Dedicated vs shared LLM inference: when reserved capacity makes sense

Most teams start with shared inference and should. You send a request to a public endpoint, you pay per token, and someone else worries about GPUs. For a prototype, a low-traffic feature, or a workload that spikes unpredictably, that is the right trade.

The trade changes once traffic becomes steady. When a product runs coding agents all day, answers a high volume of chat, extracts fields from a stream of documents, or drives multi-step workflows on a schedule, the shape of the demand is no longer bursty. It is a baseline with peaks. At that point it is worth understanding what dedicated LLM inference changes, and what it does not.

This post is the decision framework. The follow-ups cover how to size capacity, how to benchmark before committing, and what the workload-specific concerns look like for coding agents and for chat, extraction, and workflows.

Two ways to buy the same tokens

Shared, token-based inference is a pool. Many tenants send requests to the same fleet, a scheduler batches them together, and you are billed for the tokens you consumed. Its strengths follow directly from the pooling: no commitment, no capacity planning, and cost that scales to zero when you send nothing.

Dedicated inference is a reservation. A set of GPUs runs your model for your traffic and nobody else's. You pay for the capacity whether or not you fill it, and in exchange you get behavior that is a function of your own load rather than everyone's.

Neither is better in the abstract. They optimize for different things, and the question is which of those things your workload is sensitive to.

What shared inference is good at

It is easy to dismiss shared endpoints once you are past the prototype stage, but they hold real advantages that a reservation gives up:

• Elasticity. A sudden 20x spike is the provider's problem, not yours.
• Zero idle cost. Nights, weekends, and quiet weeks cost nothing.
• No planning. You never have to estimate tokens per second or decide how much headroom to buy.
• Breadth. Switching models is a string change.

If your traffic is spiky, seasonal, or still growing in ways you cannot forecast, these advantages dominate. Reserving capacity for a demand curve you cannot draw is how teams end up paying for GPUs that sit idle.

What dedicated inference changes

Four things move when the capacity is yours.

Predictable capacity

On a shared pool, your throughput ceiling is whatever the scheduler grants you at that moment. Rate limits are the visible edge of that; queueing behind other tenants' traffic is the invisible one. With reserved capacity, the ceiling is fixed and known. You can plan a batch job, a launch, or a nightly workflow against a number that does not change based on what other customers are doing.

Isolation

Noisy neighbors are real in inference just as they are in any shared system. A tenant sending very long prompts changes the batching dynamics for everyone in the pool. Dedicated capacity removes that variable. Your latency distribution reflects your traffic mix, which also makes it far easier to debug: when p99 moves, something in your system moved it.

Reliability under load

The failure mode of a shared endpoint at peak is usually a 429 or a slow queue. Retries help, but every retry is more load. When the capacity is reserved, you decide how much headroom to hold above your own peak, so the behavior at the top of your demand curve is a choice you made rather than a surprise. It also makes graceful degradation possible: you know exactly when you are approaching the limit, and you can shed or defer low-priority work before it hits.

Cost that tracks capacity, not tokens

Token pricing is linear in usage. Reserved pricing is flat in usage and linear in capacity. Above some utilization, the flat line is cheaper. Below it, the linear one is. Where that crossover sits depends on the model, the prompt and completion mix, and how full you can keep the reservation, which is exactly what a LLM inference cost calculator is for. The point here is structural: once you are buying capacity, filling it is free, and that changes how you think about features like retries, self-verification passes, or running an extra reviewer model.

The compatibility question

A common worry is that moving off a shared endpoint means rewriting the integration. It usually does not. Most dedicated offerings, including Morph's, expose an OpenAI-compatible API, so the application keeps the same request and response shapes and the same client libraries, and the change is a base URL and a key. That matters less for the migration itself than for what it enables: you can run shared and dedicated side by side, route traffic between them, and fall back from one to the other, because the code path is the same.

Compatibility is not identity, though. Streaming behavior, tool-call formatting, and which parameters are honored can differ between implementations. Test the specific features you use before you route production traffic. The benchmarking guide covers what to check.

Signals that it is time

You do not need to hit all of these. Two or three is usually enough to justify running the numbers.

1. Your traffic has a floor. There is a level of load you serve every hour of every business day. That floor is what a reservation covers well.
2. You are hitting rate limits in normal operation, not just during incidents, and you have already tuned batching and caching.
3. Tail latency is a product problem. Multi-step agents compound it: a p99 of a few seconds on one call becomes a p99 of a minute across twenty. If users notice, isolation is worth paying for.
4. Your monthly token bill is large and steady. If the number barely moves month to month, you are already paying for capacity, just at the per-token rate.
5. You run scheduled or batch work that needs a throughput guarantee to finish on time.
6. You need a stable environment for evaluation. Comparing model versions or prompts is much easier when the serving layer is not a moving target.

Signals that it is not

Equally, some conditions mean shared inference is still the right call:

• Traffic is under an order of magnitude of your peak most of the time. Your utilization would be low.
• You change models frequently and want the freedom to keep doing so.
• The feature is new and you do not yet know its demand curve.
• Your peaks are much higher than your baseline and you cannot smooth them with queueing.

In these cases the discipline is to keep measuring. Steady traffic is a state you grow into, and the signals above will show up in your metrics when you get there.

A practical hybrid

The most common end state for teams at scale is not one or the other. It is a reservation sized for the baseline, with shared inference absorbing the peaks above it. That gets you the isolation and predictability for the traffic you can forecast, and the elasticity for the traffic you cannot, without paying reserved rates for capacity you would only use a few hours a week.

Getting there is a sizing exercise, which is the subject of the next post. The short version: measure your tokens per second over a representative week, find the level you exceed most of the time, and price a reservation at that level against what those same tokens cost on the shared endpoint. The dedicated inference overview explains what a reservation includes; the calculator turns your numbers into a comparison.

Where this fits for agent workloads

Agents are the workload that most often crosses into steady traffic without anyone deciding it should. A fleet of coding agents running in VMs does not send one request per user action. It sends dozens per task, all day, with long prompts full of file contents and tool output. That is a baseline, not a burst, and it has exactly the tail-latency sensitivity that makes isolation valuable, because every slow call delays every step after it.

If that is the workload you run, the coding agents post goes into the specifics. If it is chat, extraction, or scheduled workflows, start with the migration post.