Sanitext vs AWS Comprehend PII: a Simpler Alternative

What is the difference between Sanitext and AWS Comprehend PII?

Both tools find personal data in text. AWS Comprehend PII is one feature inside Amazon's broader natural language service. Its DetectPiiEntities call returns a list of entity spans with byte offsets and scores. You then write code to mask those spans yourself.

Sanitext is a focused PII detection and redaction API. It returns the same kind of spans through POST /v1/detect, but it also has POST /v1/redact, which hands back the text with each span already masked as [LABEL]. You skip the masking step.

The other big gap is reach and setup. Comprehend supports English and Spanish only and lives behind an AWS account, IAM roles, and the AWS SDK. Sanitext detects PII in 30+ languages and works with a plain curl command and a single API key.

Sanitext vs AWS Comprehend PII: feature and price comparison

The table below lines up the points that matter most when you pick a PII tool: how you pay, what you get back, how fast you start, and where your data lives.

When AWS Comprehend PII is the better choice

We will be straight with you. AWS Comprehend PII is the safer pick in a few real cases.

If your whole stack already runs on AWS, Comprehend plugs into IAM, CloudTrail, S3, and KMS with no new vendor. Your security and billing teams already trust it. That alignment can outweigh price and language gaps.

If you need only English and Spanish, and you have engineers who are happy to build the masking layer, Comprehend does the detection job well. It also covers other Comprehend features (sentiment, entities, topic modeling) under one roof, so if you use those too, staying put makes sense.

Finally, if a strict procurement process has already approved AWS and adding any new sub-processor is slow or blocked, Comprehend wins on paperwork alone.

• —You are already deep in the AWS ecosystem (IAM, S3, KMS, CloudTrail).
• —English and Spanish are the only languages you need.
• —You have engineers ready to build the masking step from offsets.
• —You also use other Comprehend features like sentiment or entity analysis.
• —Vendor approval for AWS is done and adding a new sub-processor is hard.

Where Sanitext wins

Sanitext is built for one job, so it removes the friction Comprehend leaves behind.

Pricing is flat and transparent. Plans run from Free (one-time 300K characters, no card, never resets) to Hobby $29/mo (12M chars), Pro $149/mo (120M), and Scale $499/mo (450M). Overage is $0.35 to $0.60 per million characters, up to about 2x cheaper than the $1.00 per million that Comprehend and Azure charge. Because we own the open-weights model (Apache-2.0, OpenAI privacy-filter family) and run it on our own Cloudflare infrastructure, there is zero per-token AI cost and your text never goes to a third-party LLM.

Redaction is built in. Call /v1/redact and get text back with PII masked as [EMAIL], [PHONE], [FIRSTNAME], and so on. No glue code.

Coverage is wide. Sanitext detects FIRSTNAME, LASTNAME, EMAIL, PHONE, ADDRESS, DATE, URL, ACCOUNT, SECRET (API keys, tokens), and PREFIX like Dr. across 30+ languages, not just English and Spanish.

Setup is fast. Sign up, pay through Stripe, get an API key, and run a curl request in about 60 seconds. No IAM, no SDK. Enterprise adds EU data residency and a DPA. We do not retain raw text; we log only counts and timings.

• —Flat pricing with overage up to ~2x cheaper than Comprehend's $1.00 per 1M chars.
• —/v1/redact returns masked text, so no masking code to write.
• —30+ languages versus English and Spanish only.
• —60-second self-serve signup, no AWS account or IAM needed.
• —No raw-text retention; EU residency and DPA available on Enterprise.

How to switch from AWS Comprehend PII to Sanitext

Migration is mostly a swap of one API call. Here is the path most teams take.

First, sign up at sanitext.app and grab your API key from Stripe checkout. The free 300K characters let you test before you pay.

Second, replace your DetectPiiEntities call. If you only needed offsets, point your code at POST /v1/detect, which returns spans with label, byte offsets, score, and text in the same shape you already parse. If you were building masked output by hand, switch to POST /v1/redact and delete your masking code, since Sanitext returns the [LABEL] text directly.

Third, map the labels. Comprehend and Sanitext both use clear labels like EMAIL and PHONE, so most mappings are one to one. Check edge cases like ADDRESS, ACCOUNT, and SECRET against your test set.

Fourth, run both tools side by side on a sample of real text and compare spans. Adjust any downstream filters, then cut over. Most teams finish in an afternoon.

One honest note: Sanitext is a redaction and data-minimization aid, not an anonymization or compliance guarantee. Always review output for your own risk level, the same as you would with Comprehend.

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