Inference Economics 101: Reserved Compute versus Inference APIs

How utilization, performance diffusion, and deployment tradeoffs determine where durable value accumulates as AI inference scales from experimentation to production

As AI inference moves from experimentation to production, the infrastructure layer is no longer converging on a single “best” model. Instead, it is bifurcating into two durable and economically attractive markets, each optimized for a different constraint.

On one side are reserved and hourly compute platforms, designed to deliver predictability, control, and determinism for customers willing to operate infrastructure directly.

On the other are inference APIs, which trade some control for scale, absorbing utilization risk and abstracting complexity in exchange for cost efficiency and speed.

Understanding this split—and the tradeoffs it implies around utilization, performance, and deployment—is essential to evaluating where value will accrue as inference becomes ubiquitous.

Reserved Compute Platforms vs Inference APIs: Two Attractive Markets with Different Strengths

The AI infrastructure market is bifurcating into two durable and attractive segments, each optimized for different customer priorities and economic tradeoffs:

• Reserved / hourly compute platforms optimize for predictability and control

• Inference APIs optimize for utilization efficiency and cost abstraction

Neither is categorically superior; each wins in a different region of the workload and buyer space.

1) Reserved / Hourly Compute Platforms: Predictability and Control

Compute platforms offering hourly or reserved GPU instances (e.g., SF Compute, Modal–style offerings) are attractive because they deliver determinism.

What customers are buying

• Guaranteed access to scarce GPUs

• Predictable performance and capacity

• Full control over the runtime and stack

• Clear, stable unit economics (GPU-hours)

Why this is valuable

• Many workloads require consistency more than elasticity

• Teams want to size infrastructure once and operate against it

• Enterprises value control, compliance, and reproducibility

Economic profile

• High utilization per customer when workloads are well sized

• Revenue tied to long-lived contracts or reservations

• Smaller number of customers, but:

  • Larger ACVs

  • Lower churn

  • Clear capacity planning

Tradeoff

• Limited aggregation across customers

• Growth scales linearly with GPU fleet expansion

• TAM constrained by customers willing to operate infrastructure

This model resembles:

Early AWS EC2 reserved instances, CoreWeave-style compute, or traditional hosting—capital intensive, but predictable.

2) Inference APIs: Utilization, Cost Abstraction, and Scale

Inference APIs are attractive for the opposite reason: they absorb complexity and utilization risk on behalf of customers.

What customers are buying

• Tokens or requests, not hardware

• Elastic capacity without provisioning

• Built-in batching, routing, and scaling

• Operational simplicity and fast time-to-production

Why this is valuable

• Most inference workloads are bursty and uneven

• Most teams do not want to manage GPU fleets

• Cost predictability at the application level matters more than raw unit cost

Economic profile

• Lower utilization per customer

• Higher utilization at the platform level via aggregation

• Revenue scales with:

  • Number of customers

  • Number of use cases per customer

  • Overall inference volume

Tradeoff

• Less deterministic per-request performance

• More exposure to price compression at the execution layer

• Requires sophisticated systems to maintain margins

This model resembles:

Stripe, Twilio, or Datadog—abstracting infrastructure into a product.

3) The Core Tradeoff: Predictability vs Aggregation

Key insight

Reserved compute platforms win by making one customer predictable.

Inference APIs win by making many customers efficient together.

4) Why Both Markets Remain Attractive Long-Term

• AI workloads are diversifying, not converging

• Some buyers will always prioritize control and determinism

• Others will always prioritize speed, elasticity, and simplicity

As a result:

• Reserved compute captures value where predictability matters

• Inference APIs capture value where utilization and abstraction matter

Growth in one does not cannibalize the other; it often expands the total market.

Clean takeaway

Reserved compute platforms and inference APIs are both attractive, but for opposite reasons: one monetizes predictability with fewer, larger customers; the other monetizes utilization through aggregation across many smaller customers.

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Inference Optimization

Utilization vs. Systems-Level Optimization: Why Utilization Dominates Inference Economics

While numerous systems-level optimization companies have developed techniques to enhance performance, only inference APIs—which directly manage GPU resources—possess the capability to optimize utilization.

