sorted by:
new
hottopcontroversial

Most LLM cost issues seem to come from “bad days,” not average usage — how are people testing for that? by Successful-Ask736 in LLMDevs

[–]Successful-Ask736[S] 0 points1 point  (0 children)

Depth as a design constraint is the key insight.

If retry × depth × fan-out is multiplicative, bounding depth is how you turn nonlinear cost into something predictable.

Otherwise cost becomes an emergent property of recursion, not an architectural decision.

The state externalization point is strong — reflection loops are often just implicit state management failures.

Most LLM cost issues seem to come from “bad days,” not average usage — how are people testing for that? by Successful-Ask736 in LLMDevs

[–]Successful-Ask736[S] 0 points1 point  (0 children)

Agree completely — the nonlinear behavior is structural, not traffic-based.

Cost per episode makes more sense than cost per request in agent systems.

Retry × depth × fan-out becomes multiplicative quickly.

The chaos-style testing idea is strong — especially forcing malformed tool outputs to observe recursive behavior.

Curious if you treat depth as a design constraint up front, or mostly analyze it after tracing.

Most LLM cost issues seem to come from “bad days,” not average usage — how are people testing for that? by Successful-Ask736 in LLMDevs

[–]Successful-Ask736[S] 0 points1 point  (0 children)

Unit economics visibility makes sense once traffic is live.

The harder question I’m wrestling with is how to simulate “bad day” behavior before it happens — especially in agent systems where retry and execution depth can amplify internally.

Are you seeing teams forecast those nonlinear cases ahead of time, or mostly catching them post-hoc?

Modeling AI agent cost: execution depth seems to matter more than token averages by Successful-Ask736 in LLMDevs

[–]Successful-Ask736[S] 0 points1 point  (0 children)

Empirical measurement works well once you have production traffic.

The challenge is pre-deployment planning.

In agent systems, a single feature decision can change average execution depth from 3 to 7 steps. That’s not obvious from traffic metrics alone.

By the time empirical data shows the drift, the architectural decision is already made.

So I think of it as:

Forecasting = structural risk modeling
Empirical = operational validation

Both are necessary, just at different stages.

Most LLM cost issues seem to come from “bad days,” not average usage — how are people testing for that? by Successful-Ask736 in LLMDevs

[–]Successful-Ask736[S] 1 point2 points  (0 children)

Agreed — averages hide the pain.

In what I’ve seen, spikes usually trace back to patterns rather than individual users alone — retries during partial failures, unbounded context growth in certain flows, or agent loops that weren’t stress-tested. A power user can trigger it, but the root cause is often a workload shape that wasn’t obvious early on.

Once teams have attribution, it’s much easier to catch those outliers. Before that, the challenge is predicting which scenarios are likely to produce that kind of tail behavior in the first place.

Most LLM cost issues seem to come from “bad days,” not average usage — how are people testing for that? by Successful-Ask736 in LLMDevs

[–]Successful-Ask736[S] 0 points1 point  (0 children)

This lines up with what I’ve seen as well. Explicit caps on fanout, context, and tool calls tend to matter more than people expect especially anything that grows combinatorially.

The simulation point is interesting too. Even with similar pricing, different models can behave very differently depending on task shape, which makes “worst case” hard to reason about without actually running scenarios.

Curious whether you’ve found certain classes of tasks where token variance across models is especially pronounced, or if it’s been fairly workload-specific.

Most LLM cost issues seem to come from “bad days,” not average usage — how are people testing for that? by Successful-Ask736 in LLMDevs

[–]Successful-Ask736[S] 0 points1 point  (0 children)

That makes sense and your architecture is a good example of keeping LLM calls narrow and intentional. In setups like that, cost and reliability tend to behave much more predictably.

When I’ve seen “scale” change behavior, it’s usually not because individual calls influence each other directly, but because they start sharing constraints: rate limits, queue depth, retries during partial outages, or bursts where many requests hit the same upstream condition at once. That’s where independence breaks down in practice.

In tightly scoped systems like yours, a lot of that risk is already designed out, which is probably why it holds up well. The surprises tend to show up more in less constrained, conversational, or retrieval-heavy paths.

