Notes about possibilities of hard cost limits with Cloud providers taking example of Google Cloud - ChatGPT
Let me address this precisely and concretely.
1. Your concern is valid in principle
If you host a public API on Google Cloud and:
it is reachable on the internet, and
you do not enforce hard request limits at the service edge, and
usage exceeds the free tier,
then Google will continue serving traffic and will bill you.
Alerts, by themselves, are not protective controls. They are informational only.
So your statement:
“If I miss the alert or am too busy to look at it, I will get charged”
is factually correct.
2. However: Google Cloud does provide hard-stop mechanisms — just not at the billing layer
This is the key nuance.
GCP does not auto-suspend workloads when free limits are exceeded.
But it does allow you to implement hard enforcement at the platform edge, without writing your own backend logic.
You do not need to implement rate limiting inside your API code.
3. Concrete ways to enforce “make itself unavailable” behavior
Option A: Cloud Run + concurrency / max instances (most relevant for you)
If you deploy your API on Cloud Run:
You can set:
max instances (e.g., 1 or 2)
concurrency per instance
Once capacity is exhausted:
additional requests are rejected automatically
no more scaling occurs
no runaway billing happens
This is a hard cap, not an alert.
This is often sufficient for small APIs.
Option B: API Gateway / Endpoints with quotas
Using Google Cloud API Gateway or Cloud Endpoints:
You can define:
request quotas
rate limits
When the quota is exceeded:
requests are rejected (
429)the backend is not called
no additional compute cost is incurred
This enforcement happens before your API executes.
Again: hard stop, not alert.
Option C: VPC / firewall-based shutdown (cruder, but absolute)
You can:
restrict ingress to known IPs or clients,
or temporarily disable the service via infrastructure config.
This is less elegant but absolutely prevents further usage.
4. Billing budgets can disable some services (limited, but worth noting)
GCP budgets can be configured with:
threshold notifications (e.g., 50%, 90%, 100%)
and in some cases, budget actions (via Pub/Sub + automation)
Out of the box:
budgets do not auto-shut down services
But with a small automation hook:
you can programmatically disable a Cloud Run service or API
when a budget threshold is crossed
This is more complex, but it exists.
5. The deeper point: hyperscalers assume “professional intent”
Your discomfort comes from a philosophical mismatch, not a technical flaw.
Google Cloud is designed under the assumption that:
you want the service to stay up,
you accept that traffic = cost,
and availability is more important than absolute cost ceilings.
Your mental model is closer to:
“I want this available, but never at the cost of surprise billing.”
That is a reasonable model — it is just not the default assumption of hyperscaler clouds.
6. Practical recommendation given your risk tolerance
Given what you’ve said, the safest approach on GCP is:
Use Cloud Run
Set max instances = 1
Optionally put API Gateway in front with quotas
Treat free tier as best effort, not guaranteed
This gives you:
deterministic upper bounds,
automatic refusal of excess traffic,
no need to babysit alerts constantly.
If even that feels uncomfortable, then your instinct is correct:
GCP may be psychologically stressful, even if technically controllable.
7. Bottom line (very clear)
Your fear is not hypothetical
Alerts alone are not sufficient protection
GCP does not auto-stop on free-tier overage
But GCP does allow you to enforce hard caps at the service edge
If you configure those caps, you are no longer relying on alerts — you are relying on enforced limits.
That distinction is the key.
BTW is there a billing limit that we can specify to GCP? Something like Rs.5000 per month, after which services stop automatically. This gives a guarantee that charges will not cross 5000 per month
CG: Short answer first, very clearly:
No. Google Cloud does NOT provide a native “hard billing cap” such that all services automatically stop once ₹5,000/month is reached.
There is no built-in guarantee of “charges will not cross this amount.”
Your concern is valid, and this limitation is real.
What GCP does not have (this is the key point)
Google Cloud does not offer a feature like:
“Set a maximum monthly spend of ₹5,000 and automatically stop all services beyond that.”
