The short version
Cogneris is a multi-document, ReAct-architecture document AI platform with pay-per-page pricing, multi-LLM extraction, and a complete platform stack including review queues and audit trail. Google Document AI is Google Cloud's document extraction service — strong primitives, full compliance footprint, and tight GCP integration, but you assemble the platform yourself.
Capability comparison
| Capability | Cogneris | Google Document AI |
|---|---|---|
| Product type | Complete platform — extraction, validation, workflows, audit trail | Cloud service — extraction APIs you assemble into a workflow |
| Pricing model | Pay-per-page, includes review & audit features | Pay-per-page, additional GCP costs for storage / KMS / orchestration |
| Pre-built parsers | 40+ document types out of the box, configurable | 20+ specialized parsers (invoice, receipt, W-2, 1040, etc.) |
| LLM strategy | Multi-LLM (OpenAI + Anthropic), zero-retention configured, model-version pinning | Gemini integration available, Google models only |
| Workflow / orchestration | Built-in: review queues, validation rules, webhook callbacks | Bring your own — Cloud Workflows or external orchestrator |
| Audit trail | Immutable per-request log with prompt hash, model version, response | Cloud Logging integration; you build the schema |
| Custom model training | Few-shot config + active learning queue | Custom model training in Document AI Workbench |
| Compliance | SOC 2 Type II in progress, GDPR DPA, CCPA, HIPAA BAA on Enterprise | Inherits Google Cloud's compliance certifications (extensive) |
| Vendor lock-in profile | Standard REST API, portable schema | GCP ecosystem; portability requires migration work |
| Best fit | Teams that want a complete platform with batteries included | Teams already deeply on GCP that want raw building blocks |
When to choose which
Choose Google Document AI when…
- You're already deep in GCP and want a service that lives next to your data and IAM.
- You want Google's full compliance stack (FedRAMP, IL5, etc.) without negotiating it from a smaller vendor.
- You're building entirely on Gemini and want LLM lineage tied to one provider's model family.
- You have engineers to assemble the platform — workflow, review UI, audit schema, alerting — yourself.
Choose Cogneris when…
- You want a complete platform with review queues, validation, and audit trail out of the box.
- You need multi-LLM — different providers for different document types, or failover between them.
- You want vendor-portable extraction rather than a GCP-native one.
- You don't have an engineering team to build review interfaces and audit pipelines on top of a raw API.
- You need an audit trail with full LLM lineage per request (prompt, model version, response) without a custom logging build-out.
Platform vs primitive
This is the core distinction. Google Document AI is excellent at the extraction primitive — pull structured data from a document, fast and accurate. It is not a complete document-AI platform. To use it in production you need to build (or buy) review queues for low-confidence extractions, validation rules, audit-trail schemas, alerting, and orchestration. Cogneris bundles those as the product. If you have a strong platform team that prefers raw building blocks, Document AI gives you maximum control. If you'd rather not build the platform, Cogneris is a faster path to production.
Google processors and pricing notes
Google Document AI currently separates work by processor: OCR for text, Layout Parser for structure, Custom Extractor for trained fields, Custom Classifier for routing, and Splitter for multi-document files. Pricing is processor-specific and usually quoted per 1,000 pages, so compare the end-to-end workflow, not only the first extraction call.
| Google Document AI processor | Typical buyer intent | Cogneris equivalent | Cost/workflow watch-out |
|---|---|---|---|
| OCR processor | Recognize text from PDFs, scans, and images. | OCR API plus structured extraction when needed. | OCR alone still leaves field mapping, validation, review, and downstream JSON work. |
| Layout Parser | Preserve layout, tables, and blocks for RAG or search. | Document AI for RAG and parsing API. | Layout-aware output is useful, but business workflows still need schemas and citations. |
| Custom Extractor | Train extraction for custom documents. | Schema-based extraction API. | Training effort, evaluation sets, and schema versioning become operating costs. |
| Custom Classifier | Route documents by type. | Classification docs. | Classifier output still needs packet splitting, exception paths, and audit state. |
| Document Splitter | Split packets into component documents. | Document-type routing and async extraction jobs. | Packet workflows need per-document status, reviewer queues, and webhook retries. |
Compliance & certifications
Google Cloud's compliance footprint is unmatched — FedRAMP High, ISO 27001, SOC 2, HITRUST, IL5 in some configurations. Cogneris is currently SOC 2 Type II in progress with GDPR DPA, CCPA service-provider framing, and HIPAA BAA on Enterprise. For deeply regulated workloads (federal, defense), Document AI is the answer today. For most commercial enterprise workloads, Cogneris's posture is sufficient and getting stronger.
Multi-LLM flexibility
Google Document AI uses Google's models — Gemini and Google's specialized parsers. Cogneris routes to OpenAI, Anthropic, or both, configurable per tenant or per workflow. Customers who want provider-portable extraction (or who have explicit provider restrictions for compliance) get more flexibility from Cogneris. Customers who are happy on Gemini get tighter integration from Document AI.
Switching considerations
If you're evaluating Cogneris against Google Document AI as your incumbent, the practical pieces matter:
- Schema portability — Cogneris emits clean JSON; if your pipeline already consumes Google Document AI's output, mapping is typically a one-day translation layer.
- Side-by-side evaluation — we run a 7-day evaluation against your real documents alongside your existing Google Document AI workflow. You get an accuracy and latency report you can show your CTO.
- No annual commit — start with a month-to-month plan, scale up as confidence builds. The pay-per-page pricing means you only pay for what you actually process.
For the full security and compliance posture, see our trust page. For pricing, see pricing. For a side-by-side evaluation against your current workflow, talk to our team.