Product · Data Quality Intelligence
Your data is lying to you. DataVault finds out where.
Turn your data into your most reliable asset.
Connect your sources in minutes, read-only — warehouses, operational databases, SaaS, files, documents and streams (Kafka and Pub/Sub, not yet installed in the SaaS). DataVault profiles the data, monitors with zero configuration, reads the complaints in Jira and Slack, proposes the fix in SQL and PySpark as a pull request, and tells your AI agents whether each dataset can be trusted.
See the presentationDataVault
erp_demo · scan #1282/100
Quality score
AI Agent · Recommendation
Merge 214 fuzzy duplicates in customers (0.94 confidence).
CREATE TABLE customers_clean AS SELECT DISTINCT ON (email) …
The problem
Four problems you can't see. Until they cost money.
Manual quality checks don't scale. These four patterns repeat in almost every company with data in more than one place.
Multi-cloud sprawl
Three to five data platforms per company. No unified quality view. Manual checks, platform by platform.
Duplicates and inconsistencies
Inflated metrics, corrupted ML models, wasted storage. Manual fuzzy matching is impractical.
Silent schema drift
New columns, changed types, null spikes break pipelines without warning. Exposed PII discovered far too late.
Degrading ML models
Garbage in, garbage out. Manual root-cause analysis is slow, expensive — and always arrives after the damage.
50%
of knowledge workers' time goes on hunting for data, correcting errors and confirming numbers they don't trust.
Estimate by Thomas C. Redman, Harvard Business Review (2016)
How it works
Discover. Validate. Optimize.
Not passive monitoring. DataVault finds the problems — and proposes the fix.
Discover
Auto-discovery and automatic profiling of every connected table. No column-by-column configuration.
- Per-column metrics: counts, nulls, distincts, min/max/mean/deviation
- Cardinality and anonymizable samples
- History per table: a quality time series, scan by scan
Validate
Rule engine, duplicates and drift running on every scan. Everything converges into a comparable score.
- 6 rule types: not null, uniqueness, pattern, range, freshness, minimum row count
- Exact (hash) and fuzzy (ML) duplicates, in clusters with confidence levels
- Schema and statistics drift against the previous scan
- 0–100 score per dataset, across 5 dimensions: completeness, uniqueness, validity, freshness, consistency
Optimize
The AI Agent analyzes schema, issues and usage patterns — and returns concrete fixes, not vague advice.
- Recommendations with generated SQL, ready to review and apply
- Savings estimate in € and % — storage, compute, hours
- Risk analysis per change: safe or breaking
- Reports per persona: technical, cost-benefit, executive digest
Flow
From zero to first report in three steps.
01
Connect
Read-only access via official vendor methods. Credentials encrypted before they touch disk. Under 15 minutes to the first scan.
02
Analyze
Manual or scheduled scans — daily, weekly or monthly. Profiling, rules, duplicates and drift on every run. Alerts by severity.
03
Optimize
Actionable suggestions with SQL and estimated impact. Reports per persona. You fix, the score climbs — and the history proves it.
Who it's for
One platform, three readings.
Each stakeholder sees what matters to them. Nobody wastes time translating dashboards.
Efficient pipelines, less manual checking
Data Engineer
- Score per table and issue browser with drill-down
- Duplicate clusters: review, merge or ignore
- Schema and statistics drift alerts
- Refactoring reports with generated SQL
What you stop checking by hand: nulls, duplicates, rules and drift, on every scan.
Cost-benefit and operational efficiency
Data / Product Manager
- Score trend, open vs resolved issues
- Estimated hours saved
- Cost of poor quality per dataset
- Recommendation adoption
Target: prioritize with numbers, not opinions
Visible risk, financial efficiency
Executive (CFO/CTO)
- Total estimated financial impact, in euros
- Estimated storage and compute cost per dataset, with the waste in plain sight
- Critical incident digest: severity and exposure
- Recommended action for every incident
Target: visible risk, measurable ROI
By industry
DataVault, speaking your sector's language.
Real proof
We connected to a public source. Zero preparation.
The OpenAQ open archive — air quality measurements from real sensors, in a public S3 bucket — connected anonymously, no account, no credentials. DataVault discovered the files, ran the scan and returned its verdict in half a minute.