This is by design, not an omission.
Even today:
Budgets ≠ hard limits
Alerts ≠ enforcement
Billing continues unless you actively intervene
So if you are looking for a guaranteed upper bound enforced by Google itself, GCP does not provide that.
What GCP does provide (and why it is not enough by itself)
1. Budgets (informational, not protective)
You can:
Set a budget (e.g., ₹5,000)
Receive alerts at thresholds (50%, 90%, 100%)
But:
Nothing stops automatically
Services keep running
Billing continues past 100%
This is exactly the “tripping point” you identified earlier.
2. Budget-triggered automation (possible, but not native)
There is a workaround:
Budget → Pub/Sub notification → Cloud Function / script
Script disables:
Cloud Run service
API Gateway
Project billing
or shuts down specific resources
This can approximate a hard stop.
However:
It is not atomic
There can be delay
Some overrun is still possible
It requires setup and maintenance
So it is not a guarantee, only a mitigation.
The only true hard-stop on GCP
The only absolute way to stop billing is:
Disable billing for the project
Once billing is disabled:
Paid services stop
APIs fail
Resources are suspended or deleted
But:
Billing disablement is manual, unless you automate it
Automation still has lag and risk
Again: no native “₹5,000 and stop” switch.
Why Google does not offer this (important context)
Hyperscalers (GCP, AWS, Azure) assume:
Availability > cost certainty
Traffic should not be dropped due to billing
Customers are responsible for cost governance
A hard billing cap would:
cause unexpected outages,
break SLAs,
and create complex liability questions.
So they intentionally avoid it.
This is philosophical, not technical.
Practical truth table (very important)
| Requirement | GCP |
|---|---|
| Pay only when used | Yes |
| Free tier available | Yes |
| Alerts on spend | Yes |
| Automatic stop at ₹X | ❌ No |
| Guaranteed max spend | ❌ No |
| Hard enforcement possible | ⚠️ Only via architecture |
What this means for your mindset
Given everything you’ve said so far, your instinct is accurate:
“I want a guarantee that charges will not cross a fixed amount.”
GCP cannot give you that guarantee by policy.
You can:
engineer guardrails,
reduce risk,
cap scale,
but you cannot eliminate billing risk entirely.
If you want absolute cost certainty
Then only these models truly satisfy it:
Fixed-price hosting (₹X/month, capped)
Prepaid credits with hard exhaustion
Self-hosted / local / VPS with fixed billing
Cloud services that stop on credit exhaustion
Hyperscalers are not designed for this mental model.
Bottom line (very crisp)
❌ No native billing cap in GCP
❌ No automatic service stop at ₹5,000
⚠️ Automation can reduce risk but not eliminate it
✅ Your concern is technically and financially justified
Your caution here is not overthinking — it is correct risk assessment.
Me: Hmm. so are there cases where any hyperscalar sevice like AWS, Azure or GCP charge some customer a huge amount even though the customer set his budget to a fraction of that amount. If so, does the customer have to cough up the money, or perhaps even go bankrupt.
CG: Yes — such cases do exist, across AWS, Azure, and GCP. And the outcomes are nuanced. Let me explain this carefully and soberly, without fear-mongering.
1. First, an important clarification
A budget on AWS / Azure / GCP is not a spending cap.
It is an alerting mechanism.
So when you ask:
“even though the customer set his budget to a fraction of that amount”
that budget never had contractual force. It did not limit billing; it only notified.
This distinction is absolutely critical.
2. Do runaway billing incidents actually happen?
Yes. There are many documented cases where:
a misconfigured service,
an exposed API,
an infinite loop,
a forgotten resource,
or an unexpected traffic spike
generated bills that were orders of magnitude higher than intended.
Typical patterns:
Someone expected ~$10–$50/month
They received bills in the thousands or tens of thousands
In rare cases, much higher
This is not unique to one provider. It happens on:
AWS
Azure
GCP
It is a consequence of metered, elastic infrastructure.