A common intuition in inference infrastructure is that systems-level optimizations—higher tokens/sec, better kernels, smarter schedulers—are the primary drivers of durable advantage. In practice, for inference APIs, utilization is the first-order determinant of cost, margin, and competitiveness, while systems-level optimizations are second-order and increasingly commoditized.

1) The Two Levers Are Fundamentally Asymmetric

There are two ways to lower effective cost per token:

1. Increase tokens per second (performance)

2. Increase utilization (time GPUs are busy)

These levers behave very differently.

• Performance improvements reduce cost only if demand exists to consume the additional throughput.

• Utilization improvements reduce cost unconditionally, by eliminating idle time.

For most inference workloads—especially user-facing, bursty workloads—idle time dominates waste, not insufficient throughput.

2) Performance Advantages Compress Because Techniques Are Open and Diffuse

(With Representative Vendors)

Across modern inference APIs running similar model classes (e.g., 70B-parameter LLMs on A100/H100-class hardware), real-world tokens/sec variance is relatively narrow and shrinking.

Observed production ranges typically look like:

• Lower-throughput, aggregation-optimized APIs:
  ~120–150 tokens/sec

• Higher-throughput, latency-optimized APIs:
  ~220–260 tokens/sec

This ~1.3–2.1× spread reflects different optimization priorities rather than fundamentally different technology.

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Representative Vendors by Throughput Orientation

(Illustrative, not exhaustive; varies by model, region, and configuration)

Higher Tokens/sec (Latency / Performance Optimized)

These platforms tend to push hardest on kernel-level and model-specific optimizations, often trading off breadth or flexibility.

• Fireworks AI

  • Heavy focus on latency and throughput (tokens/s)

  • Aggressive batching and kernel tuning

  • Narrower operational envelope per model

• BaseTen (dedicated / reserved setups)

  • High sustained throughput when utilization is high

  • Performance stability prioritized over elasticity

  • Best-case tokens/sec when capacity is fully consumed

• Select hyperscaler inference offerings

  • Deep integration with hardware-specific runtimes

  • Strong performance under controlled workloads

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Lower Tokens/sec (Aggregation / Cost Optimized)

These platforms optimize for heterogeneous demand, model breadth, and utilization, rather than peak throughput.

• DeepInfra

  • Broad model catalog

  • Conservative batching to support mixed workloads

  • Optimized for platform-level utilization rather than per-model speed

• Together.ai (non-reserved, general API paths)

  • Trades some peak throughput for flexibility and rapid model availability

  • Performance varies widely by model and load

• General multi-tenant inference APIs

  • Support many models and traffic shapes concurrently

  • Throughput traded off in favor of reliability and aggregation

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Why These Differences Persist—but Compress

The key observation is not who is faster, but why the gap is bounded:

• The fastest vendors rely on:

  • Flash-Attention variants

  • TensorRT-LLM

  • Triton kernels

  • KV-cache and batching strategies

• These techniques are:

  • Largely open source

  • Rapidly upstreamed into shared libraries

  • Adopted across vendors within quarters

As a result:

• Performance leaders change frequently

• The slowest credible APIs rarely fall more than ~2× behind

• The economic impact of performance differences is often dominated by utilization

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Key Implication

Being the fastest inference API confers temporary advantage; being the most utilized confers durable advantage.

3) Utilization Variance Is Larger and More Durable

By contrast, utilization variance across inference platforms is both larger and more persistent.

It is common to observe:

• ~40–55% utilization on lightly aggregated or partially reserved systems

• ~70–85% utilization on well-designed, multi-tenant inference APIs

This represents a ~1.5–2.0× difference in effective cost, independent of tokens/sec.

Crucially:

• Utilization is driven by aggregation, customer mix, traffic heterogeneity, admission control, and product abstraction.

• These are business-model and architecture choices, not kernel tweaks.

• They do not diffuse via GitHub.

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4) Concrete Comparison: Highest vs Lowest Tokens/sec Inference APIs

To make the tradeoff tangible, compare the best-case performance leader with a conservative, aggregation-optimized inference API running the same model class under production conditions.

