Key facts
| Pricing models | Per-token, per-request, per-seat, flat monthly or committed enterprise |
| Cost drivers | Model tier, input/output mix, context length, retries and caching |
| Flat-rate option | Plugsky self-serve plans are flat monthly with unlimited fair-use usage and no per-token billing |
| Free tier | Free plan with 2 free AI models (plugsky-micro, plugsky-lite) and no card |
| Trial | 14-day full-access trial for measuring real workloads |
| Model choice | 30+ models from small and fast to frontier reasoning |
| Hidden costs | Retries, re-embedding, evaluation runs and egress are commonly missed |
| Product status | Chat, streaming, JSON mode, function calling, embeddings and RAG are live |
TL;DR
- AI API cost is a function of your workload, not a single sticker price.
- Model tier and output tokens dominate most bills.
- Calculate with measured tokens, then compare against a flat monthly plan.
- Free tiers are for prototyping; production needs a plan sized to peak usage.
- Self-serve Plugsky plans have no per-token charges or overage fees.
How it works, step by step
- List the workloads you will run: chat, RAG, classification, agents, embeddings.
- For each, measure average input and output tokens per request.
- Estimate monthly request volume and multiply to get monthly tokens.
- Apply current rates from the live pricing page to the input and output totals.
- Add retry, evaluation and re-indexing overhead based on observed data.
- Compare the total with flat plans that cover the same usage level.
- Re-run the calculation after any model or prompt change.
Original data
Try it yourself
Open the AI model pricing calculator →
What actually sets the bill
Four variables explain most of the variance. Model tier: small models handle routing, classification and formatting at a fraction of frontier cost. Token mix: output tokens are usually priced higher than input, so long generations cost more than long prompts. Context: RAG and multi-turn chat resend text on every call, multiplying input tokens. Waste: retries, failed calls and duplicate embedding jobs all reach the meter.
Calculate your own monthly cost
Skip vendor averages and use your numbers:
- Average tokens per request x requests per month = monthly tokens per workload.
- Split into input and output, then apply the current rates from the live pricing page.
- Add measured retry and evaluation overhead — typically a percentage you can log.
- Sum the workloads to get your monthly variable cost.
- Divide by active users or requests to get cost per unit.
The pricing calculator does the arithmetic interactively. If the total is steady month over month, a flat plan with unlimited fair use removes the per-token line entirely.
Why 2026 pricing is not one number
The market has split into three shapes. Per-token API pricing remains the default for granular, variable usage. Flat-rate subscriptions have grown because teams want predictable budgets and no overage surprises. Enterprise agreements add committed capacity, residency and support on top of either.
That is why quoting a single figure is misleading: the same application can cost very differently depending on whether it streams short answers to thousands of users or runs long agent loops for a handful. The unit economics of your product, not the market average, decide which shape fits.
Cutting the number without hurting quality
- Route easy requests to small models and reserve frontier calls for hard ones.
- Trim conversation history and retrieved context to what the answer needs.
- Cap completion length and use structured output where possible.
- Cache stable prompt prefixes and reuse embeddings instead of recomputing them.
- Batch offline work and schedule re-indexing outside peak hours.
Each of these lowers cost per task on metered plans and improves latency on flat plans.
Honest comparison
| Pricing shape | Plugsky flat-rate | Per-token API | Enterprise committed |
|---|---|---|---|
| Monthly cost | Fixed plan price | Varies with tokens and retries | Commitment plus overage |
| Entry point | Free plan with 2 models, no card | Pay-as-you-go from the first call | Annual contract |
| Budget risk | Low on self-serve plans | Moves with traffic and prompts | True-ups above commitment |
| Model access | 30+ models under one plan | Priced per model | Negotiated catalogue |
| Best fit | Steady production workloads | Low or spiky usage | Regulated, high-volume buyers |
| Where to see prices | Live pricing page | Provider pricing pages | Written schedule |
Frequently asked questions
What is the average cost of an AI API?
Averages are misleading because usage profiles differ so much. The useful figure is cost per task: measure tokens per request in your application, apply current published rates, and multiply by your monthly volume.
How much does Plugsky cost?
Plugsky uses flat monthly plans with unlimited fair-use usage on self-serve, and prices change over time, so check the live pricing page for current plans. The free plan includes two models with no card.
Is there a free way to start?
Yes. The free plan includes plugsky-micro and plugsky-lite with two API keys and no credit card. A 14-day full-access trial lets you measure production-like workloads before choosing a paid plan.
Why do output tokens cost more than input tokens?
Generating text is sequential and compute-heavy, while prompts are processed in parallel. Providers reflect that difference in their rates, which is why uncapped completions inflate costs.
What hidden costs should I budget for?
Retries that reach the model, evaluation runs, re-embedding unchanged documents and egress. Log them explicitly so your forecast includes the work your application actually does.
Can I switch pricing models later?
Yes. Plugsky keeps an OpenAI-compatible API, so moving between the free plan, a flat self-serve plan and enterprise terms does not require code changes.
Does a flat plan get more expensive if usage grows?
No. Cost stays fixed under fair use while rate limits govern throughput. If sustained growth needs more headroom, enterprise plans offer committed capacity and negotiated limits.
How do I compare providers fairly?
Compare cost per task for the same prompts and quality bar, not cost per million tokens on different models. Include retries, latency and the engineering time each integration requires.