3. Does the customer legally owe the money?
Strictly speaking: Yes
From a contractual standpoint:
The customer agreed to pay for usage
The services were consumed
The bill is valid under the terms
Cloud providers do not guarantee cost safety.
So in principle:
the customer does owe the money
4. But what happens in practice?
This is where reality is more humane than the contract.
For individuals / small developers / first-time incidents:
Providers often:
forgive charges partially or fully
issue credits
reduce bills significantly
Especially if:
it’s a first incident
there was no malicious intent
the customer acts quickly
the usage pattern is clearly accidental
Many people have had large bills reduced to near zero after explaining the situation.
For repeat incidents or businesses:
Much less leniency
Expect partial credits at best
Sometimes no relief
5. Do people actually go bankrupt over this?
Almost never in practice, for normal developers or hobbyists.
Reasons:
Cloud providers do not want PR disasters
They value long-term trust
Aggressively bankrupting small users is bad business
However — and this matters —
There is no formal guarantee of forgiveness.
You are relying on:
provider discretion,
goodwill,
and human judgment.
That is uncomfortable if you are risk-averse — and rightly so.
6. Why these stories still matter (even if forgiveness exists)
Three reasons:
Stress and anxiety
Even if later forgiven, the shock is real.
Time and effort
Support tickets
Explanations
Waiting for resolution
No absolute safety net
You cannot rely on mercy as a control mechanism.
Your instinct to want a hard ceiling is therefore rational.
7. How hyperscalers expect you to protect yourself
They expect defensive architecture, not budgets, to limit cost:
Hard rate limits (API Gateway)
Max instances (Cloud Run / Lambda / Functions)
Authentication (no open public endpoints)
Quotas
Manual enablement of services
Separate projects for experiments
Aggressive cleanup
Budgets are treated as last-resort visibility, not protection.
8. Your position, evaluated honestly
Given everything you’ve said:
You want predictable worst-case cost
You do not want to rely on:
alerts,
constant monitoring,
or provider goodwill
That puts you at odds with hyperscaler philosophy, not because you are wrong, but because your risk tolerance is lower than what these platforms assume.
This is a perfectly legitimate stance.
9. Bottom line (very clear)
✅ Yes, runaway bills have happened
✅ Yes, budgets did not prevent them
⚖️ Legally, customers owe the money
🤝 Practically, providers often forgive first-time accidents
❌ There is no guarantee of forgiveness
❌ No hyperscaler offers a true hard spend cap
Your caution is not paranoia.
It is an accurate understanding of how elastic cloud billing really works.
If you want, next we can discuss architectures or alternatives that do give you absolute cost ceilings.
Me: If I were an IT head of a company with services hosted on these hyperscalar cloud platforms, I would feel very uneasy due to this lack of billing cap. It could be a big figure, say 50,000 US Dollars a month. But even if that figure is not there, through mistakes on company IT dept. side or through malicious operators, some runaway usage may occur leading to runaway billing. That's so scary.
I mean, I can't think of anybody guaranteeing that some mallicious operator may not be able to crack some password/authentication mechanism and thereby do a flooding of the server.
CG: Your unease is understandable, but there is an important distinction to make between individual / small-team usage and how enterprises actually operate on hyperscalers. What feels “scary” at an individual level is systematically managed and bounded at an enterprise level—not by a single switch, but by layered controls.
Let me address your concern directly and realistically.
1. Your fear is valid in theory, but incomplete in practice
You are absolutely right about this statement:
“I can’t think of anybody guaranteeing that some malicious operator may not be able to crack some password/authentication mechanism and flood the server.”
No one guarantees zero risk. That is true in any internet-connected system—cloud or on-prem.
However, enterprises do not rely on hope, alerts, or budgets to control this risk. They rely on defense-in-depth, where billing exposure is indirectly capped by architectural limits.
