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Performance & cost
This page session · since page load
- Typical delivery time
- —
- Live input tokens
- 0
- Estimated inference cost · USD
- $0.00
How these numbers work · 0 successful requests
- Live model compute
- —
- Successful requests
- 0
Average per measured successful live request.
Run an example for free to see its measurements.
Delivery time is the median browser time for successful requests, including queue, network, and retries. Cached delivery speed is not inference speed. Model compute can exceed browser time when one request processes many inputs.
Tokens come from response headers. Inference costs use $0.04 per million input tokens, before plan credits. The estimate includes live and recorded tokens at the same rate; cached examples are not charged again. Missing usage is unavailable or a partial total (≥). Demo resets keep these totals; reload to start a new session.
Detect details in the stock transcript free, review redactions, then use your key for your own text.
Build with the SDKTypeScript · Python · dm1
Use these API building blocks in your own app. Each sample is one request from this demo’s supplied inputs. Your code owns the surrounding workflow, validation and actions.
Copied samples call the live API with MS_API_KEY and use your quota. Keep the key on your server. The free demo cache is only for this site.
npm install @cloudraker/milliseconds
export MS_API_KEY="your-api-key"import { DecisionMachine } from "@cloudraker/milliseconds";
// Run on your server; reads MS_API_KEY from the environment.
const dm = new DecisionMachine();
const { result, usage } = await dm.entities(
"Customer: Hi, I’m Maya Chen. The weekly export stops at 80%.\n\nAgent: I can send a reproduction to our integration vendor. Which account is affected?\n\nCustomer: My email is maya.chen@example.com and my account is NW-2048. You can call +1 202 555 0148.\n\nAgent: Thanks. We can reproduce this when the date filter is set to last month. The workaround is to export one week at a time.",
{
"person": "A person’s full name",
"email": "An email address",
"phone": "A telephone number",
"account_id": "A customer account, order, or ticket identifier",
"address": "A street or postal address"
}
).withUsage();
console.log(result);
console.log({ inputTokens: usage.inputTokens, modelMs: usage.inferenceMs });pip install cloudraker-milliseconds
export MS_API_KEY="your-api-key"from milliseconds import DecisionMachine
# Reads MS_API_KEY from the environment.
dm = DecisionMachine()
result = dm.entities(
"Customer: Hi, I’m Maya Chen. The weekly export stops at 80%.\n\nAgent: I can send a reproduction to our integration vendor. Which account is affected?\n\nCustomer: My email is maya.chen@example.com and my account is NW-2048. You can call +1 202 555 0148.\n\nAgent: Thanks. We can reproduce this when the date filter is set to last month. The workaround is to export one week at a time.",
{
"person": "A person’s full name",
"email": "An email address",
"phone": "A telephone number",
"account_id": "A customer account, order, or ticket identifier",
"address": "A street or postal address"
}
)
print(result)npm install -g @cloudraker/milliseconds
export MS_API_KEY="your-api-key"# Reads MS_API_KEY. JSON on stdin supplies the complete request.
dm1 entities --json --usage <<'DM1_REQUEST'
{
"text": "Customer: Hi, I’m Maya Chen. The weekly export stops at 80%.\n\nAgent: I can send a reproduction to our integration vendor. Which account is affected?\n\nCustomer: My email is maya.chen@example.com and my account is NW-2048. You can call +1 202 555 0148.\n\nAgent: Thanks. We can reproduce this when the date filter is set to last month. The workaround is to export one week at a time.",
"types": {
"person": "A person’s full name",
"email": "An email address",
"phone": "A telephone number",
"account_id": "A customer account, order, or ticket identifier",
"address": "A street or postal address"
}
}
DM1_REQUESTKeep the context. Review the details.
The entities endpoint looks for names, emails, phone numbers, account or order IDs, and street addresses. The demo checks every returned span against the original text before highlighting it. Your selections are replaced with literal [REDACTED] markers; overlapping selections are merged.
Detection is a starting point
Models can miss sensitive details or mark harmless text. Review the entire transcript, add missed text, and remove unnecessary selections before exporting. This demo does not certify that text is anonymous. Live detection sends the transcript to the milliseconds API; sample review and manual redactions run in your browser.