Token API
Fastest procurement and integration path for many products, but cost scales with usage, context size and output length.
Pricing
LLM Pricing Guide
Compare LLM pricing models for token APIs, dedicated inference, GPU cloud and self-hosted models.
Executive Summary
LLM pricing starts with tokens but rarely ends there. A production application also pays for context growth, output verbosity, retries, tool calls, embeddings, storage, evaluations, logging, observability, safety filters, latency targets and engineering time. A cheap model can become expensive if it needs repeated calls or extensive post-processing. A more expensive model can be economical if it solves the task with fewer tokens and less infrastructure work.
Teams should compare LLM cost by workflow, not only by provider. Customer support, coding assistance, extraction, summarization, search augmentation and agentic workflows all have different token profiles. Pricing decisions should be based on measured prompts, expected traffic, quality thresholds and the cost of operating the surrounding system.
Dedicated endpoint
Useful when traffic is predictable and the team needs clearer latency, isolation or model-serving controls.
GPU cloud
Gives more control over models and serving stack, but requires infrastructure and operations work.
Self-hosted
Can improve control and cost predictability for stable workloads, while shifting responsibility to the operating team.
LLM Pricing Comparison
| Approach | Best for | Cost risk | Optimization path |
|---|---|---|---|
| Managed frontier API | High-quality product features and fast launch | Token growth and vendor dependency | Prompt trimming, caching and task routing |
| Open-model API | Cost-sensitive workloads and model flexibility | Quality variance and serving limits | Evaluation harnesses and model selection |
| Dedicated inference | Predictable production traffic | Underutilized reserved capacity | Right-size endpoints and track utilization |
| Self-hosted model | Stable high-volume or private workloads | Operations complexity | Serving optimization and infrastructure automation |
Decision Framework
- Measure real prompts and outputs before forecasting spend.
- Separate high-value reasoning tasks from simple classification or extraction tasks.
- Decide which requests require the strongest model and which can use smaller models.
- Include evaluation, staging and retry traffic in cost projections.
- Compare API, dedicated endpoint and self-hosted options after usage stabilizes.
Practical Recommendations
- Log tokens by feature, tenant and model to find cost drivers.
- Use retrieval carefully; long context is not free capacity.
- Cache stable answers, extracted context and expensive intermediate steps.
- Keep model evaluation separate from production traffic metrics.
Related Guides
FAQ
Is token pricing enough for cost planning?
No. Context length, output volume, retries, caching, latency targets, observability and fallback routing can materially affect total cost.
When can self-hosting be cheaper?
Self-hosting can be cheaper for stable high-volume workloads when the team can operate infrastructure efficiently and the selected model meets quality requirements.
Why do output tokens often matter more?
Output volume can be harder to control than input volume, and some providers price input and output tokens differently. Teams should measure both.
Should teams use one model for every request?
Usually not. Routing simple tasks to smaller or cheaper models can reduce cost when quality and safety requirements allow.