The same source ships in the app's public sources gallery — connect it on your free account and see the same result.
Try it with this source30
files discovered as datasets
32 s
full scan duration
30
issues found and explained
What showed up — genuine real-world sensor dirt:
- —Sentinel values (-999) mixed with valid measurements
- —Negative pollutant concentrations — physically impossible
- —Daily files with gaps and inconsistent columns
What it does today
The full loop: detect, explain, fix, prove.
Connect in minutes, read-only by construction
29 source types — warehouses, operational databases, SaaS, files, object storage, streams, documents and chat — always through the vendors' official methods, 22 of them already available in the SaaS. Encrypted credentials, a read-only HTTP client and anti-SSRF guards: there is no code path for DataVault to write to your source.
Start without your own data
“Explore with demo data” spins up an ERP, online shop, Portuguese neobank or anonymized hospital workspace with planted problems and the story behind them. Or connect in one click to verified public sources (Ensembl, OpenAQ, NOAA) — and the first-value checklist walks you to the first scan, in PT, EN or ES.
Deep profiling with semantic types
Quantiles, histograms, correlations and profile alerts computed by pushdown on SQL sources and over the sample elsewhere, with capped samples. Every column is classified automatically — email, tax ID, IBAN, phone, postal code — and format masks show up with nothing to configure.
Huge tables without a huge bill
Incremental profiling per dataset: a re-scan reads only the new time window and merges statistics (HLL for distinct counts), with a periodic full profile.
Streams as datasets
The Kafka and Google Pub/Sub connectors are built, with their drivers not yet installed in the SaaS. Once they are, topics show up in the catalog with a sampled profile, optional Schema Registry and a live panel. Maximum event age, schema conformity, required fields and % of malformed messages are first-class rules.
Documents for RAG, with a gate at the door
Confluence, GCS and S3 treated as a corpus: duplicates, language, PII, garbled text and files nobody has touched in years, with a score and issues like any dataset. The SharePoint and Google Drive connectors are built, with their drivers not yet installed in the SaaS. Before indexing, the pre-ingestion gate (batch API plus LangChain/LlamaIndex middleware) blocks what shouldn't get in.
Automatic monitors, zero configuration
Every table in the connection, up to 500 per connection, gets by default a freshness monitor that learns its cadence, a volume monitor and a schema monitor. Schema changes arrive with a diff, suggested renames, downstream impact and a per-dataset timeline.
Anomalies without rules, seasonality included
Volume, nulls and cardinality compared against history (median+MAD) and against the same weekday, so Monday doesn't look like an incident. The explanation card shows the normal band and the segment that caused the deviation; false-positive feedback mutes the metric.
Duplicates, drift and medallion layers
Exact and fuzzy duplicates grouped into clusters, with per-cluster actions and a constellation view. Null spikes and row-count drops between scans, and Bronze, Silver and Gold layers detected by name — the fix adapts: quarantine in Bronze, cleansing in Silver, a contract in Gold.
Trust score and AI readiness per use case
Each dataset gets an explainable trust score (A–E) with components and blockers, plus a readiness profile for RAG, training, agent or BI with hard blockers — PII in RAG, drift in training. A comparable radar across datasets and suspicious-label detection for training sets.
Rules engine with real-world validation
not-null, uniqueness, pattern, range, freshness and minimum row count checked on every scan. Plus check digits for Portuguese tax IDs and IBANs and reference lists — ISO countries, currencies, PT postal codes — so “this field is valid” isn't a hand-written regex.
Rules that write themselves — no AI needed
The deterministic generator proposes rules with thresholds computed from your profile; when you resolve an issue, DataVault suggests the rule that would have caught it (“never again”); a Jira complaint becomes a rule proposal. Nothing lands until you accept it, and industry packs — e-commerce, finance, SaaS, Portuguese healthcare — apply in one click.
Import the tests you already have
dbt tests, Great Expectations, SodaCL and ODCS come in with one upload, with preview, dedupe and declared skips. Export back dbt tests and model contracts generated from what we observe. Zero migration, zero lock-in.
