# LLM Token Cost Calculator: Model Your Real Monthly AI Spend

> Source: https://netray.co/tools/llm-token-cost-calculator
> Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems.
> Section: Free Tools

## Short answer

Turn request volume, prompt length, and per-million token pricing into a defensible monthly and annual LLM budget, including the effect of prompt caching.

## Category

on-prem-ai

## Tool

**Kind:** calculator

**Inputs:**

#### Monthly requests to the model

**Key:** monthly_requests

**Type:** number

**Default:** 200000

**Min:** 100

**Max:** 100000000

**Step:** 1000

**Unit:** requests

**Help:** Every individual model call, including agent sub-steps and retries, not just end-user questions.

#### Average input tokens per request

**Key:** avg_input_tokens

**Type:** number

**Default:** 2500

**Min:** 50

**Max:** 200000

**Step:** 50

**Unit:** tokens

**Help:** System prompt plus retrieved context plus conversation history. RAG apps typically land between 2,000 and 8,000.

#### Average output tokens per request

**Key:** avg_output_tokens

**Type:** number

**Default:** 500

**Min:** 10

**Max:** 32000

**Step:** 10

**Unit:** tokens

**Help:** Generated response length. Roughly 750 words equals about 1,000 tokens.

#### Input price per 1M tokens

**Key:** input_price_per_1m

**Type:** number

**Default:** 3

**Min:** 0.01

**Max:** 200

**Step:** 0.05

**Unit:** $

**Help:** Mid-tier frontier models run $0.50-$5.00 per million input tokens; small models run under $0.30.

#### Output price per 1M tokens

**Key:** output_price_per_1m

**Type:** number

**Default:** 15

**Min:** 0.01

**Max:** 500

**Step:** 0.25

**Unit:** $

**Help:** Output is usually 3-5x the input price. This is where runaway agent loops get expensive.

#### Prompt cache hit rate

**Key:** cache_hit_pct

**Type:** slider

**Default:** 30

**Min:** 0

**Max:** 95

**Step:** 5

**Unit:** %

**Help:** Share of input tokens served from a cached prefix. Cached tokens typically bill at about 10% of list price.

#### Expected 12-month volume growth

**Key:** growth_multiplier

**Type:** select

**Default:** 1.5

**Unit:** x

**Help:** Applied to the annual projection. Internal AI pilots that succeed almost never stay flat.

**Options:**

##### Flat - no growth expected (1.0x)

**Value:** 1

##### Moderate rollout (1.5x)

**Value:** 1.5

##### Fast adoption (2.0x)

**Value:** 2

##### Company-wide expansion (3.0x)

**Value:** 3

**Outputs:**

#### Monthly input tokens (millions)

**Key:** monthly_input_tokens_m

**Expr:** monthly_requests * avg_input_tokens / 1000000

**Format:** number

**Description:** Total prompt tokens billed per month before cache discounts.

#### Monthly output tokens (millions)

**Key:** monthly_output_tokens_m

**Expr:** monthly_requests * avg_output_tokens / 1000000

**Format:** number

**Description:** Total generated tokens billed per month. Never discounted by caching.

#### Estimated monthly cost

**Key:** monthly_cost

**Expr:** monthly_input_tokens_m * input_price_per_1m * (1 - cache_hit_pct / 100 * 0.9) + monthly_output_tokens_m * output_price_per_1m

**Format:** currency

**Description:** Blended input and output spend with the cache discount applied to the cached share.

**Primary:** true

#### Projected 12-month cost

**Key:** annual_cost_projected

**Expr:** monthly_cost * 12 * growth_multiplier

**Format:** currency

**Description:** Twelve months at current usage, scaled by your expected growth multiplier.

**Primary:** true

#### Average cost per request

**Key:** cost_per_request

**Expr:** monthly_cost / max(monthly_requests, 1)

**Format:** currency

**Description:** The unit economic that matters. Compare it against the value each call creates.

**Disclaimer:** Estimates only. Vendor list prices, cache semantics, and batch discounts change frequently; validate against a real 30-day usage export before committing budget.

## Content

**Intro:** This free LLM token cost calculator estimates what a production language model workload will cost per month and per year, and it is built for AI platform leads, ERP application owners, and IT directors who need a budget number they can defend. Enter request volume, average prompt and response length, per-million token prices, and your prompt cache hit rate, and the tool returns monthly spend, a growth-adjusted annual projection, and cost per request. Most teams underestimate spend by two to four times because they count user questions rather than model calls. Agent frameworks, retries, and retrieval reranking multiply the true call count.

