DEV Community: Saikiran Kalidindi

Your LLM gateway's token budget is a suggestion

Saikiran Kalidindi — Mon, 26 Oct 2026 14:43:09 +0000

If several teams share one LLM bill, someone eventually asks for a hard cap: team A gets two million tokens an hour, and not one more. Most LLM gateways offer that. I wanted to know how hard
the cap really is, so I built a gateway, put a budget on it, and hammered it with concurrent
streams.

With 128 streams in flight per tenant, spread across three gateway replicas, the usual implementation let a tenant spend 3.3× its budget. The approach this post describes held
the same tenant to 0.03% over: 5 tokens on a 20,000-token budget.

The code, the benchmark and the raw results are open source:
github.com/saikirankalidindi/hardcap.

Why request limits don't work for LLMs

A classic rate limiter counts requests. That's fine when every request costs about the same. An
LLM request can cost 10 tokens or 100,000, and you don't know which until the response is done:
output length is decided by the model, one token at a time, while the stream is open.

So gateways count tokens instead. The common implementation is:

On each request, look up how many tokens the tenant has used this window.
If used < limit, let it through.
When the response finishes, add its real token count to used.

Step 3 is the problem. Between admission and settlement, a request is invisible to the budget.
Run one request at a time and you overshoot by at most one response. Run 128 at once and all 128
are admitted while the tenant still looks under budget, and all 128 are charged when they finish.
The overshoot grows linearly with concurrency, which is exactly what the blue line shows.

Replicas make it worse, not better. Even with shared state in Redis, nothing reserves the tokens
that in-flight requests are about to use.

Reserve, then settle

The fix comes from payments, where it is old news: a card terminal authorizes an amount before
the sale and captures the real amount after. Applied to tokens:

admit   used + reserved + estimate ≤ limit ?   → reserved += estimate
stream  every 64 tokens: grow the reservation (or stop if the budget is gone)
settle  reserved -= reservation;  used += actual

Each of these is a single Lua script in Redis. Redis runs one script at a time, so for a given
tenant the checks and updates are serialized, no matter how many gateway replicas send them. That
gives one invariant, checked inside every admit and every extension:

used + reserved ≤ limit

Settlement swaps a reservation for the real count, so the only way past the limit is a token that
was generated without a reservation. The whole design comes down to deciding what happens to
those tokens.

The real problem is the estimate

What should you reserve? There are two obvious answers, and both are wrong.

Reserve max_tokens. It's safe, because the model can't exceed it. But most completions use a
fraction of their cap. With max_tokens: 1024 and a median response of about 150 tokens, every
in-flight request holds about 6× what it will use. Near the end of a budget, the remaining tokens
are all held and none are used, so new requests get rejected even though the budget isn't
spent. In the benchmark, about 4% of every budget was never usable, and at 128 streams each
tenant's clients saw roughly 2,900 "come back later" rejections.

Reserve a small fixed number. It's efficient, until a long response blows straight past it.

Hardcap reserves the rolling p95 of real output lengths for that route (the last 1,000
completed responses) and treats the 5% tail as an extension problem. As a stream gets within
32 tokens of its reservation, the gateway asks Redis for 64 more. If the budget can't cover them,
there are two policies:

allow: let the stream finish. Overshoot is bounded by the tail of the streams in flight when the budget ran out (+7.9% worst case at 128 streams).
truncate (the default): stop forwarding, send the client a final chunk with finish_reason: "length", and close the upstream connection so the provider stops generating.

Truncated responses are kept out of the p95 sample. Otherwise the estimate would drift down, cause
more truncation, drift down further, and spiral.

128 streams per tenant, 3 replicas	Worst overshoot	Budget usable	Truncated streams per tenant
Count after the fact	+234.3%	100%	0
Reserve `max_tokens`	never over	95.6%	0
Reserve p95, allow	+7.9%	98.7%	0
Reserve p95, truncate	+0.03%	98.7%	1.2

That 0.03% isn't a rounding error, and I didn't want to hide it. It is the tokens a provider
generates between the gateway closing the connection and the provider noticing: a chunk or two
sitting in a TCP buffer. The benchmark only catches them because ground truth is what the mock
upstream says it generated, not what the gateway thinks it forwarded. A real provider bills the
former.

