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Self-hosting an LLM gives your team responsibility for the serving system. An inference API delegates part of that responsibility to a supplier. The right choice depends on workload, control requirements and operating capacity, rather than a universal claim that local inference is cheaper or more private.
This comparison is an engineering decision framework. It contains illustrative arithmetic and a pilot plan; it does not report GPU benchmarks, measured model quality or supplier uptime.
Name the boundary you want to control
“Self-hosted” can mean a developer laptop, a rented accelerator, a managed deployment in your cloud account or hardware in your own facility. Those options have different operators, network paths and maintenance duties. Write down which organization can access prompts, weights, storage and logs.
An API can likewise be direct to a model provider or routed through an intermediary. Check each processing party and its applicable terms. A regional API hostname alone does not establish where every operation, log or backup is processed.
| Question | Self-hosted responsibility | API responsibility |
|---|---|---|
| Serving capacity | Provision, schedule and monitor your deployment | Fit documented quotas and supplier capacity |
| Updates | Choose and validate runtime, weights and dependencies | Validate supplier changes and available versions |
| Reliability | Operate recovery, redundancy and resource protection | Handle outages, deadlines and fallback contracts |
| Data controls | Configure access, storage, logs and deletion | Assess contractual and technical processing boundaries |
| Cost | Pay for allocated resources and operations | Pay published or negotiated usage and commitments |
Neither column removes your application's authorization, evaluation or customer-support obligations.
Check whether the model fits the task
Start with representative tasks and an acceptance rubric. Test language coverage, context needs, output structure and required tool behavior. A deployment that runs successfully is not automatically useful for your users.
For self-hosting, identify the exact weight revision, license, runtime, precision and configuration. Confirm you are allowed to use and distribute the model in the intended way. Memory demand includes more than stored weights: concurrent contexts, runtime overhead and other components also consume resources. We have not tested a particular accelerator configuration here, so no hardware sizing guarantee is offered.
For an API, record the endpoint and model identifier, supported parameters and version policy. A model name that resembles another supplier's offering does not establish identical behavior. Use compatibility contract tests to check actual assumptions.
Build a break-even model with honest assumptions
Use a shared unit: cost per accepted task over the same operating period. Include all attempts, idle capacity and required redundancy. Do not compare the best second of a local benchmark with a full month of API traffic.
self-host monthly cost = allocated compute + storage + network
+ operations labor + support + redundancy
API monthly cost = billable model operations + related services
+ integration/operations labor + commitmentsSuppose, purely for calculation, self-hosting totals $1,200 per month and an API totals $120 fixed operations plus $0.004 per accepted task. The simplified break-even quantity is ($1,200 − $120) / $0.004 = 270,000 accepted tasks per month. These are invented scenario inputs, not market rates or tested capacity.
The calculation is useful only if the self-hosted service can deliver that number of accepted tasks at the required latency and reliability. If another replica is needed at 150,000 tasks, the fixed-cost assumption changes and so does the break-even point. If model quality differs, compare accepted outcomes rather than raw requests.
Peak demand can dominate the decision
Average utilization is insufficient for interactive services. A workload of concentrated daytime requests may require more capacity than an evenly distributed batch job with the same monthly token count. Queueing can make nominally inexpensive hardware costly when customers abandon slow tasks.
Determine the maximum waiting time, concurrency and request size your product permits. Test admission limits before load testing a provider or machine. Define what happens when capacity is exhausted: reject promptly, queue bounded work or switch to an explicitly evaluated fallback.
An API's burst quota is not a guarantee of unlimited capacity. A self-hosted deployment's full resource ownership is not a guarantee that it can absorb arbitrary input lengths. Both need overload behavior, cancellation and a budget for repeated attempts.
Privacy requires a complete data-flow review
Self-hosting can reduce external prompt processing, but it does not automatically remove leakage through logs, telemetry, backups or operator access. Inventory those paths and test deletion. Isolate tenant data and restrict access to model administration.
For an API, review retention, training-use terms, subprocessors, regional controls and available contractual commitments for the actual account. The AIx data privacy documentation describes its own handling and distinguishes upstream provider policies. Treat that as documented policy to assess, not proof that every routed model meets your obligations.
Sensitive workloads may require a narrower approved model/provider set, private networking or a different deployment boundary. Make the requirement concrete before procurement. “No external processing of these documents” is actionable; “enterprise privacy” is too vague to implement or test.
Maintenance is recurring engineering work
Self-hosting requires vulnerability management, capacity monitoring, deployment rollback and recovery practice. Someone must respond when a driver, runtime or model update changes behavior. Count that work even when the team already owns hardware.
APIs also need maintenance. Credentials expire or rotate, quotas change, models deprecate and output behavior shifts. Keep contract tests and evaluations that can distinguish a supplier regression from an application change. Do not assume an interface-compatible replacement is a quality-equivalent replacement.
For small teams, a managed API may reduce infrastructure work enough to justify a higher unit charge. For stable high-volume workloads with appropriate expertise, dedicated serving may offer useful control. These are decision patterns to test, not performance claims.
Run a bounded pilot
Freeze a sanitized evaluation set, scoring rubric and budget. Record the API configuration or local hardware/runtime details. Measure end-to-end latency, accepted-task rate, errors and total operating cost under a representative load schedule. Include a warm-up policy and distinguish cold starts from steady state.
Use the same input/output limits where feasible, and report meaningful differences where they are not. A provider may apply a tokenizer or internal processing policy you cannot observe; record that uncertainty. Keep results reproducible using the latency methodology, rather than declaring a winner from one request.
Can you use both?
A hybrid design can route selected tasks locally and others to an API, but it adds policy and evaluation work. Specify which data may leave the boundary, what triggers fallback and how customers are informed. An outage must not silently move restricted documents to an unapproved supplier.
Start with one well-understood deployment unless requirements justify multiple paths. Review the decision after real traffic changes the assumptions. The next step is a signed-off workload and ownership worksheet, followed by a limited pilot—not purchasing hardware based on an unverified tokens-per-second claim.