Shortlist Fireworks Reserved Throughput or Together Provisioned Throughput when you want a contracted allocation for a supported model. Compare Modal, Hugging Face Inference Endpoints, Replicate or Baseten when you mainly want a dedicated deployment that you can size, configure and operate with the provider. If you use Cerebras or another specialized service, evaluate a written capacity offer for the specific model and traffic profile before counting it as a reservation.

This is a documentation-based comparison. We did not buy capacity, run load tests or verify service credits. A dedicated endpoint can provide isolation or steady replicas. Contracted availability of capacity, supported overflow and recovery during a regional disruption are different purchase decisions.

Compare the commitment you actually need

Option	What to buy or evaluate	Cost and SLA checks
Fireworks Reserved Throughput	A throughput reservation for eligible inference	Model, covered allocation, overflow, minimum commitment and measurement
Together Provisioned Throughput	Reserved capacity with token-based pricing	Eligible models, usable volume, endpoint rules and contract scope
Modal Dedicated Endpoints	Dedicated instances and autoscaling controls	Billed resource time, minimum replicas, quotas and reservation terms if needed
Hugging Face Inference Endpoints	Selected instances with managed deployment	Replica billing, region, cold start and whether capacity is assured
Replicate or Baseten	Dedicated or configurable custom model deployment	Hardware availability, scaling and any contracted allocation

Fireworks and Together for model throughput

Fireworks describes Reserved Throughput separately from dedicated deployments. Its enterprise page describes overflow for its reservation offer. Read the current order form for the specific model. Ask whether overage enters a shared pool, incurs a different rate, gets queued or receives an error. Even a written throughput target can depend on the request shape and the conditions under which it is measured.

Together’s Provisioned Throughput overview describes reserved capacity and token-based pricing. Check what the model catalog offers under a reservation, and whether you need to change the endpoint or request routing. Decide how you will handle a model migration if a version is retired or you need a different context length. Ask if a second region or fallback model requires a separate commitment.

Dedicated endpoints when deployment control comes first

Modal’s dedicated endpoint documentation describes dedicated capacity, region and scaling controls, and resource-based billing. Hugging Face Inference Endpoints lets you select the serving engine and hardware for a managed deployment. Replicate describes dedicated custom deployments, and Baseten’s deployment approach supports managed deployment of model runtimes.

Ask these providers how quotas, allocation and scale-out relate. Your active workers may be isolated while extra workers still depend on available capacity. A minimum replica setting tells you the desired floor in the service configuration. For a guaranteed hardware floor in two regions, require a written commitment for both and a tested failover. Request the same details from a Cerebras account team if its inference profile matches your workload.

Size for actual demand and spend

Suppose an application receives 20 requests per second at peak. If a typical request generates 500 output tokens, aggregate demand is 10,000 output tokens per second at that arrival rate. That is a planning example, not a provider quota. Input lengths, bursts, concurrency, latency targets and an uneven output distribution can all change what the platform needs. Ask the provider to say how it accounts for input tokens, cached tokens, images, reasoning, context lengths and failed attempts in your exact offer.

Compare one month of effective spend. For time-based capacity, multiply billed instances by their hourly rate and billed hours, then include storage or network charges and any secondary site. For a reservation, add the committed amount and the cost of likely overage. Compare against a pay-per-use baseline measured under the same request distribution. Include a quiet week so you see how much of the floor sits unused.

Test the boundary before signing

1. Replay a synthetic or properly sanitized peak profile with representative inputs and outputs. Record p95 and p99 time to first token, full response time, errors and completed requests.
2. Reach the purchased allocation. Observe what happens just below, at and above it. Confirm retry guidance and whether your usage report identifies spillover.
3. Test a restart and regional incident under agreed conditions. Check how traffic reaches fallback and what data or model revisions it needs there.
4. Compare monitoring records with a sample invoice. Get the measurement period, exclusions, service credits and evidence process for the SLA.
5. Ask when the reservation starts, when it renews, how to reduce or transfer it, and how you can leave without losing model artifacts.

My take

Buy a reservation when the predictable peak matters more than the cost of unused capacity and the provider commits to the outcome your service actually needs. For a custom runtime, start by testing a dedicated endpoint and the ability to scale it. If the workload has spiky, uncertain demand, test the cost and behavior of overflow before committing the entire peak.

Related Articles

• Serverless versus dedicated GPU
• LLM inference cost calculator
• How to benchmark inference
• Cloud GPU providers
• GPU memory planning

FAQ

Is reserved throughput always dedicated hardware?

Ask the provider. A throughput allocation may be a commitment at the service layer, while a dedicated deployment reserves an instance topology or allocates isolated workers.

Is an uptime SLA enough for a latency target?

No. Ask for the definitions and measurement of throughput and latency, and test how the system behaves when a request falls outside the covered shape.

What if our usage is seasonal?

Get the terms for changing or transferring capacity. Model the slow period and peak separately, and compare flexible overflow with a larger fixed commitment.

Can we reserve one model and move the capacity later?

It depends on the contract and available models. Ask about version changes, equivalent capacity and cancellation before counting on portability.