Two kinds of "no"

Reservations created a problem I didn't expect. A tenant with 900 tokens left, all held by
in-flight requests, is in a different position from a tenant whose 900 tokens are spent. The
first will have room again in a second; the second has to wait for the window to reset. Return
the same error for both and clients either give up too early or retry a spent budget forever.

So the admission script checks one more thing when it rejects: would this fit if nothing were in
flight? If yes, it answers budget_busy with Retry-After: 1; if no, budget_exceeded with the
time until the window resets.

When a replica dies mid-stream

Reservations are only safe if they can't leak. Each one is stored with an expiry. A live stream
refreshes it, and the next admission for that tenant reclaims any that expired. If a replica
crashes, its tenants get their held budget back after the TTL (two minutes by default). The
benchmark checks this too: across every run, 0 reserved tokens were left behind once traffic
stopped.

What it can't recover is the usage of the streams that died with it. The provider billed those
tokens and the gateway never learned the count. That's in the limitations section of the README,
because it's a real gap.

What it costs

Two Redis round trips per request, plus a small extension call every 64 tokens. Through the full
path (auth, rate limit, reservation, settlement, Prometheus metrics), measured open-loop on one
laptop:

Load	Added p50	Added p99
1,000 req/s	+1.1 ms	+1.2 ms
2,500 req/s	+1.6 ms	+3.2 ms
1,000 req/s, streaming	+1.9 ms	+3.0 ms

For calls that take hundreds of milliseconds to seconds, that's noise. It isn't free at the top
end: five Python workers saturate somewhere between 2,500 and 5,000 req/s on my machine, and the
knee moved between runs. Profiling showed about 65% of the handler's CPU going to the Redis
client's pure-Python protocol code. Switching to its C parser (hiredis) moved one worker's limit
from about 1,300 to over 1,600 req/s. If raw throughput were the goal, the hot path would be Go. It
wasn't: the goal was a budget that holds.

Failover, briefly

A budget gateway also has to route around a failing provider. With a primary → secondary route at
200 streaming req/s:

Primary returns 503 for seven seconds: 0 of 4,000 requests failed, and p99 latency didn't move.
Primary process killed with SIGKILL: 26 of 4,000 failed. Every one of them was a stream already in flight on the killed process. Once bytes have reached the client, no gateway can retry transparently. Every request sent after the kill succeeded.

The bug the live test found

The benchmarks use a mock upstream, because measuring overshoot exactly needs ground truth. Before
release I also ran the same paths against a real API (Groq, gpt-oss-20b). Ledger charges
matched the provider's reported usage to the token, on unary and streaming calls. The truncation
check failed: a stream on a 60-token output budget ran to 400 billed tokens.

The model was a reasoning model. Reasoning models stream their thinking in a separate
delta.reasoning field before any delta.content appears, and Hardcap counted only content.
For the whole reasoning phase, the gateway saw zero tokens. It never asked to extend the
reservation and never cut the stream, so the budget was right back to "count it afterwards".
The mock upstream couldn't catch this: it only ever sent content.

The fix counts every delta the provider bills for: reasoning, reasoning_content, Anthropic
thinking blocks and tool-call arguments. Rerun: truncated after 49 tokens. The lesson
generalizes. Any part of the output the gateway doesn't count is output with no budget.

What I'd tell someone building this

Measure overshoot against the provider, not your ledger. In my runs the ledger and the upstream agreed to within 40 tokens, but only checking the upstream told me that.
The estimator matters more than the locking. Atomic Redis scripts are the easy part. Reserving the right amount decides whether you leave 4% of the budget unusable or 1.3%.
Split "busy" from "spent". It's one extra comparison, and it decides whether clients can behave sensibly.
Publish the uncomfortable numbers. Where it saturates, what a crash loses, which estimate
the benchmark doesn't test (input tokens, counted with a heuristic that the mock upstream
shares). A benchmark that only shows wins doesn't tell anyone much.
Test against a real provider, even when the mock is the better benchmark. The mock gave
exact ground truth, and it also shared my assumptions about what a stream looks like.

