Sanitext vs Azure AI Language PII Detection

What is the difference between Sanitext and Azure AI Language PII?

Both tools detect and redact personally identifiable information (PII) in text. Azure AI Language PII is one feature inside Microsoft's Azure AI Language service. You need an Azure subscription, a Language resource, and the Azure SDK or REST endpoint to use it. It bills per 1,000 text records, where one record is 1,000 characters, and it rounds up per document.

Sanitext is a standalone PII API. You sign up, pay through Stripe, get an API key, and call POST /v1/detect or POST /v1/redact with a curl command. There is no cloud account to provision. Pricing is a flat monthly plan with a fixed character allowance, plus simple overage if you go over.

The core technical difference is the model and where it runs. Sanitext owns its open-weights model (Apache-2.0, from the OpenAI privacy-filter family) and runs it on its own Cloudflare infrastructure. That means zero per-token AI cost and no third-party LLM ever sees your text. Sanitext logs counts and timings only, never raw text.

How do Sanitext and Azure AI Language PII compare on price and features?

The table below compares the two on the points that matter most when you are switching: pricing model, language coverage, redaction output, setup, data residency, and the free tier.

Where does Sanitext win?

Sanitext wins on cost predictability and speed of setup. Azure charges roughly $1.00 per 1M characters at the entry tier, dropping toward $0.30 at high volume, and it rounds up per document, so many small documents cost more than the raw character count suggests. Sanitext uses flat monthly plans (Hobby $29 for 12M chars, Pro $149 for 120M, Scale $499 for 450M) with overage at $0.35 to $0.60 per 1M chars, up to about 2x cheaper than the hyperscaler entry rate.

No Azure resource is required. You do not need a subscription, a resource group, or the Azure SDK. A developer can go from sign-up to a working redaction call in about 60 seconds with a single curl command.

Privacy posture is stronger by design. Sanitext runs its own model on its own infra, so your text is never sent to a third-party LLM, and no raw text is retained. EU data residency and a DPA are available on Enterprise.

• —Flat, predictable pricing with no per-document round-ups
• —Self-serve in 60 seconds, no Azure account or SDK
• —Zero per-token AI cost because we own and host the model
• —No raw-text retention, only counts and timings logged
• —Overage up to about 2x cheaper than the Azure entry tier

When is Azure AI Language PII the better choice?

Azure is the honest better pick in several cases. If your stack already lives in Azure and you bill through one Microsoft enterprise agreement, keeping PII detection inside Azure AI Language means one vendor, one invoice, and one security review. That consolidation has real value.

Azure offers an on-premises container option. If your compliance team requires PII detection to run fully inside your own network with no outbound calls at all, Azure's container deployment covers that today. Sanitext is API-first; self-hosted deployment is handled case by case at the Enterprise level.

Azure also has very broad language coverage and a large free tier of 5,000 records per month. At extreme volume its negotiated rate can fall toward $0.30 per 1M characters, which can beat flat plans if you process hundreds of millions of characters and have already committed to Azure spend.

One honest caveat that applies to both tools: PII detection is a redaction and data-minimization aid, not an anonymization or compliance guarantee. Neither product makes you automatically GDPR or HIPAA compliant. Always review output for your own risk tolerance.

How do you switch from Azure AI Language PII to Sanitext?

Migration is mostly a request and response swap. Both APIs take text and return labeled entity spans, so the mental model is the same.

Step 1: Sign up at sanitext.app, pay through Stripe, and copy your API key. The free tier gives you a one-time 300K characters with no card, which is enough to test against real samples.

Step 2: Replace your Azure endpoint call with a POST to /v1/detect (to get spans with label, byte offsets, score, and text) or /v1/redact (to get text with spans masked as [LABEL]). Map Azure categories to Sanitext labels: FIRSTNAME, LASTNAME, EMAIL, PHONE, ADDRESS, DATE, URL, ACCOUNT, SECRET, and PREFIX.

Step 3: Run both APIs on a sample set, diff the spans, and tune which labels you redact. Then cut over. There is no Azure resource to decommission on the Sanitext side, so rollback is just pointing back at the old endpoint.

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