Build vs Buy for AI Inference
The most fundamental AI infrastructure decision is whether to host models yourself or consume them as a managed API from leading providers. Self-hosting gives you control, privacy, and potentially lower costs at scale. Managed APIs give you the latest models, zero operational overhead, and instant scalability. Most enterprises end up using both for different workloads. The strategy determines which workloads go where and why.
Cost-Per-Token Analysis
Self-hosted inference costs $0.50-2.00 per million tokens at scale versus $3-15 for managed APIs. But self-hosting has fixed costs (hardware, staff) that only amortize at sufficient volume. We calculate your crossover point.
Capability Assessment
Managed APIs offer the most capable flagship models. Open-weight models are 6-12 months behind on general benchmarks but can match or exceed on specific tasks after fine-tuning.
Privacy and Control
Managed APIs process your data on shared infrastructure governed by provider terms. Self-hosting ensures zero data sharing. For some workloads this is a legal requirement, not a preference.
Operational Complexity
Managed APIs require zero GPU expertise. Self-hosting requires model deployment, monitoring, scaling, and updates. The operational cost is real and must be factored into the total comparison.
Hosting Strategy Decision
Classify
Workload sensitivity and complexity
Cost
Per-token economics at your volume
Evaluate
Model quality on your tasks
Route
Assign workloads to hosting mode
Classify
Workload sensitivity and complexity
Cost
Per-token economics at your volume
Evaluate
Model quality on your tasks
Route
Assign workloads to hosting mode
Model Hosting Comparison
When to Self-Host
Self-hosting makes economic and strategic sense in specific scenarios. We help you identify which of your workloads qualify.
High-volume, well-defined tasks. If you are processing 10 million+ tokens per day on a consistent workload (document extraction, classification, summarization), self-hosted inference is 3-10x cheaper than API pricing. A fine-tuned 7B model on a single A100 can match API quality on narrow, well-defined tasks.
Data privacy requirements. If your legal or compliance team prohibits sending data to third-party APIs, self-hosting is the only option. This is common in healthcare, legal, defense, and financial services.
Customization requirements. If you need fine-tuned models, custom guardrails, or output formats that API providers do not support, self-hosting gives you full control over the model and serving pipeline.
When to Use Managed APIs
Managed APIs remain the right choice for workloads where capability, speed-to-market, or operational simplicity outweigh cost and privacy concerns.
Frontier capability requirements. For tasks requiring the most capable models available (complex reasoning, creative generation, multi-step planning), managed APIs from leading AI providers still lead open-weight alternatives.
Low or unpredictable volume. At under 1 million tokens per day, the fixed costs of self-hostinginfrastructure exceed API costs. Managed APIs are pure variable cost with no minimum commitment.
Rapid prototyping. API calls work immediately with no infrastructure setup. Validate the use case first, then optimize hosting strategy for production.
Who This Is For
Model hosting strategy is for organizations evaluating where their AI workloads should run. Whether you are currently using managed APIs and considering self-hosting for cost savings, or planning new AI initiatives and need to decide upfront, we provide the analysis that supports a data-driven decision.
Contact us at ben@oakenai.tech