For a 70B-parameter LLM on A100/H100-class hardware:

• Highest-tokens/sec inference API: ~230–260 tokens/sec

  • Aggressive kernel fusion

  • Highly tuned attention implementations

  • Large batch sizes under load

  • Often prioritizes throughput/latency over flexibility

• Lowest-tokens/sec (credible) inference API: ~120–150 tokens/sec

  • Conservative batching

  • Broader model catalog

  • More heterogeneous workloads

  • Optimized for reliability and aggregation

This is roughly a ~1.7–2.1× gap in raw tokens/sec.

Now layer in utilization:

Despite being nearly 2× slower on paper, the aggregation-optimized platform can deliver meaningfully better economics because its GPUs are busy far more of the time.

Two observations follow:

1. The fastest API is rarely the cheapest in practice. Peak tokens/sec only matters when demand is perfectly aligned and sustained—uncommon outside narrow workloads.

2. The performance gap is bounded and compressing. Even the slowest serious inference APIs are usually within ~2× of the fastest, while utilization differences regularly exceed that.

In inference APIs, the difference between “best” and “worst” performance is bounded; the difference between “busy” and “idle” infrastructure is decisive.

5) When Systems-Level Optimization Still Matters (Second-Order)

This does not imply systems work is unimportant.

Performance optimizations matter most when:

• Utilization is already high and stable

• Latency directly impacts revenue

• Workloads are homogeneous and predictable

• SLAs are strict

In these regimes, performance improvements compound on top of utilization.

However, for most inference APIs, performance is a multiplier on utilization, not a substitute for it.

Clean takeaway

Utilization variance dominates performance variance—and unlike performance optimizations, utilization advantages do not open-source away.

Strategic Synthesis

Utilization determines whether an inference API is competitive at all.

Systems-level optimizations determine how competitive it is once it is.

Because performance techniques are largely open, shared, and fast-diffusing, durable advantage in inference APIs comes from aggregation, abstraction, and control, not raw execution speed.

Utilization Assumptions (for Pricing & Cost Models)

All cost and unit-economics comparisons in this report assume observed production utilization, not theoretical peak throughput. A critical distinction throughout is between customer-level utilization and platform-level utilization—they behave very differently and are often conflated.

Key Definitions

• Customer-level utilization: How busy GPUs are for a single customer or single workload.

• Platform-level utilization: How busy GPUs are across the entire platform, aggregating many customers and workloads.

Most misunderstandings come from applying customer-level intuition to platform-level economics.

Utilization Tiers Used Throughout

10–40% Utilization: Customer-Level Reality (Small, Isolated, or Reserved)

This band is normal for individual customers, even when systems are well engineered.

Who lives here

• Series A–C AI startups
  Enterprise internal AI teams

• Teams using reserved GPUs or BYOC deployments

• Products with one or two core AI features

Why utilization falls in this range

• Capacity is provisioned for p95–p99 traffic, not averages

• Demand is bursty and diurnal
  Multiple models or pipelines fragment GPU memory

• Latency SLOs prioritize headroom over cost efficiency

Representative examples

• A Series B startup running reserved A100s for a customer-facing copilot

• A Fortune 500 company deploying an internal HR or IT chatbot

• A SaaS product with one AI feature used heavily during business hours

Key point

Utilization in the 10–40% range reflects traffic isolation and risk tolerance, not poor execution.

This is the dominant reality for most companies below platform scale.

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40–60% Utilization: Mature Single-Tenant or Light Aggregation

This band reflects operational maturity without hyperscale aggregation.

Who lives here

• Mid-size SaaS companies with steady AI demand

• Large enterprises with a single, heavily used internal platform

• Vendors offering semi-dedicated capacity to a small number of customers

Why utilization improves

• Demand becomes more predictable

• Fewer models are active per GPU
  Batching and admission control are consistently applied

Representative examples

• A public AI SaaS company with steady weekday traffic

• A large enterprise analytics copilot used company-wide

Important constraint

• This is often the upper bound for single-tenant or lightly multi-tenant systems

• Pushing beyond ~60% usually requires:

  • Aggregating many customers, or

  • Accepting higher tail latency

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60–85% Utilization: Platform-Level Aggregation (Large Platforms)

This band is structurally limited to platforms that aggregate many customers.