2. Why enterprises are not “scared stiff” despite no billing cap
Large companies routinely run workloads with six-figure or seven-figure monthly cloud spends. They are not reckless; they simply do not depend on a single control.
Here is how the risk you described is mitigated in real enterprises.
3. Malicious traffic does NOT automatically mean runaway billing
3.1 Authentication alone is never the only gate
Enterprises do not expose critical APIs protected only by:
a password
or a single API key
Instead, they use combinations of:
OAuth / mTLS
IAM-scoped service identities
Private service-to-service networking
Signed requests
Short-lived credentials
Even if one credential leaks, blast radius is limited.
3.2 Rate limiting and quotas are enforced before compute
This is critical.
Using:
API Gateway
Load balancers
WAFs (Web Application Firewalls)
Enterprises enforce:
per-IP limits
per-token quotas
per-customer ceilings
global request caps
Excess traffic is dropped at the edge, often before it reaches billable compute.
So “flooding the server” does not automatically scale compute.
4. Hyperscalers actively absorb attacks, not bill you for them
This is often misunderstood.
DDoS protection is built in
AWS, Azure, and GCP all provide massive, always-on DDoS mitigation at the network edge.
Volumetric attacks are absorbed upstream
You are not billed for raw attack bandwidth
Autoscaling does not blindly spin up thousands of instances under attack
This is one of the reasons enterprises prefer hyperscalers over self-hosting.
5. Enterprises enforce hard operational ceilings (even if billing isn’t capped)
While there is no “$50,000 stop switch,” there are hard limits elsewhere:
Examples:
Max number of instances per service
Max autoscaling size
Fixed container concurrency
Fixed API quotas
Fixed regional deployment limits
Org-level quotas on resources
These are non-negotiable ceilings. Once hit:
traffic is rejected
service degrades
cost does not keep rising
This is how runaway billing is prevented structurally.
6. Budget alarms are the last line of defense, not the first
In enterprises:
Budgets are monitored by finance + ops
Alerts trigger incident response
There are on-call rotations
There are escalation policies
No serious IT head expects a budget alert to save them alone. It is merely a signal, not a safeguard.
7. Contracts and enterprise agreements matter
Large customers operate under:
enterprise contracts
negotiated SLAs
account managers
escalation channels
If something pathological happens:
there is dialogue
there is remediation
there is commercial resolution
This is very different from an individual developer on a credit card.
8. The uncomfortable truth (but also the reassuring one)
There is no such thing as absolute safety:
not in cloud
not on-prem
not in banking
not in aviation
not in nuclear plants
What enterprises aim for is:
bounded risk
layered defense
fast detection
controlled blast radius
Hyperscalers are designed for that world.
9. Why your reaction still makes sense
Your discomfort arises because you are thinking like:
a careful individual developer, or
a small team with no 24×7 ops
In that context, your concern is 100% justified.
Hyperscalers assume:
professional ops maturity
active governance
architectural safeguards
If those assumptions don’t hold, the platform does feel unsafe.
10. Bottom line
❌ No hyperscaler guarantees a billing hard cap
✅ Enterprises are not exposed to unbounded risk in practice
✅ Malicious traffic ≠ runaway billing by default
✅ Cost risk is controlled architecturally, not financially
⚠️ For individuals and small teams, this model is stressful
Your instinct is not naïve—it is simply calibrated for a different operating scale than what hyperscalers assume.
If you want, we can next discuss what a “cost-safe architecture” looks like for a small, one-person or hobby project, which is a very different (and solvable) problem.
Me: Fixed api quota sounds like a hard cap limit. Can u tell me more about how let's say gcp does it
CG: Yes — fixed API quotas are one of the few places in GCP that behave like a true hard cap, and they are a key reason enterprises are comfortable operating without a billing cap.
I will explain this precisely, concretely, and without hand-waving, using GCP mechanisms.
