Dynamic index sizing protects available API funds by creating a compounding efficiency loop, where larger cache indexes lower marginal inference costs per retrieval query. In Machinations Play, illustrative Remaining Inference Budget rises from 85 to 105 at step 15 after doubling the cache TTL multiplier, demonstrating how expanded caching accelerates fund preservation over repeated queries.
Key takeaways
- Increasing Search Cache TTL Multiplier from 2 to 4 leaves Remaining Inference Budget at 105 instead of 85 at step 15 in this model.
- Raising Base Query Token Cost from 12 to 18 reduces Remaining Inference Budget to 10 at step 15, showing that high base token consumption rapidly outpaces cache savings.
- Checking and containing Base Query Token Cost is the primary priority in this model because base query spend drains budget reserves before cache accumulation gains momentum.
How does search caching protect your inference budget?
Teams allocating operational resources across autonomous retrieval systems constantly balance caching infrastructure against raw generation expenses. In this model, setting Search Cache TTL Multiplier to 4 leaves Remaining Inference Budget at an illustrative 105 units at step 15 compared to an illustrative 85 units in the baseline. Conversely, raising Base Query Token Cost to 18 leaves the budget at 10 units. These values are illustrative model figures meant to display system relationships.
When an AI gateway retains prior query representations, each retained entry diminishes the marginal cost of downstream retrieval calls. This dynamic prevents budget depletion across repeated external queries by creating compounding savings as traffic persists over time.
What happens when you adjust the model levers?
The first lever is Search Cache TTL Multiplier, which establishes how long query entries persist in memory. In the baseline setting with a multiplier of 2, Remaining Inference Budget finishes at 85 at step 15. Doubling this lever to 4 keeps the budget at 105 at step 15, showing how longer retention protects capital.
Early in the play at step 1, both paths sit at 149. By step 5, the baseline path drops to 115 while the higher retention path remains at 125, proving that cache accumulation provides greater advantages as time advances.
The second lever is Base Query Token Cost, which sets the gross token spend required per query. Moving this lever from its baseline value of 12 up to 18 drives Remaining Inference Budget down to 10 at step 15. This illustrates how expensive baseline queries erode resources.
Looking at step 2, the baseline path holds 139 units while the elevated cost setting drops to 127. By step 8, the higher spend path reaches 52, whereas the baseline retains 100, showing that unchecked query costs overwhelm early reserves.
The card also has two more sliders. Remaining Inference Budget sets the compute tokens available for processing queries before step 1: 160 in the baseline, adjustable from 0 to 1000. Gateway Cache Size sets how many cached search results are stored before step 1: 2 in the baseline, adjustable from 0 to 100.
How do the parts interact in the model?
The model connects incoming query volume directly to cache accumulation and expenditure. Query Traffic Source generates raw query volume that enters Gateway Cache Size every hour. Search Cache TTL Multiplier dictates how many of these cached entries persist in storage instead of evaporating.
Meanwhile, Remaining Inference Budget begins with a fixed allocation and drains continuously into Inference Spend Sink. Effective Token Burn governs this outflow by subtracting cache hit reductions from Base Query Token Cost. As Gateway Cache Size expands, it lowers the burn rate, slowing budget depletion over each hour of traffic.
What do these results mean for planning decisions?
In this model, the first two levers drive opposite outcomes by influencing different sides of the expenditure equation. Adjusting Base Query Token Cost changes the baseline outflow immediately, causing rapid losses before caching benefits can take root. Lengthening cache retention works gradually, compounding over hours as the stored index grows larger.
A planner in this scenario should examine baseline query expenses first because high baseline token demands rapidly exhaust capital. Once base costs are managed, extending cache retention acts as a steady protector against ongoing operational drain.
What does the model leave out?
This playable model relies on simplified structures and explicit constraints. It assumes each discrete step represents one single hour of continuous traffic. Flow rates, baseline values, and proportional savings operate through static relationships without variation.
Real systems are more complicated than any model. The diagram includes only seven specific nodes and tracks just two stocks across fixed hourly steps without dynamic traffic shifts or external fluctuations.
Where does the topic come from?
This model was inspired by the publication titled Introducing Web Search API via AI Gateway, published by Cloudflare on 2 October 2026. This essay and its specific numbers represent the playable model's own illustrative scenario for exploring gateway dynamics.
How can you test these dynamics yourself?
You can explore these relationships directly by shifting the levers in the playable model. Try setting Search Cache TTL Multiplier to 0 or pushing Base Query Token Cost to 24 to see how quickly reserves deplete under extreme settings.
The parts of the model
The question it asks: How does dynamic index size in an AI gateway cache affect remaining inference budget over repeated retrieval queries?
- Query Traffic Source · Source
- Generates raw incoming search queries that populate the dynamic gateway index each hour.
- Gateway Cache Size · Pool
- Holds cached query representations that accelerate retrieval and reduce downstream token usage.
- Search Cache TTL Multiplier · Lever
- Sets the cache retention multiplier determining how many query entries persist per hour.
- Remaining Inference Budget · Pool
- Tracks the remaining API inference budget available to fund live retrieval queries in dollars.
- Base Query Token Cost · Lever
- Sets the baseline token expenditure in dollars incurred per hour before cache savings.
- Effective Token Burn · Register
- Calculates net hourly inference spend by deducting cache hit savings from baseline token cost.
- Inference Spend Sink · Sink
- Drains spent API budget units consumed by live query extraction and generation.
Assumptions and limits
- Each step of Play represents one hour of continuous API query traffic.
- Higher Gateway Cache Size reduces the rate of the Budget Depletion Flow proportionally.
- All initial stock values and flow rates are illustrative and intended for structural analysis.
A teaching model with illustrative values; each Play is a scenario, not a forecast.
Sources
- Cloudflare (blog.cloudflare.com)
About this model
Machinations makes these models with AI from a public source, and each one is a teaching scenario: its numbers are illustrative values chosen for the model, not measurements or forecasts. You can open this diagram in the Community to remix it and change it.