Who lives here

• Multi-tenant inference APIs

• Large inference platforms serving many startups

• Hyperscaler-managed inference services

Why utilization is high

• Many small, asynchronous demand curves smooth out

• Peaks rarely align (statistical multiplexing)

• Platforms can batch aggressively and queue intelligently

• Startup customers tolerate shared infrastructure and mild latency variance

Representative examples

• OpenAI-style public APIs

• Large inference platforms serving hundreds or thousands of startups

Critical caveat

• Sustained utilization above ~85% sharply increases tail latency and failure risk

• Most user-facing platforms deliberately operate below this ceiling

Implications for the Rest of This Report

1. Startup-heavy platforms can still achieve high utilization
   Individual startup customers are low-to-moderate utilization, but aggregating many of them drives platform-level utilization into the 60–85% range.

2. Reserved and BYOC models structurally reduce utilization
   Pinning capacity to customers collapses the benefits of aggregation.

3. Utilization differences dominate cost outcomes
   A platform running at 70–80% utilization will typically outperform a faster system running at 40–50%, even if tokens/sec is lower.

Utilization is primarily a function of aggregation and abstraction—not company size or engineering quality.

All subsequent cost, performance, and market analysis should be interpreted through this lens.

Why 10–25% Utilization Is Real (and Common)

When people hear “10–25% GPU utilization,” they often imagine mismanagement. In practice, it usually reflects normal product behavior, not incompetence.

1) Early-Stage and Growth-Stage Products

For most inference-backed products before real scale:

• Traffic is bursty (peaks during business hours, troughs at night)

• Feature usage is uneven (one agent or endpoint dominates)

• Capacity is sized for p95 or p99, not average

Result:

Even “healthy” systems sit at 10–30% average utilization.

This mirrors:

• Web servers pre-autoscaling

• Databases sized for peak QPS

• Early SaaS infra economics in general

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2) Reserved Capacity Is Sized for Worst Case, Not Average

Any system with:

• SLAs

• Latency SLOs

• Human-facing UX

must provision for spikes, not medians.

Example:

• Peak demand: 5× average

• You size for peak

• Average utilization → ~20%

This is by design, not error.

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3) Multi-Model and Multi-Tenant Fragmentation

Utilization collapses when:

• Multiple models are deployed in parallel

• Each has independent traffic patterns

• GPU memory constraints prevent consolidation

Even if total demand is high:

• Individual GPUs may be underutilized

• Fragmentation blocks efficient packing

This is especially common in:

• Enterprise copilots

• R&D-heavy teams

• Products experimenting with multiple models

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4) Why Serverless APIs See 60–85% While Customers See 10–25%

This is the key contrast.

Single customer / reserved setup

• Demand curve = narrow

• Variance = high

• Utilization = low

Aggregated serverless platform

• Demand curves from thousands of customers overlap

• Variance smooths out

• Utilization rises to 60–85%

This is not magic — it’s statistical multiplexing.

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Is 10–25% the Floor or the Average?

Important nuance:

• 10–25% is common for:

  • Early-stage products

  • Reserved GPUs

  • Internal tools

• 40–60% is achievable for:

  • Mature SaaS with predictable usage

  • Well-tuned admission control

• 70–85% is rare outside:

  • Large serverless platforms

  • Highly constrained, always-on workloads

Sustained >85% utilization is dangerous unless you’re:

• Willing to drop requests

• Willing to increase latency

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Quantitative Reality Check

Consider a product with:

• 8 hours/day of peak usage

• 16 hours/day of low usage

• 2× peak-to-average ratio

Even with good smoothing:

That’s a well-run system.

Now add:

• Model experimentation

• Geographic skew

• Release cycles

You’re back in the 20–30% range quickly.

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Why This Matters Strategically

This is why:

• Reserved GPU inference looks cheap on paper and expensive in reality

• Performance optimizations often disappoint economically in isolation

• API-based inference dominates long-term share

It also explains why:

• Systems that maximize utilization outperform those that maximize peak throughput

• Control planes beat raw execution engines

Inference APIs vs. Customer Utilization: Why Startup-Heavy Platforms Can Run High Utilization

A common misunderstanding in inference economics is the assumption that serving mostly startups implies low utilization. This confusion comes from conflating customer-level utilization with platform-level utilization—two very different things.