ODCS contracts the producer actually accepts
An ODCS v3.1 contract generated from what we observe — metrics, dimensions, SLA — and checked against datacontract-cli. Send a link to the source system's owner: they comment and accept section by section without an account, and the accepted version becomes the active contract, numbered with a diff.
CI gate, data-diff and reconciliation
The pass/warn/fail verdict fails the pipeline when the data fails — via script, API or `datavault gate` in the SDK. Deterministic data-diff between dev and prod or source and target, and row-count reconciliation between two datasets from different connectors, with no queries to the source.
Signals from Jira and Slack
DataVault reads the tickets and channels you choose (opt-in, authors anonymized) and turns “that column is always null” into an issue linked to the right dataset — wrong, stale, duplicated, missing. Tribal knowledge becomes a work queue. The Teams connector is built, with its driver not yet installed in the SaaS.
Scan notebooks, jobs and DDL
On Databricks and Snowflake, engineering assets get inspected too: SELECT * in production, jobs with no retry, timeout or alert, orphaned notebooks. Issues aggregated by practice, so you fix the habit and not just the symptom.
Root-cause diagnosis with evidence
One click and the agent correlates profiles, drift, lineage, quoted human signals and orchestrator runs — “dbt job X failed 20 minutes earlier” — to explain the probable cause, with verification steps. Works offline, no AI key required.
Fixes in SQL and PySpark, under approval
Every suggestion ships a safe diagnostic and a breaking fix — ALTER TABLE, CHECK, dedup — in SQL and the PySpark equivalent, per layer. Proposed → accepted → applied → verified: the next scan confirms whether it worked, and you export it all as remediation.sql. We never write to your sources.
Fixes as pull requests
An accepted suggestion opens a PR in GitHub or an MR in GitLab against your repository, and status syncs both ways. The fix enters the review flow your team already uses.
Work queue and incidents
Issues with an owner, due date, snooze and history, on a drag-and-drop kanban board. Upstream and downstream problems group into a single incident, and one click creates the ticket in Jira or Linear.
Alerts, on-call and quiet hours
Slack, Teams, email and webhooks with per-channel minimum severity and a weekly digest on by default. Escalation policies per severity, on-call rotations per domain and per-user quiet hours — the right alert reaches the right person, and not at 3 a.m.
Column-level lineage, dbt included
Foreign keys, view SQL and declared edges give you automatic lineage down to the column. The dbt manifest joins the graph with exposures as consumers and test gaps as suggestions; the same metric with different formulas raises a metric-drift issue, and so does dbt Cloud saying “fresh” when the data says otherwise.
Domains with a declared SLA and measured SLOs
Group datasets into domains with an owning team and their own scorecard. The declared SLA feeds the SLO: freshness and latency attainment over time, per dataset and domain, exportable as CSV — and, if you opt in, a public badge and quality page per dataset.
Real usage and cost next to quality
Query logs from BigQuery, Snowflake and Databricks classify tables as hot, active, cold or dead — 90 days without a query raises a decommissioning notice. Compute cost per dataset and cost spikes sit next to the score: how much of what you pay runs on wrong data.
Ask DataVault
A chat with read-only access to your catalog, the same tools as the MCP server and cost guards: “which datasets contain PII?”, “why did the orders score drop?”. When it proposes an action — create a rule, open an issue — it only runs with your approval.
Reports and dashboards per persona
Engineer, manager and executive reports with SQL, PySpark and charts, exportable as PDF or HTML and shareable via an expiring link for people without an account. Daily, weekly or monthly schedules delivered by email, Slack or Teams; per-persona dashboards, a quality calendar, a catalog treemap and a TV mode for the control room.
Memory that compounds
A README per dataset drafted by AI, edited and approved before publishing. Reports and diagnoses feed a knowledge base with semantic search and a sources↔data↔knowledge graph that prompts and the assistant cite — your feedback (useful / not useful) feeds the golden set.
Fits the stack you already run
An MCP server for agents, a Python SDK with CLI, a Terraform provider, an Airflow provider, API tokens and workspace configuration as YAML. It emits and consumes OpenLineage, publishes verdicts to OpenMetadata and DataHub, and the dbt Cloud webhook triggers a scan when the job finishes.
