# A Free AI Tier Is an Architecture. Review It Like One. > Source: > Published: 2026-08-26 11:21:40+00:00 Everyone is building on free AI tiers right now. Few people review the architecture underneath. A free tier is a system, not a discount. It has three parts: a token budget, a server, and a gateway between them. Each part fails in its own way, so review all three before you build. MonkeyCode is an open source project, and it offers free model access plus a free server. Disclosure: This article was prepared as part of MonkeyCode's product outreach. Most free tiers hand you an API key and nothing else, but MonkeyCode also gives you a place to run the code that calls the API. That means you inherit a full system, not just a credential. Review it the way you would review any dependency. **Constraints first.** A free tier runs on shared capacity. The operator pays for every request you make, so the advertised 10M token budget is a cap, not a gift. That cap protects the pool from one noisy tenant. The free server is the same story. It is a shared resource with an eviction policy, so assume it can disappear. Write down your assumptions before you write code. Your traffic is bursty but bounded, your context fits the window, and your jobs tolerate waiting. If your workload breaks any assumption, the architecture punishes you later. Most providers also meter output tokens at a higher rate than input tokens. Your budget dies faster on chatty workloads. Plan for that asymmetry. **Trace the data flow.** Follow one request from your code to the model and back. Your client sends a prompt, the gateway meters it, and the queue absorbs the wait. The model answers, then the response flows back to your server. The free server changes where your code runs. Without it, you would call the API from your laptop or your own VM. With it, the whole loop lives next to the gateway. The gateway is the meter, counting input and output tokens. The server is the executor, holding your state and running your loops. The queue is the shock absorber, so your request waits when the model is busy. Here is the key insight: the meter and the server are separate failure domains. You can exhaust tokens while the server is healthy, or lose the server while the balance looks fine. Treat them as two systems, not one. **Know the failure domains.** Token exhaustion is the first failure domain. Your budget runs dry mid-job, leaving partial work and no rollback. The job stops where the meter stopped it. Queue saturation follows. Burst traffic backs up, latency climbs, and requests start timing out. The queue hides the problem until it is too late. Eviction is next. The free server gets recycled without warning, and your state vanishes with it. Your long-running job restarts from zero. Context loss is quieter. The model forgets what it saw earlier, so long jobs drift. Earlier decisions stop influencing later ones. Rate limits round out the list. Tokens are not the only meter, because requests per minute and concurrency have their own ceilings. You discover them at the worst moment. **What I would change next.** I would meter tokens on the client first. Do not discover exhaustion at the API, so count what you send before you send it. Stop at ninety percent of the budget, not at one hundred. Then I would checkpoint everything. Persist state after every step, so an eviction costs a retry instead of a restart. Write the position and the token count to disk. A circuit breaker comes next. If latency crosses a threshold, fail fast rather than piling work onto a saturated queue. A fast failure is easier to handle than a slow timeout. I would also bound my own concurrency. A shared tier cannot absorb fifty parallel calls, so limit your worker count. Backpressure is your friend. Finally, I would split long jobs into segments. Check the budget between segments, then decide whether to continue while you can still stop cleanly. Most free tiers give you little visibility, so build your own observability. Log the token count and the queue latency on every call. A dashboard is nice, but a single line per request is enough. Here is the pattern I recommend. It is pseudocode, but it runs with any provider SDK. ``` python import json BUDGET = 10_000_000 # advertised free-tier token cap GUARD = int(BUDGET * 0.9) # stop before the meter stops you CHECKPOINT = 'job_state.json' def load_state(): try: with open(CHECKPOINT) as fh: return json.load(fh) except FileNotFoundError: return {'position': 0, 'tokens_used': 0} def save_state(state): with open(CHECKPOINT, 'w') as fh: json.dump(state, fh) def run(chunks, call_model): state = load_state() while state['position'] < len(chunks): if state['tokens_used'] >= GUARD: print('budget guard hit: stopping cleanly') break chunk = chunks[state['position']] result, tokens = call_model(chunk) state['position'] += 1 state['tokens_used'] += tokens save_state(state) print('step', state['position'], 'done,', state['tokens_used'], 'tokens used') ``` The loop checks the budget before every call and persists progress after every call. If the server dies, you resume from the last checkpoint. If the budget dies, you stop with a clean state. The same logic applies to the queue. Here is a minimal circuit breaker in Python: ``` python import time class CircuitBreaker: def __init__(self, cooldown=30.0): self.cooldown = cooldown self.last_failure = 0.0 def allow(self): return time.time() - self.last_failure > self.cooldown def record_failure(self): self.last_failure = time.time() ``` Call `allow()` before each request. Call `record_failure()` when a call times out. The breaker gives the queue time to drain. **Who should not use this.** Do not build production on a free tier. Sustained throughput needs a reserved quota, and guaranteed latency needs a dedicated path. Long-lived memory needs a stable server, and none of those exist here. Do not use a free server for customer data. Do not use it for jobs with a hard deadline, and do not use it for agents that must remember a whole conversation. Use it for prototypes, experiments, and batch jobs that tolerate retries. The free tier is a test bed, not a foundation. That is fine, because many useful things start as tests. Just know which one you are building. If you want to run this review against a real system, MonkeyCode's free model access and free server are a convenient place to start. The project is open source, so you can read the code yourself. The review method above works on any provider, because the architecture is what you are really testing.