Knowi VS DynamicDocumentAPI

Knowi's answer

Verizon Telstra Everbridge ConvergeOne NJM Insurance Volante Systems intlx Solutions Alteas Health Hometime Tata

DynamicDocumentAPI's answer:

Dynamic Document API is a young product. Our users are mostly developers and small SaaS teams, and we don't name customers without their permission.

Knowi's answer

Knowi runs AI agents inside the data layer rather than on top of finished dashboards. The agents reach schemas and the query engine directly, which means they can build queries, widgets and dashboards, not just answer questions about ones that already exist.

That architecture comes from what Knowi was built to do: query data where it already lives. It speaks MongoDB and Elasticsearch in their own query language, including nested documents and arrays, and it joins across MongoDB, PostgreSQL, Snowflake, Databricks SQL, Trino, Salesforce and REST APIs in a single query without first moving anything into a warehouse.

Knowi also runs its own AI by default, with OpenAI and Claude available as optional integrations rather than requirements, and it deploys to cloud, your own VPC, on-premises via Docker or Kubernetes, or air-gapped.

DynamicDocumentAPI's answer:

It covers the whole path from data to finished document in one API: you design a template in a visual or HTML editor (or send raw HTML, CSS or Markdown), send JSON, and get a PDF or image back. The same service also creates e-invoices (XRechnung, EN 16931, Factur-X / ZUGFeRD) that are validated against the official rules before they are returned, and can embed them in the PDF. Rendering is deterministic: the same template and data always give the same document, laid out by a headless browser, with no AI model generating content. Everything runs in the EU (Germany). It also ships an MCP server, so AI agents like Claude and ChatGPT can render documents directly.

Knowi's answer

Three practical reasons.

Your data does not have to be relational first. Most BI tools need a warehouse and an ETL pipeline before you see a chart, which means unstructured, nested and API data either gets flattened or gets left out. Knowi queries those sources natively, so the modelling work you would normally do up front becomes optional.

The AI is part of the query path, not a chat box on the side. Many platforms added a copilot that describes existing dashboards. Knowi's agents have access to the schema and the query engine, so they can create new datasets, widgets and dashboards from a question.

You control where it runs and which model touches your data. Knowi AI is the default, third-party models are optional, and deployment can be Knowi Cloud, your own VPC, on-premises, or air-gapped. Knowi is SOC 2 Type II certified, GDPR compliant, and offers a HIPAA BAA.

DynamicDocumentAPI's answer:

Most document APIs do one thing: convert HTML to PDF, or fill templates. Dynamic Document API does both, adds image templates, PDF merging and validated e-invoices, and offers them through a REST API, SDKs for Node and Python, Zapier, Make, n8n and an MCP server for AI agents. Data is processed and stored in the EU only, files come back as signed links that expire, and storing nothing at all is a supported option. Pricing is predictable: one published credit table, the same core features on every paid plan and a spend cap you control. We also publish a render-time benchmark with its method and raw data, so you can check our speed instead of trusting a claim. The free plan includes 50 renders a month without a credit card.

Knowi's answer

Data, engineering and product teams at mid-market and enterprise companies whose data is spread across more than one kind of system: NoSQL alongside SQL, warehouses alongside SaaS APIs and documents.

Two buying patterns show up most often. Internal analytics teams who need governed self-service across sources without building a pipeline for every question. And product teams who need customer-facing, multi-tenant analytics embedded inside their own application with row-level access control.

By industry, the customer base skews to telecom, healthcare, manufacturing, SaaS and adtech, proptech and e-commerce. Knowi is sold through a sales team and priced per deployment rather than by public self-serve tier.

DynamicDocumentAPI's answer:

Developers and product teams whose software has to send documents: invoices after a payment, certificates after a course, reports, labels and statements. Typical users are SaaS companies, marketplaces, e-commerce and learning platforms. A second group are businesses in Germany and the EU that need compliant e-invoices (XRechnung, ZUGFeRD) for the German B2B e-invoicing mandate and public-sector buyers. No-code builders use it through Zapier, Make and n8n, and teams building AI agents use the MCP server.

Knowi's answer

Knowi was founded in 2014, at the point where a lot of production data had stopped being relational. Teams were running MongoDB and Elasticsearch, and the BI tools of the day all assumed a star schema in a warehouse. Getting a dashboard meant building a pipeline first, and anything nested or semi-structured got flattened or dropped along the way.

Knowi was built the other way round: connect to the source, query it in its own language, and join across sources at query time instead of moving the data. Cross-source joins, post-query transformation and a dataset layer that data teams could govern followed from that starting point, and embedded analytics came from customers who wanted to give the same views to their own users.

The AI work is a continuation rather than a pivot. Because Knowi already owned the query path across every connected source, AI agents could be placed inside the data layer with access to schemas and the query engine, instead of being bolted onto a finished dashboard.

DynamicDocumentAPI's answer:

Almost every product eventually needs to produce documents, and most teams end up building their own rendering stack: a headless browser, fonts, page breaks, storage and retries. It works until an invoice splits a table row or a certificate shows the wrong date, and then it becomes a support ticket. We built Dynamic Document API to be that layer for them: templates, rendering and delivery as one service, hosted in Europe, treating pagination, fonts and formatting as engineering problems rather than edge cases. When e-invoicing became mandatory for B2B in Germany, we added validated XRechnung and ZUGFeRD output, and with AI agents producing more paperwork, an MCP server followed.

DynamicDocumentAPI's answer:

Rendering runs in Rust workers that drive headless Chromium over the DevTools protocol, with MiniJinja for templates. The API and control plane are built with Django and Django REST Framework, with Celery, PostgreSQL, Valkey and NATS JetStream for queues and events, and S3-compatible object storage for files. PDF/A output is checked with veraPDF and e-invoices with the official KoSIT validator. The dashboard and website are built with Next.js. Everything runs on Kubernetes in data centres in Germany.