AI in your own account, your way
Your own Anthropic key, Claude in your AWS (Bedrock) or GCP (Vertex) account, a per-workspace model policy with an “AI off” mode. A router per task type with samples anonymized (tax ID, social security, IBAN, phone) before any budget provider, and a golden set with a fail-closed judge before any model switch.
Privacy and the AI Act
Column-level sensitivity classification with manual curation, a workspace-wide PII map and a per-layer data policy — PII in gold, mandatory owner on confidential — checked on every scan and in CI. Art. 50 label on every AI output and an Art. 10 dossier clause by clause, ready for Annex IV.
Team security, GDPR included
Admin, member and viewer roles, invites, OIDC SSO (Google, Entra ID, Keycloak) and organizations with multiple workspaces and federated catalog search. Exportable admin audit log, encryption key rotation, API rate limiting, and the admin exports or deletes the workspace on their own — no support ticket.
Connectors
Connects to what you already run.
Warehouses, operational databases, files, business applications, documents, streams and chat: 29 source types, 22 already available in the SaaS. Always read-only, always through official methods.
Why it's different
It detects — and proposes the fix.
Enterprise suites fix things with a project and write credentials. Open source is free until you count the engineering hours. DataVault finds the problem and proposes the fix, without writing to your sources — and every claim about the others cites its source.
| Dimension | Enterprise (Collibra, Informatica) | Open-source DIY (Great Expectations, Soda) | Cloud observability (Monte Carlo, Sifflet) | DataVault |
|---|---|---|---|---|
| Proof before you buy | Demo and proof of concept run with the vendor or an integrator[1][2] | Install and try it right away: GX Core and Soda Core are open source[3][4] | Demo or trial requested through a sales form[5] | Self-service free account, no card and no sales call, plus a public proof on open data: 30 files scanned in 32 s |
| Time-to-value | A project with an integrator and implementation services; Collibra DQ is installed in the customer's environment[1][2] | First check in hours; coverage and investigation are ongoing engineering work | Metadata monitors once the warehouse is connected, without querying the tables (Monte Carlo), as SaaS or with a collector in your environment; Metaplane documents exceptions for Postgres, MySQL and SQL Server[6][7][8] | First scan in under 15 minutes; metadata monitors on every dataset in the connection from the first scan, up to 500 per connection |
| Fixing | Cleansing, standardization and MDM that write to your systems (Informatica IDMC) — requires write credentials and a project[2] | Tests fail. Someone investigates and fixes by hand | Detects, alerts and helps investigate; the fix stays with your team[5][7] | Proposes the fix in SQL/PySpark or as a pull request, with € savings and risk — read-only by contract Evidence |
| Fixes where the code lives | Workflows inside the platform[1][2] | You write the PR | An alert linking to the platform; the PR is up to your team[5] | An accepted fix opens a pull request in GitHub or GitLab, syncs its state, and the next scan confirms it worked Evidence |
| Tests you already have | Suite-native rules; no announced import of dbt, Great Expectations or Soda[1][2] | They are your tests — and your maintenance | Monitors in their own YAML or in dbt meta; Monte Carlo generates monitors from dbt tests, singular tests included, and Sifflet opens incidents when a dbt test fails; no announced export back as dbt tests or import of Great Expectations or Soda[9][10][11][12][13] | Imports dbt tests, singular tests included, Great Expectations suites and SodaCL and exports back to dbt, SodaCL and GX; relationships becomes a referential integrity rule; what doesn't convert (macros and Jinja beyond ref, source and config), or wouldn't come back unchanged from the SodaCL and GX export, is reported, never silently dropped |