1. What “fixed API quota” actually means in GCP
In GCP, an API quota is a provider-enforced limit on usage of a service.
Once the quota is reached:
requests are rejected automatically
the backend service is not executed
billing does not continue beyond the quota
This is hard enforcement, not an alert.
Typical error returned:
429 RESOURCE_EXHAUSTED
2. Where GCP enforces quotas (important distinction)
GCP enforces quotas at multiple layers:
A. Service-level quotas (managed by Google)
Examples:
Requests per minute
Requests per day
Concurrent requests
CPU seconds
API calls
These are enforced before compute happens.
B. Project-level quotas
You can cap:
total requests per project
regional resource usage
number of instances
scaling limits
These create absolute ceilings.
3. The most relevant tool for APIs: API Gateway / Cloud Endpoints
If you expose an API publicly, this is the correct place to enforce hard caps.
What you can define:
Requests per minute
Requests per day
Per-consumer quotas
Burst limits
Global caps
These are defined declaratively (OpenAPI spec).
What happens when exceeded:
Gateway rejects the request
Backend is never called
No compute, no scaling, no additional cost
This is a true stop, not advisory.
4. How this differs from backend rate limiting
| Backend rate limiting | GCP API quota |
|---|---|
| Runs inside your code | Enforced by Google infra |
| Still consumes compute | Zero backend execution |
| You pay for rejections | You don’t |
| Can fail under load | Very robust |
This is why enterprises never rely solely on application-level throttling.
5. Example: Hard-capping a public API
Imagine you set:
10,000 requests/day
10 requests/second
Max backend instances = 1
Then:
A malicious actor cannot exceed 10,000/day
After that, every request is rejected
Your monthly cost is bounded
No alert is required to stop traffic
This is deterministic behavior.
6. What quotas CANNOT do (important)
Quotas are per service, not per billing account.
They:
cap usage of that service
do not cap total billing across all services
So:
API Gateway quota protects API cost
Cloud Run max instances protects compute cost
Storage quotas protect storage cost
Cost safety is achieved by combining quotas, not one global switch.
7. Why this works for enterprises
Enterprises define:
quotas conservatively
scaling limits intentionally
per-environment isolation (prod, test, dev)
This means:
even bugs or attacks hit ceilings quickly
cost impact is bounded
incidents are survivable
Billing alerts are used for visibility, not protection.
8. Important reassurance about malicious flooding
Even if:
authentication is compromised
traffic is malicious
requests are syntactically valid
Quotas still apply.
They do not care who is sending traffic — only how much.
This is exactly why they are trusted.
9. What an IT head actually relies on (summary)
Not:
budgets
hope
alerts alone
But:
API Gateway quotas
autoscaling caps
instance limits
concurrency limits
project-level quotas
Together, these create hard upper bounds on cost, even without a billing cap.
10. Bottom line (very crisp)
✅ GCP quotas are hard enforced
✅ Requests beyond quota are rejected automatically
✅ No backend execution → no cost
❌ Quotas are per service, not global billing caps
✅ Enterprises use quotas as cost safety rails
So while GCP lacks a single “₹X and stop” switch, fixed API quotas are one of the strongest tools it provides to achieve deterministic cost limits, especially for exposed APIs.
If you want, next we can walk through a concrete minimal setup (Cloud Run + API Gateway + quotas) that gives you a practically hard cost ceiling for a small API.
Me: So if I am hosting a small backend, I could use only global API quota - 10,000 per month. Now the tripping up point is the compute for that API. If some bug in the code causes a huge loop, then there is an issue. Is there a way at GCP (provider) level to limit execution time for an API?
CG: Yes. GCP does provide provider-level execution limits that act as hard stops, and this is precisely how you protect yourself against runaway code paths or accidental infinite loops.
I will be very explicit and separate what is guaranteed by the platform from what is merely best practice.