Everything here reproduces with one command per benchmark. The raw JSON, the machine specs and the
parameters are committed next to the charts:
github.com/saikirankalidindi/hardcap.

Mastering Python: Best Practices for Beginners and Advanced Developers

Saikiran Kalidindi — Fri, 12 Apr 2024 07:27:48 +0000

Python has established itself as a cornerstone of modern software development, powering everything from web applications to data science projects. Whether you're just starting your Python journey or have been coding in Python for years, adhering to best practices is essential for writing clean, efficient, and maintainable code. In this article, we'll explore some key practices that both beginners and advanced developers should follow to elevate their Python skills to the next level.

1. PEP 8: The Style Guide

The Python community values readability and consistency, and the Python Enhancement Proposal 8 (PEP 8) lays out the official style guide for Python code. Adhering to PEP 8 ensures that your code is easy to understand and maintain by yourself and others. Some key points from PEP 8 include:

Use 4 spaces for indentation.
Limit lines to 79 characters.
Use clear, descriptive variable names.
Follow consistent naming conventions (e.g., snake_case for functions and variables, CamelCase for classes).

# Example of adhering to PEP 8
def calculate_average(numbers_list):
    total = sum(numbers_list)
    average = total / len(numbers_list)
    return average

2. Virtual Environments

Virtual environments are essential for managing dependencies and isolating project environments. Tools like venv or virtualenv allow you to create separate environments for each project, preventing conflicts between different versions of libraries.

# Create a virtual environment
python -m venv my_project_env

# Activate the virtual environment
source my_project_env/bin/activate

3. Use List Comprehensions

List comprehensions are a concise and elegant way to create lists in Python. They improve readability and can often be more efficient than traditional loops.

# Traditional loop
squares = []
for i in range(10):
    squares.append(i ** 2)

# List comprehension
squares = [i ** 2 for i in range(10)]

4. Embrace Pythonic Idioms

Python has its own set of idioms and patterns that make code more idiomatic and expressive. For example, leveraging context managers with the with statement for resource management or using enumerate() for iterating over a sequence while keeping track of the index.

# Using context managers
with open('file.txt', 'r') as f:
    content = f.read()

# Using enumerate
fruits = ['apple', 'banana', 'cherry']
for index, fruit in enumerate(fruits):
    print(f"Index {index}: {fruit}")

5. Documentation Matters

Clear and concise documentation is crucial for understanding how your code works and how others can use it. Use docstrings to document modules, classes, functions, and methods following the Google Python Style Guide.

def greet(name):
    """
    Greets the user with the given name.

    Args:
        name (str): The name of the user.

    Returns:
        str: A greeting message.
    """
    return f"Hello, {name}!"

6. Unit Testing

Writing automated tests ensures the correctness of your code and makes it easier to refactor and maintain. Python's built-in unittest module and third-party libraries like pytest provide powerful tools for writing and running tests.

import unittest

def add(x, y):
    return x + y

class TestAddFunction(unittest.TestCase):
    def test_add(self):
        self.assertEqual(add(1, 2), 3)
        self.assertEqual(add(-1, 1), 0)

7. Continuous Integration

Integrating continuous integration (CI) into your workflow ensures that your code is automatically tested whenever changes are made. Services like GitHub Actions, Travis CI, or CircleCI can be used to set up CI pipelines for your projects.

Conclusion

By following these best practices, both beginners and advanced Python developers can write code that is not only functional but also clean, readable, and maintainable. Remember, the journey to mastering Python is ongoing, and continuously learning and applying these best practices will help you become a more proficient Pythonista. 🐍💻🚀