| Custom checks | Informatica: rule specifications built from graphical elements or IF-THEN-ELSE expressions; Collibra: custom SQL rules with joins and aggregations, hand-written or AI-suggested — pushdown jobs only[14][15][16] | SQL and Python without limits: singular tests and macros (dbt), custom expectations in Python or SQL (Great Expectations), SQL checks (Soda)[17][18][19][20] | Metadata monitors; Monte Carlo accepts custom SQL monitors, with manual or learned thresholds[6][21] | 16 rule types (nulls, uniqueness, patterns, ranges, freshness, volume, check-digit validity for tax IDs and IBANs, reference lists, opt-in accuracy against authoritative sources, streams (Kafka and Pub/Sub, not yet installed in the SaaS), cross-column conditions, custom SQL and referential integrity across datasets), industry packs and suggestions from the profile and from acceptance history; custom SQL is a single read-only SELECT, validated before it runs in the source (SQL sources only); no custom Python checks |
| Work signals and new rules | Stewardship workflows: issues are logged by hand and rules are written by stewards[1] | Complaints arrive as tickets; you write and maintain every test | Open tickets in Jira or ServiceNow and learn monitors; they don't read your teams' complaints[5] | Reads Jira, Confluence and Slack and turns complaints into issues linked to the right dataset; proposes rules from the profile, from the resolved issue and from those complaints — they only land when you accept them. The Teams connector is built, with its driver not yet installed in the SaaS Evidence |
| Memory of what already happened | Glossary and manual stewardship[1] | — | Incident history per table[5] | A knowledge base of reports and diagnoses, semantic search and a sources↔data↔knowledge graph; the assistant and the diagnoses cite it Evidence |
| Governance and stewardship | Catalog and business glossary are the suite's system of record; in Collibra, quality scores sit right on the data marketplace[22] | Out of scope: tests live in your repository and wiring them into the catalog is up to you | The focus is detecting and investigating incidents; formal governance and stewardship aren't the product[5][7] | Business glossary linked to columns, owner and steward per dataset, tags and an exportable data dictionary; publishes verdicts to your OpenMetadata or DataHub and reads owners, domains and tags from it |
| Multi-source view | A broad suite, licensed per module[1][2] | One test repository per source | Strong on warehouses; limited operational SaaS sources[5][7] | Warehouses, operational DBs and SaaS in a single view Evidence |
| Business-process integration (SAP, Salesforce) | Quality inside the process, with write-back: SAP MDG validates master-data changes against business rules before activating them; Informatica cleanses, standardizes and validates with agents and brings data quality into applications (Informatica Headless). Collibra connects to S/4HANA through Edge, for the catalog[23][24][25] | Tests run in your pipeline, on data already written; calling them from SAP or Salesforce is integration you write and maintain | Monte Carlo connects to Salesforce CRM and SAP HANA and monitors data already written (freshness, volume, schema, SQL rules); no announced validation at the point of entry[26][27] | Reads SAP S/4HANA and SAP Gateway over OData, Salesforce, Dynamics 365 and the ERP databases on SQL Server (Primavera, PHC, Sage). Validates records at the point of entry: the integrator calls the dataset's rules through the API before writing them and gets a verdict for each record, with nothing stored by DataVault. Never writes to the ERP or the CRM: the fix comes out as SQL/PySpark or a PR |
| Trust for AI agents | Catalog and AI governance; no announced readiness verdict per use case[1][28] | — | Monitor data and pipelines; no announced verdict on the dataset before an agent consumes it[5][7] | Explainable trust score and readiness per use case (RAG, training, agent, BI), a pre-ingestion gate for RAG and an MCP server so the agent asks before it uses Evidence |
| Where the work runs — and what leaves your perimeter | Vendor SaaS or installation in the customer's environment (Collibra DQ installs there): once installed, processing stays on your infrastructure[1][28] | Runs in your pipeline, on your warehouse or your Spark: nothing leaves — operations and scale are on you | Read warehouse metadata and query logs, as SaaS or with the collector in your environment (Monte Carlo)[7] | On SQL sources, profiling runs in the source itself and only aggregates and a few examples per column come back; row-level rules and duplicates use a sample of up to 5,000 rows per table, processed in the EU. We keep results and limited examples, never the table. With AI on, examples follow the workspace's AI policy |