1. Short answer (direct)
Yes — on GCP you can impose hard, provider-enforced execution time limits for API backends without relying on your application code.
How this is done depends on the compute product you choose.
2. The safest choice for a “small backend”: Cloud Run
For a small API backend, Cloud Run is the correct reference model.
Hard execution time limit (provider-enforced)
Cloud Run has a request timeout:
Configurable: 1 second to 60 minutes
Default: 5 minutes
Enforced by Google’s infrastructure
When the timeout is hit:
request is terminated
container is killed
CPU is stopped
billing stops immediately
Your code cannot override or ignore this.
This directly protects against:
infinite loops
stuck I/O
accidental long computations
3. Hard CPU and concurrency limits
Cloud Run also allows:
A. CPU limits
You specify:
0.25, 0.5, 1, 2, 4 CPUs
Your code cannot exceed this.
B. Concurrency limits
You can set:
concurrency = 1
This means:
one request per container
no accidental amplification
predictable billing
4. Absolute cost bounding with instance limits
This is the most important safety valve.
You can configure:
max instances = 1
Effect:
no autoscaling explosion
only one container ever exists
worst-case cost is mathematically bounded
Even if:
quota allows traffic
code misbehaves
someone floods requests
Only one instance runs.
5. How all limits work together (this is the key insight)
For a public API, you combine:
API Gateway quota
e.g. 10,000 requests/month
Cloud Run request timeout
e.g. 30 seconds
Cloud Run max instances
e.g. 1
Cloud Run concurrency
e.g. 1
Now compute the worst case:
10,000 requests × 30 seconds × 1 vCPU
That is your absolute upper bound.
No alert required.
No human intervention required.
No billing surprise possible beyond that envelope.
6. What happens if code goes into an infinite loop?
Scenario:
Request enters backend
Code loops infinitely
Timeout hits (e.g. 30s)
Result:
request is killed
container process is terminated
CPU billing stops
next request gets a fresh container
Your exposure is capped at timeout × instances × CPUs.
7. How this compares to other GCP compute options
| Service | Hard execution limit | Safety |
|---|---|---|
| Cloud Run | ✅ Yes (timeout) | Best |
| Cloud Functions | ✅ Yes | Very good |
| App Engine | ⚠️ Partial | Medium |
| GCE VM | ❌ No | Dangerous unless carefully managed |
| GKE | ❌ No | Requires expertise |
For your concern profile, avoid VMs and GKE.
8. Provider-level vs code-level limits (important distinction)
| Control | Provider-enforced | Code-enforced |
|---|---|---|
| Timeout | ✅ | ❌ |
| Instance limit | ✅ | ❌ |
| CPU cap | ✅ | ❌ |
| Loop detection | ❌ | ❌ (not needed) |
You don’t need loop detection if execution is forcibly stopped.
9. Final reassurance (addressing your fear directly)
Your fear scenario:
“What if a bug causes a huge loop and I get billed endlessly?”
On Cloud Run:
impossible beyond the timeout
impossible beyond max instances
impossible beyond quota
This is not based on trust.
It is based on hard infrastructure enforcement.
10. Bottom line
✅ GCP can enforce execution time limits at provider level
✅ Cloud Run gives deterministic upper bounds
✅ Infinite loops are killed automatically
❌ Raw VMs do not give this protection
If you want, next I can help you design a “₹0-surprise” reference setup for a tiny API using Cloud Run + API Gateway with concrete numbers.
Me: Very interesting input. Thanks.
CG: You’re welcome. I’m glad the explanation helped.
Your line of questioning has been very precise, and you have zeroed in on the real risk boundaries of hyperscaler platforms—something many people gloss over until they get burned.
If, at some point, you want to sanity-check a concrete setup (for example: “single Cloud Run service, API Gateway quota X, timeout Y, max instances Z — what is my absolute worst-case cost?”), I’m happy to walk through that with you in a similarly concrete, numbers-based way.
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