**Sections:**

#### How the token cost math works

**Body:** Providers bill input and output tokens separately, and output almost always costs three to five times more. The calculator converts your request volume into millions of tokens, prices each stream at its own rate, then applies the cache discount only to the cached share of input tokens. With the defaults, 200,000 requests at 2,500 input and 500 output tokens produce 500 million input tokens and 100 million output tokens per month. At $3 and $15 per million with a 30% cache hit rate, that is roughly $1,095 of input spend and $1,500 of output spend, or about $2,595 per month. The annual projection then applies your growth multiplier, because a successful pilot rarely holds volume flat for twelve months.

#### Pricing benchmarks used in this tool

**Body:** The defaults reflect mid-2026 list pricing for mid-tier frontier models and typical enterprise RAG prompt shapes. Treat them as anchors rather than facts, and replace them with your provider's current rate card before presenting a number to finance. Pricing in this market has fallen steadily for several years, so a model that was uneconomic eighteen months ago may be viable today. Self-hosted open-weight models change the equation entirely: there is no per-token bill, but you take on GPU capital, power, cooling, and platform engineering instead. The figures below are the reference points we use most often when sizing enterprise workloads for manufacturers.

**Bullets:**

- Frontier mid-tier models generally sit at $1-$5 per million input tokens and $5-$25 per million output tokens.
- Small and distilled models often price below $0.30 input and $1.20 output, a 10x reduction for classification and extraction work.
- Prompt caching bills reused prefixes at roughly 10% of list price, which is why a stable system prompt is a cost control.
- Batch or asynchronous processing tiers commonly discount 40-50% for work that tolerates a multi-hour turnaround.

#### How to interpret your result

**Body:** Read cost per request before total spend. If a call costs $0.013 and saves a planner ten minutes of ERP research, the economics are obvious. If it costs $0.40 and answers a question a report already answers, no volume discount will rescue it. Then look at the split between input and output spend. Input-heavy profiles usually mean the retrieval layer is stuffing too many chunks into context and can be fixed with better reranking. Output-heavy profiles usually mean prompts are not constraining response length. Finally, stress the growth multiplier. Budgets fail when a 40-user pilot becomes a 900-user rollout and nobody re-ran the arithmetic.

#### How Netray helps you control AI spend

**Body:** Netray builds AI systems for manufacturers running Infor SyteLine, LN, and M3 where per-token cost, data residency, and ITAR constraints all have to be satisfied at once. We start by instrumenting real token usage per workflow, then route each workload to the cheapest model that passes your evaluation bar - often a small self-hosted model for extraction and a frontier model only for genuinely hard reasoning. For customers where data cannot leave the building, we deploy open-weight models on-prem and convert a variable API bill into a fixed, auditable infrastructure line. Engagements typically begin with a two-week cost and architecture review against your actual traffic.

**Cta:** Get a personalized LLM cost model and a routing plan that cuts spend without lowering answer quality.

**Faq:**

#### Why is my actual bill higher than this estimate?

**Answer:** Almost always because the real call count exceeds the user-visible request count. A single agent turn may fire a query rewrite, three retrieval reranks, a tool call, and a final synthesis, so one question becomes six billed calls. Retries after timeouts, evaluation runs, and background summarization add more. Pull a 30-day usage export from your provider and divide total calls by user sessions to find your true multiplier, then re-run this tool.

#### How much does prompt caching actually save?

**Answer:** It depends entirely on prefix stability. If your system prompt, tool definitions, and few-shot examples stay byte-identical across calls, hit rates above 70% are achievable and cached tokens bill at roughly a tenth of list price. That can cut total input spend by more than half. But any dynamic value near the front of the prompt, such as a timestamp or user name, invalidates the cache for every call. Put volatile content at the end.

#### At what volume does self-hosting beat API pricing?

**Answer:** For most enterprises the crossover sits somewhere between $8,000 and $20,000 per month of sustained API spend, assuming you can keep GPUs meaningfully utilized. Below that, the capital, power, and engineering overhead of running your own cluster rarely pays back. Regulated workloads are the exception: if ITAR, CMMC, or contractual data residency rules out sending data to a public API, on-prem becomes a compliance requirement rather than a cost decision.

## Search terms

LLM token cost calculator, LLM API pricing calculator, AI token cost estimator, cost per 1M tokens calculator, enterprise LLM budget planning, prompt caching cost savings, GPT and Claude cost comparison

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