Serverless vs Provisioned vs Dedicated Inference

TL;DR: Serverless inference fits uncertain or bursty traffic because spend follows usage. Provisioned throughput fits stable workloads that need reserved capacity and an SLA. Dedicated inference fits custom models, isolation, and consistent high volume. The break-even point depends on utilization, peak traffic, redundancy, and operating cost, not average token price alone.

Deployment mode is an economic and reliability decision. Two teams can use the same model and pay very different effective costs because one has volatile traffic and the other keeps dedicated hardware busy.

How do the deployment modes differ?

Mode	Billing pattern	Best for	Main risk
Shared API	Usage-based	Evaluation and common models	Variable capacity and limits
Serverless	Usage-based with automatic scaling	Bursty and uncertain demand	Cold starts and tail variation
Provisioned throughput	Committed capacity or token rate	Stable production load and SLA	Underutilized commitment
Dedicated endpoint	Reserved hardware or managed deployment	Custom models, isolation, consistency	Minimum spend and operations
VPC or self-hosted	Infrastructure and operations	Governance and full control	Engineering burden

Provider labels are not standardized. Review the actual capacity, hardware, scaling, region, and SLA behind each name.

When is serverless the right choice?

Serverless fits products that are still learning their traffic, launch-driven workloads, and applications with long idle periods. It avoids paying for unused capacity and can expose new models quickly. It is usually the simplest starting point for prototypes and early production.

Test cold starts, p95 latency, concurrency limits, and burst recovery. Automatic scaling cannot remove every queue or hardware constraint. Confirm whether scale-to-zero applies, whether cached state survives scaling, and how provider rate limits interact with a launch spike.

Usage pricing can become expensive at sustained high volume. Model expected and peak traffic rather than extrapolating from a short evaluation.

When does provisioned throughput fit?

Provisioned throughput reserves a defined service level without necessarily assigning a single physical endpoint. It can provide quotas, more consistent latency, and an uptime commitment. Together AI, major clouds, and other providers offer versions of provisioned capacity.

The economic question is utilization. Divide the commitment by completed accepted output at low, expected, and peak load. Include overage, unused capacity, failover, and regional redundancy. A commitment can lower unit cost while increasing total spend when demand falls.

Provisioned throughput is useful when the model is standard but the workload needs stronger guarantees than shared service. Custom weights or deeper kernel control may still require dedicated deployment.

When is dedicated inference justified?

Dedicated inference fits custom or fine-tuned models, strict isolation, model-specific optimization, stable high volume, or latency consistency that shared capacity cannot provide. It can enable custom kernels, quantization, batching, cache policy, and hardware selection.

Dedicated capacity also creates responsibility. Teams need checkpoint deployment, monitoring, redundancy, autoscaling, upgrades, rollback, and a plan for model replacement. Managed platforms can operate these layers, while VPC or self-hosted deployments place more work on the customer.

Calculate the value of control and consistency beside the hardware cost. A dedicated endpoint that prevents abandonment or supports a differentiated model can be justified even when its raw token cost is higher.

How is break-even calculated?

effective committed cost = commitment + overage + redundancy + operations

effective usage cost = API usage + gateway fees + retries + support costs

Compare the totals at several utilization levels. Include prompt caching, output length, retries, failed requests, and calls per interaction. Use hourly or daily traffic curves so peaks are visible.

Traffic volatility matters as much as volume. A product averaging 50% utilization with predictable demand can fit commitment better than one averaging 70% with extreme bursts that still require overflow capacity.

Recalculate when model prices, hardware, traffic, or context length change. Consumer applications often increase context and interactions faster than unit prices fall.

How should a team move between tiers?

Start with a usage-based API and measure the workload.
Identify models or routes with stable high spend.
Test provisioned or dedicated paths on the same prompts.
Model low, expected, and peak utilization.
Canary the committed path and preserve shared overflow.
Review utilization and quality monthly.

A hybrid setup can keep baseline traffic on committed capacity and send bursts to shared inference. Confirm model and response compatibility across paths.

Related Guides

Key Takeaways

Serverless fits uncertain demand; provisioned throughput fits stable standard-model workloads.
Dedicated inference fits custom weights, isolation, and model-specific optimization.
Average traffic hides peaks, overflow, and underutilization risk.
Break-even includes redundancy, retries, overage, and operations.
Hybrid shared and committed capacity can balance baseline efficiency with burst protection.

Frequently Asked Questions

Is dedicated inference always faster?

No. Dedicated capacity can improve consistency and allow optimization, but speed depends on hardware, model, serving engine, batching, cache, region, and load. Test the same workload against shared and dedicated paths and report p50 and p95 latency with quality.

What utilization makes dedicated capacity cheaper?

There is no universal percentage. It depends on the commitment, hardware, usable throughput, redundancy, and managed-service premium. Calculate effective cost at low, expected, and peak utilization. Include idle time and burst overflow rather than using a headline GPU price.

Can serverless handle a viral launch?

It can when the provider has sufficient quotas, concurrency, regional capacity, and burst scaling. Confirm limits and coordinate major load tests. Define fallback and graceful degradation. A shared API can scale automatically while still rejecting traffic above plan or provider capacity.