This service finds personal data in text — names, addresses, telephone numbers, national identifiers, account numbers, dates of birth, medical details — and removes or masks it, so that records can be shared, analysed or used for training without exposing the people in them.
PII stands for personally identifiable information. Detection is only half the task: what happens next matters as much. A value can be blacked out, replaced by a label, replaced by a realistic but fictitious value, or replaced consistently by the same pseudonym everywhere it appears — the last being what allows records to stay linkable after redaction. Because a missed identifier is a reportable incident, these pipelines are tuned to over-flag and are measured on what they miss, not on their average accuracy.
Models in this group take text or whole documents as input: plain text, PDFs, scanned pages and office files. Each specification table gives three hardware tiers — Minimum, the smallest setup on which the model runs correctly; Medium, the usual production configuration; and High, a configuration sized for peak volume. The rate is the number of sample inputs processed per hour on that hardware. Use these rates for initial sizing. Before production, benchmark your own data to validate accuracy, latency, throughput and cost. The sample input here is one A4 page of about 500 words with all personal data found and masked.
Presidio
Vendor: Microsoft
What it does: a complete detection and masking framework: it combines pattern rules for structured identifiers such as card and insurance numbers with model-based detection for names and addresses, and applies your chosen masking. The usual backbone of a redaction pipeline.
Requirement
Minimum
Medium
High
GPU type
No GPU required
RTX 4090
A100 80 GB
VRAM
—
24 GB
80 GB
vCPUs
4
8
24
RAM
8 GB
32 GB
64 GB
Server
CPU instance, 4 vCPU
1× RTX 4090 24 GB
1× A100 SXM 80 GB
Rate (pages/hour)
≈ 5,000
≈ 20,000
≈ 60,000
GLiNER
Vendor: Urchade Zaratiana and contributors
What it does: finds any identifier type you name in plain words — "employee number", "patient identifier" — with no training data, which covers the identifiers specific to your business that no standard model knows.
Requirement
Minimum
Medium
High
GPU type
RTX 3060
RTX 4090
A100 80 GB
VRAM
6 GB
24 GB
80 GB
vCPUs
4
8
24
RAM
16 GB
32 GB
64 GB
Server
1× RTX 3060 12 GB
1× RTX 4090 24 GB
1× A100 SXM 80 GB
Rate (pages/hour)
≈ 3,000
≈ 12,000
≈ 34,000
DeBERTa v3
Vendor: Microsoft
What it does: fitted to your own data from labelled examples, which gives the lowest miss rate on your document types. The right answer once a labelled sample exists.
Requirement
Minimum
Medium
High
GPU type
RTX 3060
RTX 4090
A100 80 GB
VRAM
6 GB
24 GB
80 GB
vCPUs
4
8
24
RAM
16 GB
32 GB
64 GB
Server
1× RTX 3060 12 GB
1× RTX 4090 24 GB
1× A100 SXM 80 GB
Rate (pages/hour)
≈ 8,000
≈ 30,000
≈ 90,000
XLM-RoBERTa
Vendor: Meta
What it does: finds personal data across a hundred languages with one model, so an obligation is met uniformly rather than only in English.
Requirement
Minimum
Medium
High
GPU type
RTX 3060
RTX 4090
A100 80 GB
VRAM
8 GB
24 GB
80 GB
vCPUs
6
12
24
RAM
16 GB
32 GB
64 GB
Server
1× RTX 3060 12 GB
1× RTX 4090 24 GB
1× A100 SXM 80 GB
Rate (pages/hour)
≈ 6,000
≈ 24,000
≈ 70,000
spaCy transformer pipelines
Vendor: Explosion
What it does: labels the standard entity types in a fast production pipeline, used as a broad first pass that a stricter model then reviews.
Requirement
Minimum
Medium
High
GPU type
RTX 3060
RTX 4090
A100 80 GB
VRAM
6 GB
24 GB
80 GB
vCPUs
4
8
24
RAM
16 GB
32 GB
64 GB
Server
1× RTX 3060 12 GB
1× RTX 4090 24 GB
1× A100 SXM 80 GB
Rate (pages/hour)
≈ 12,000
≈ 45,000
≈ 130,000
Llama 3.1 8B
Vendor: Meta
What it does: catches personal data that only context reveals — a person identified by their role and location rather than by name — which pattern and small-model detection both miss.
Requirement
Minimum
Medium
High
GPU type
RTX 3090
RTX 4090
H100 80 GB
VRAM
16 GB
24 GB
80 GB
vCPUs
8
12
24
RAM
32 GB
48 GB
128 GB
Server
1× RTX 3090 24 GB
1× RTX 4090 24 GB
1× H100 SXM 80 GB
Rate (pages/hour)
≈ 900
≈ 2,800
≈ 9,000
Choosing between them
What matters is your definition of personal data, the jurisdictions involved, and what the redacted text is used for. Our consultants review your data and obligations, then recommend the detection models, the masking strategy, and a measured miss rate you can put in front of an auditor.
At the start of a project we may run a short proof of concept on a sample of your own data, measuring the accuracy and the throughput the model actually achieves on your material. That replaces the estimates on this page with real figures, so the cost and the schedule for the full engagement are known before it is committed.
Share a representative sample, expected volume, latency target and deployment location for PII detection and redaction. We benchmark the shortlisted models, recommend the lowest-cost GPU configuration that meets the target, and scale it from pilot capacity to a dedicated production cluster.