, {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      Authorization: `Bearer ${KEY}`,
    },
    body: JSON.stringify({
      messages: [{ role: "user", content: prompt }],
      max_tokens: 200,
    }),
  });
  if (!res.ok) throw new Error(`HTTP ${res.status}`);
  const data = await res.json();
  const tokens = data.usage?.total_tokens ?? Math.ceil(prompt.length / 4);
  state.usedTokens += tokens;
  return data;
}

async function worker(task) {
  if (state.open) return task.fallback();
  try {
    const out = await callModel(task.prompt);
    state.failures = 0;
    task.resolve(out);
  } catch (err) {
    state.failures += 1;
    if (state.failures >= 3) state.open = true;
    task.reject(err);
  }
}

function enqueue(prompt, fallback) {
  return new Promise((resolve, reject) => {
    state.queue.push({ prompt, fallback, resolve, reject });
    pump();
  });
}

async function pump() {
  if (state.busy) return;
  state.busy = true;
  while (state.queue.length) {
    const task = state.queue.shift();
    await worker(task);
  }
  state.busy = false;
}

// Probe: measure latency and failures before trusting the tier.
async function probe() {
  const samples = [];
  for (let i = 0; i < 10; i++) {
    const t0 = Date.now();
    try {
      await callModel("Reply with one word: ok");
      samples.push(Date.now() - t0);
    } catch {
      samples.push(null);
    }
    await new Promise((r) => setTimeout(r, 1000));
  }
  console.log(samples);
}

if (process.argv.includes("--probe")) probe();

Run the probe before you wire the gateway. It gives you a latency baseline. It also shows the failure pattern. Ten samples are enough to see the shape. A null sample means a failure. A long sample means a stall. Both are design inputs.

AI_ENDPOINT=https://api.example.com/v1/chat AI_KEY=... node gateway.js --probe

What I would change next

The fixed concurrency of one is too blunt. I would add adaptive concurrency. Start at one, then raise it until error rates climb. I would add a cache for repeated prompts. Many prompts are identical. A cache saves tokens and latency. I would add a watchdog timer. A request running past sixty seconds should be aborted. I would add daily telemetry. A dashboard showing token burn changes team behavior.

Limitations

This gateway is a starting point. It has no persistence, so a restart loses the budget counter. It has no authentication, so any client can enqueue. It has no multi-tenant isolation. It is a scaffold, not a product. This design assumes one tenant and one endpoint. A real deployment needs both.

Do not use this approach for real-time features. Do not use it for regulated workloads. Do not use it when a failed request is unacceptable. A free tier is a development resource. It is not a production promise.

The takeaway

The community is asking who reviews AI output. My answer is simple. Review the boundary first. The model will change. The budget and the queue will not. Build the gate, measure the pipe, then let the model work. A free tier without a gate is a bill you cannot see.

Try MonkeyCode's free tier with this gateway. Probe it before you trust it. The 10M tokens are real. The architecture around them is up to you.