Begin with the commercial boundary
Dedicated compute rental and a managed model API can transfer different responsibilities to the buyer. A compute comparison needs to state who supplies the runtime, model loading, monitoring, upgrades, incident handling and capacity planning. An API comparison needs to state model revision, supported features, pricing units and service limits.
Aguasto's analytical view is that the useful question is the cost of an accepted workload over time. A low device-hour rate is not sufficient evidence of a cheaper application if the configuration misses latency targets, needs extra replicas or consumes substantial engineering effort. These are evaluation criteria; Aguasto publishes no available inventory, supplier price or return forecast.
Use provider categories as examples, not standards
Together AI's official product explanation distinguishes shared serverless service billed per token from dedicated resources billed by GPU-hour. That is evidence of Together's service model, not a universal definition for every provider. Its separate provisioned-throughput category also shows that a simple shared-versus-dedicated binary can hide relevant alternatives.
For a buyer worksheet, name the actual service being evaluated and transcribe only its verified pricing units and responsibility scope. Check whether reservation, replica count, minimum commitment or different input/output token rates change the calculation. Do not infer available capacity or an Aguasto quotation from another operator's published offer.
Dedicated Model Inference — Together AI (external source)Idle capacity belongs in the denominator
Together's knowledge base notes that dedicated reservations incur cost while idle. A dedicated configuration may become more economical under high utilization, but that does not establish a universal utilization threshold. The observed workload, required spare capacity and operating scope determine the result.
For a hypothetical period, sum the billed resource cost over all provisioned replicas, then divide by actual completed output tokens. Multiply by one million for a comparable unit. Keep failed attempts and retry work in cost while reporting their token accounting separately. A zero-token period has cost but no defined cost-per-million-completed-tokens result; it must not display zero or infinity as a competitive rate.
Dedicated vs Serverless models — Together AI Knowledge Base (external source)Autoscaling changes both cost and response time
Baseten documents minimum and maximum replicas, scale-to-zero and startup behavior. It notes that starting a replica is billable even before serving, and that a synchronous request can wait during a cold start. These provider-specific details show why a monthly average traffic number is incomplete.
Evaluate a representative time series with idle stretches, bursts and sustained demand. Record how many replicas remain warm, how long newly started capacity takes to become useful, and whether rejected or queued work meets the application objective. Run a separate scenario for failover reserve. Do not assume every minute of billed GPU capacity delivers the same number of acceptable responses.
Autoscaling — Baseten official documentation (external source)Compare quality and accounting consistently
The proposed comparison keeps model/task quality, prompt/output distributions, latency objectives and reporting period consistent. Count compute, network and operations in the same scope on both sides. If an API charges differently for input and output, retain those separate line items; do not compare its full bill to only the electricity component of dedicated infrastructure.
Display assumptions alongside results and let the reader change them. A sensitivity view should vary demand and replica policy before suggesting a decision. Avoid a payback claim unless all capital, operating, financing and residual-value assumptions are supplied and independently reviewed. An educational calculator is a scenario, not an investment case or an offer.
A practical choice can change over time
Aguasto's analysis favors revisiting the deployment boundary as workload evidence improves. Early variable demand, a narrowly supported model and a small operations team may justify a different arrangement from steady traffic with a verified serving configuration. Document the reason for a decision and the threshold that would trigger reevaluation.
The useful output is a dated workload brief: expected traffic shape, quality and latency needs, permitted locations, required isolation, model/engine support and verified cost inputs. It enables a concrete commercial discussion without claiming that planned Aguasto compute or inference services have launched. No economics presented here guarantee utilization, supplier pricing or savings.
Sources & verification
Reference revision 1 · Reviewed . Manufacturer claims and documentation are attributed; Aguasto has not run the cited products in production.
- Dedicated Model Inference — Together AI ↗
Provider distinguishes per-token shared service and GPU-hour dedicated resource billing; Dedicated and serverless are provider-specific service categories. Publication: date not stated in the reviewed source. Verified: 2026-10-12.
- Dedicated vs Serverless models — Together AI Knowledge Base ↗
Dedicated hardware has cost while idle; High utilization changes economic comparison. Publication: date not stated in the reviewed source. Verified: 2026-10-12.
- Autoscaling — Baseten official documentation ↗
Independent replicas; minimum/maximum capacity; Scale-to-zero and startup billing/cold-start latency; Running-replica billing boundary is provider-specific. Publication: date not stated in the reviewed source. Verified: 2026-10-12.