Customer-Level vs Platform-Level Utilization

At the customer level (typical startup behavior):

• Utilization is often low (10–30%)

• Traffic is bursty and uneven

• Workloads are small and episodic

• Capacity is sized for peaks, not averages
  

This is true for most startups, and it is entirely rational.

At the platform level (inference APIs):

• Hundreds or thousands of independent demand curves are aggregated

• Peak usage times rarely align

• Idle capacity smooths out statistically

This is classic statistical multiplexing.

A platform serving many low-utilization customers can itself operate at 60–85% utilization.

This is not unique to inference. It is exactly how infrastructure platforms like:

• AWS EC2

• Datadog

• Stripe

• Twilio

achieved strong unit economics early—despite serving predominantly small customers.

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Why Startup Customers Often Improve Platform Utilization

Counterintuitively, startup-heavy customer bases are often better for utilization than a few large customers.

1) Asynchronous, Heterogeneous Workloads

Startups tend to:

• Operate across time zones

• Have different peak hours

• Use different models, endpoints, and request shapes

This creates natural demand smoothing.

Contrast this with:

• One large enterprise customer

• One internal copilot

• One dominant model

Those scenarios concentrate demand and make it harder to keep GPUs busy.

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2) Fewer Hard SLAs

Startup customers are typically more tolerant of:

• Mild latency variance

• Queueing under load

• Shared infrastructure

That tolerance allows inference APIs to:

• Batch more aggressively

• Apply stricter admission control

• Run GPUs closer to saturation

By contrast, enterprise customers often impose:

• Dedicated capacity requirements

• Strict tail-latency guarantees

• Isolation constraints

All of which structurally reduce utilization.

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3) Minimal Demand for Reservation or Isolation

Most startups:

• Do not request dedicated GPUs

• Do not require VPC isolation

• Default to fully serverless APIs

This allows inference platforms to:

• Pool capacity globally

• Move workloads dynamically

• Optimize scheduling and batching across customers

That flexibility is the core utilization advantage of inference APIs.

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When Utilization Drops for Inference Platforms

There are two scenarios where even inference APIs experience utilization pressure.

1) Growth in Reserved or Dedicated Capacity

If a platform:

• Pins GPUs to specific customers

• Sells large amounts of reserved capacity

• Guarantees isolation or fixed latency

Then utilization begins to resemble:

• 40–60% in well-sized cases

• 20–40% when demand variance is high

This is why reserved inference tends to be expansion revenue, not the economic core.

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2) Heavy Skew to a Single Model or Workload

Utilization also drops when:

• A single model dominates demand

• Requests cluster in time

• Latency SLOs are strict

In these cases:

• Capacity must be overprovisioned

• Scheduling flexibility decreases

However, these are also situations where:

• Pricing power increases, or

• Routing and scheduling logic becomes strategically valuable

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Where Inference APIs (i.e. DeepInfra, Fireworks, Together) Sit (Illustrative, Not Unique)

For a modern inference API with:

• A serverless model

• A startup-heavy customer base

• A broad catalog of models

It is reasonable to expect:

• Platform-level utilization: ~60–80%

• Customer-level utilization: often 10–25%

• Effective economics: closer to hyperscale APIs than reserved-GPU platforms

In other words:

Startup customers are low-utilization individually.

Inference APIs that aggregate them do not have to be.

This dynamic is inherent to the API model itself, not specific to any single provider.

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Why This Matters Strategically

This distinction explains several broader conclusions in the report:

• What segments of the market API-based inference versus reserved capacity captures long-term

• Why utilization variance dominates performance and tokens/sec variance

• Why inference APIs are structurally different from reserved-GPU platforms

It also clarifies why inference APIs optimized for aggregation and cost discipline are not simply “worse versions” of platforms optimized for model novelty or research adjacency. They are solving different problems and, critically, exhibit different utilization dynamics.