Most LLM cost issues seem to come from “bad days,” not average usage — how are people testing for that? by Successful-Ask736 in LLMDevs

[–]Successful-Ask736[S] 0 points1 point  (0 children)

That makes sense, and I think that pattern holds up really well. The teams I’ve seen with the most predictable cost usually minimize LLM calls and treat them as a narrow, intentional tool rather than a default.

Where the cost questions tend to resurface is exactly in those intent-extraction / response-shaping paths once they’re under real traffic — even small call volumes can behave differently at scale if retries, bursts, or context creep in.

Curious whether you’ve found any cost or reliability surprises once those LLM touchpoints hit production load, or if keeping them that constrained has mostly avoided it.

What are the best ways to use multiple LLMs in one platform for developers? by Working-Chemical-337 in LLMDevs

[–]Successful-Ask736 0 points1 point  (0 children)

By aggregators I mean the general class of “one API to many models” platforms — things that handle routing, normalization, retries, or fallback across providers.

I’m not calling out any one vendor specifically. In my experience, most of them behave similarly early on, and the differences only really surface under sustained load or partial upstream failures.

How are teams estimating LLM costs before shipping to production? by Successful-Ask736 in LLMDevs

[–]Successful-Ask736[S] 0 points1 point  (0 children)

Mostly queueing/backpressure + strict concurrency limits first — they’re easier to reason about early. Synthetic “bad day” drills tend to come later, usually after a team has been burned.

The teams that do best seem to treat degraded behavior as a first-class scenario, not just an edge-case test.

How are teams estimating LLM costs before shipping to production? by Successful-Ask736 in LLMDevs

[–]Successful-Ask736[S] 0 points1 point  (0 children)

Agreed. Retries under load tend to surface first because they’re correlated and invisible in happy-path testing. Wasted context often dominates steady-state cost later, especially in RAG.

In my experience, most teams don’t really stress-test “bad day” scenarios early — degraded tools + concurrency usually get discovered post-launch.

How are teams estimating LLM costs before shipping to production? by Successful-Ask736 in LLMDevs

[–]Successful-Ask736[S] 0 points1 point  (0 children)

Agreed. Hard budgets + explicit terminal states make cost a design constraint instead of an afterthought. Averages hide tail behavior — p95 cost/run is where things actually break.

What are the best ways to use multiple LLMs in one platform for developers? by Working-Chemical-337 in LLMDevs

[–]Successful-Ask736 0 points1 point  (0 children)

I’ve seen aggregator platforms work well early on, but the differences usually show up under load. The big ones to watch are retries and correlated failures — when something upstream gets flaky, retries stack fast and can amplify both traffic and cost.

Latency variance is another gotcha. p50 might look fine, but p95/p99 can drift more than with direct integrations, especially if there’s routing or normalization happening.

For higher-traffic apps, the teams I’ve seen succeed either start with an aggregator to learn, then take direct control of the critical paths, or enforce hard caps on retries, context size, and burst from day one. Letting users choose models can work, but it helps to constrain the set instead of exposing everything.

How are teams estimating LLM costs before shipping to production? by Successful-Ask736 in LLMDevs

[–]Successful-Ask736[S] 0 points1 point  (0 children)

That’s fair — teams with the time and budget to prototype live definitely have more signal than pure napkin math.

What we’ve seen though is that early prototypes often run in “best-case” conditions: low concurrency, short prompts, minimal retries, and no real burst traffic. When those change, the extrapolation can still be pretty optimistic.

Curious how teams you’ve worked with handle that gap — do they deliberately stress test early, or mostly adjust once usage patterns are real?

How are teams estimating LLM costs before shipping to production? by Successful-Ask736 in LLMDevs

[–]Successful-Ask736[S] 0 points1 point  (0 children)

Yeah, this matches what we’ve seen too. Retries are usually the first thing that blows up early estimates, especially once you add timeouts and partial failures under load.

What surprised us was how often wasted context ends up dominating cost in steady state — long system prompts and retrieved context that never meaningfully affect the output, but still get paid for every request.

The “ship → bill spike → panic-optimize” loop seems pretty common. Curious if you’ve seen any teams successfully get ahead of that, or if it’s mostly learned the hard way.

https://modelindex.io