🧮 New · AI Spend Intelligence · Free

TokenObserve — AI Token & Cost Analyzer

Count tokens for any text — including Arabic — compare what the latest frontier models actually charge, and project your monthly AI spend before the invoice does. Runs entirely in your browser. Nothing is uploaded.

Estimate tokens for any text

Paste a prompt, a document, or model output. TokenObserve estimates the token count using script-aware heuristics — English, code, Arabic, and CJK all tokenize differently. Your text never leaves this page.

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Characters
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Words
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Estimated tokens
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Likely range (±15%)
⚠ Arabic token tax detected. of this text is Arabic script. Most tokenizers split Arabic into roughly 2–3× more tokens per word than English — so Arabic prompts and RAG content cost proportionally more per request. If you serve bilingual AR/EN experiences in MENA, budget for it: the same content in Arabic can cost 2× or more.
Estimates use character-class heuristics calibrated against modern tokenizers (o200k, Claude, Gemini). Exact counts vary by model — use your provider's tokenizer API for billing-grade numbers. This tool is for planning, not invoicing.
If sent as input toEst. cost (this text)× 1,000 requests× 100k requests

Frontier model pricing — side by side

USD per million tokens. Defaults last reviewed — provider pricing changes often, so every field is editable: correct any number and the whole tool (counter costs + projector) updates instantly. Verify against provider pages before committing budget.

ModelInput $/MOutput $/MCached input $/MNotes
Cached input = price for prompt-cache hits (system prompts, RAG context re-sent every call). Batch APIs typically run at ~50% of these rates with delayed results. Long-context surcharges above ~200k tokens are not modeled here.

Project your monthly AI spend

Model your real workload: how many requests, how big the prompts, how much output. The projector prices every model in the table above and flags the cheapest — and shows what prompt caching would save you.

API calls / agent steps per day
Prompt + system + RAG context
Model response length
Share of input served from cache
Model$ / request$ / day$ / monthWith caching
Month = 30 days. "With caching" applies the cached-input rate to the cache-hit share of input tokens. Agent workloads multiply fast: a 20-step agent loop is 20 requests — model it that way.

Guardrails your CFO will ask about

AI spend is the fastest-growing line on enterprise cloud invoices, and public LLM providers have no incentive to cap your usage. These are the controls that work.

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Hard quotas at the gateway
Set daily token budgets per team, tenant, or microservice at the API-gateway layer — alert at 80%, block at 100%. A looping agent bug can burn thousands of dollars in minutes without one.
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Prompt caching & batching
Cached input runs at ~10% of normal price on most providers; batch APIs at ~50%. System prompts and RAG context re-sent on every call are the first thing to cache.
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Model routing
Most requests don't need a frontier model. Route classification, extraction, and short replies to small models and reserve reasoning models for what earns the premium — routing alone commonly cuts spend 40–70%.
Running AI agents in production and can't see what they call — or what it costs? AgentObserve — AI agent dependency & spend discovery — is on our roadmap. Enterprise AI spend audits available now for UAE / GCC teams.
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