| AI in your own account | AI built into the vendor's platform; no announced own key or model in your cloud account[1][2] | — | AI built into the vendor's platform; no announced model in your cloud account[5][7] | Your own key, Claude in your AWS or GCP account, a per-workspace model policy with an “AI off” mode, and samples anonymised before any budget provider |
| AI Act compliance | AI governance as a suite module (Collibra: ISO 42001)[28] | — | No announced Art. 50 labelling or Art. 10 dossier[5][29] | Art. 50 labelling on every AI output + exportable Art. 10 dossier |
| Security, residency and certifications | Informatica: ISO 27001 (BSI) and SOC 2 Type II; Collibra: SOC 2 and ISO 27001/42001, with a public trust center. SaaS or installed in the customer's environment[1][2][28] | Runs inside your perimeter: security and certification are those of your own infrastructure | Monte Carlo and Sifflet: SOC 2 Type II and ISO 27001; Monte Carlo as SaaS or hybrid, with the collector in your environment[5][7][29] | SaaS hosted in the EU (Google Cloud europe-west1), read-only enforced in code and optional AI with your own key or in your own AWS or GCP account (Bedrock or Vertex), with samples that can be anonymised before the model. A fixed egress IP (Cloud NAT) for your firewall allow-list: built, not yet active in production. SOC 2 Type I in progress, ISO 27001 next — no badges we don't have: we don't claim them, and we answer with the technical evidence for each control |
| Identity and administration | SSO and role-based access control, included in the suite[1][2] | Open source has no users; SSO and roles come with paid cloud plans (e.g. Soda Team)[4] | SSO and roles on higher-tier plans[5] | OIDC single sign-on with your IdP (Entra ID, Google Workspace, Keycloak…), TOTP MFA mandatory for organisation admins and enforceable across the workspace, per-workspace roles, teams, an audit log exportable to CSV and support access only with your consent, read-only and for 72 h at most; no SCIM yet |
| Support and SLA | Support and availability levels in the contract — Informatica IDMC: 99.5% with service credits; Collibra: a public status page[1][2][30] | Community support for open source; support with an SLA only on paid plans[3][4] | Support and SLA by plan, in the annual contract[5] | No contractual availability or support SLA yet: it only goes into the contract after a quarter of measured availability and a support policy by severity, still being defined. Today: daily backups with 7-day PITR and offline mode if AI fails |
| Data exit | End-of-contract data extraction, under the service terms[1][2] | Tests and results are already yours, in code and in your warehouse | Data exports only on the Scale and Enterprise plans (Monte Carlo)[31] | Self-service export of the workspace data (GDPR) and of the configuration as code (datavault.yaml) |
| Real cost | Public UK G-Cloud prices: Collibra DQ £122,919 per licence/year; Informatica IDMC £74,999/year, plus implementation[1][2] | Licence 0; Soda Team $750/month, plus warehouse compute and engineering hours[4] | Annual contracts or per monitored table (Metaplane, with 10 tables free); Sifflet ≈$48k/year at published pricing[5][32] | Free plan; paid plans from €490/mo, no implementation project Evidence |
Sources
Vendors' public pages behind the competitor columns. The numbers in the table point here; the DataVault column links to the RFP answers and the security page.
- 1.UK G-Cloud — Collibra Data Quality (service 136629392504870) · applytosupply.digitalmarketplace.service.gov.uk ·
- 2.UK G-Cloud — Informatica IDMC (service 727452156234281) · applytosupply.digitalmarketplace.service.gov.uk ·
- 3.Great Expectations — plans and pricing · greatexpectations.io ·
- 4.Soda — plans and pricing · soda.io ·
- 5.Sifflet — plans and pricing · siffletdata.com ·
- 6.Monte Carlo — docs: monitors overview · docs.getmontecarlo.com ·
- 7.Monte Carlo — docs: security and compliance overview · docs.getmontecarlo.com ·
- 8.Metaplane — docs: monitor types · docs.metaplane.dev ·
- 9.Monte Carlo — docs: Monitors as Code (montecarlo.yml and dbt schema.yml meta) · docs.getmontecarlo.com ·
- 10.Monte Carlo — docs: generating monitors from dbt tests · docs.getmontecarlo.com ·
- 11.Sifflet — docs: Monitors as Code (workspaces and monitors in YAML) · docs.siffletdata.com ·
- 12.Sifflet — docs: incidents from monitor runs or dbt tests · docs.siffletdata.com ·
- 13.Metaplane — docs: Monitors as Code (in dbt model meta) · docs.metaplane.dev ·
- 14.Informatica — docs: rule specification modes · docs.informatica.com ·
- 15.Collibra — docs: about the rule workbench (custom SQL rules) · productresources.collibra.com ·
- 16.Collibra — docs: supported SQL rule types · productresources.collibra.com ·
- 17.dbt — docs: data tests (singular and generic) · docs.getdbt.com ·
- 18.Great Expectations — docs: define a custom Expectation class · docs.greatexpectations.io ·
- 19.Great Expectations — docs: use SQL to define a custom Expectation · docs.greatexpectations.io ·
- 20.Soda — docs: user-defined checks (SodaCL) · docs.soda.io ·
- 21.Monte Carlo — docs: Custom SQL monitors · docs.getmontecarlo.com ·
- 22.Collibra — Data Quality & Observability · collibra.com ·
- 23.SAP — Master Data Governance · sap.com ·
- 24.Informatica — Data Quality & Observability · informatica.com ·
- 25.Collibra Marketplace — Driver for S/4HANA Integration · marketplace.collibra.com ·
- 26.Monte Carlo — docs: Salesforce CRM integration · docs.getmontecarlo.com ·
- 27.Monte Carlo — docs: SAP HANA integration · docs.getmontecarlo.com ·
- 28.Collibra — Trust Center · collibra.com ·
- 29.Monte Carlo — Trust Center · trust.montecarlo.ai ·
- 30.Collibra — status page · status.collibra.com ·
- 31.Monte Carlo — docs: data exports · docs.getmontecarlo.com ·
- 32.Metaplane — plans and pricing · metaplane.dev ·
Security
Your data stays where it is.
Read-only access
DataVault never writes, moves or changes data in your sources. It reads, analyzes, reports.
Official vendor methods
Official drivers and APIs for every platform. No scraping, no shortcuts.
Encrypted credentials
Encrypted with Fernet before being persisted. Never shown in logs.
Controlled samples
On SQL sources, profiling runs in the source itself. Row-level rules and duplicates use at most 5,000 rows per table, processed in the EU — the table is never copied. Samples used by the AI are limited and anonymizable.
Pricing
Simple pricing. No implementation project.
Gratuito
€0/month
Connect one source and watch the engine at work.
- Data sources
- 1
- Datasets
- 25
- Scans per month
- 30
- AI reports per month
- 5
- Measurement history
- 30 days
- Full quality engine + sample AI suggestions
- No card required
Team
€490/month
For the data team to put quality into production.
- Data sources
- 5
- Datasets
- 500
- Scans per month
- Unlimited
- AI reports per month
- 200
- Measurement history
- 1 year
Everything in Gratuito, plus:
- API tokens
- Alert webhooks
- Document analysis for RAG
- Scheduled email reports
- Synthetic data from the profile
- Entity matching across datasets
- Business glossary linked to columns
- Read-only rehearsal of the fix before accepting it
- Unlimited users
- ODCS contracts + CI gate
Business
€1,190/month
For whoever answers for compliance, cost and remediation.
- Data sources
- 15
- Datasets
- 2,000
- Scans per month
- Unlimited
- AI reports per month
- 1,000
- Measurement history
- 2 years
Everything in Team, plus:
- AI Act compliance dossier
- Platform cost telemetry
- Remediation as a Pull Request (GitHub/GitLab)
- Your own Anthropic key (BYOK)
- Choice of AI models per task
- Deep profiling by default (correlations and co-nullness)
- 4× the Team AI cap
Enterprise
Custom
For custom volumes and procurement requirements.
Custom volumes: no caps on sources, datasets, scans or AI
Everything in Business, plus:
- Claude in your organisation's cloud account (Amazon Bedrock or Google Vertex AI)
- Assisted onboarding
On request, in progress
- Dedicated instance, managed by Praxis
- DPA with negotiated terms (the template already exists)
- Contractual availability SLA
Launch pricing, per month and per workspace, excluding VAT, billed annually; monthly billing costs 20% more. Team and Business are activated in the app, by card, with your company's VAT ID on the invoice; Enterprise is contracted with us. The gap between DIY open source and the enterprise suites — whose public prices are cited in the comparison above — is where we live, on purpose.
See everything each plan includesSee DataVault working on your data.
A 30-minute demo, with your data or our demo ERP. You leave knowing what it would find in your account.