Deepnote

What Is Deepnote?

Data science has a tooling problem. Jupyter notebooks, the de facto standard for over a decade, were built for individual researchers — not for teams shipping production analytics. They are painful to version-control, impossible to collaborate on in real time, and stubbornly local-first in a cloud-native world. Google Colab patched some of these gaps but introduced its own constraints around vendor lock-in and limited customisation.

Deepnote set out to fix this. Founded in Prague in 2019 by Jakub Jurovych, Jan Matas, and Filip Stollar — engineers with backgrounds at Two Sigma, Palantir, and Google — the company built a cloud-native notebook from scratch with multiplayer collaboration as a first principle, not an afterthought. Think Google Docs, but for data science.

The result is a platform used by over 500,000 data professionals, including teams at SoundCloud, Gusto, and Statsig. In 2025, Deepnote made a significant strategic move: open-sourcing its entire notebook under the Apache 2.0 licence, positioning itself as a genuine Jupyter successor rather than just another managed notebook service. The open-source version introduces a clean YAML-based notebook format (.deepnote) that replaces Jupyter's notoriously messy JSON (.ipynb), making version control and code review practical at last.

For European teams, Deepnote sits in an interesting position. The founders are Czech, the engineering team is based in Prague, and the company graduated from Y Combinator before raising $23.8 million from Accel and Index Ventures. It is legally incorporated in the US as Deepnote Inc. — a common structure for European startups chasing American venture capital — but its roots and much of its talent remain firmly in the EU.

Key Features

Real-Time Collaboration

This is the headline capability. Multiple team members can work on the same notebook simultaneously, with live cursors, inline comments, and shared execution state. It transforms notebooks from solitary artefacts into collaborative workspaces where a data engineer can build a pipeline while an analyst explores the output in the same document. Sharing is as simple as sending a link — viewers do not need accounts.

AI Copilot and Agent

Deepnote was among the first data notebooks to ship an integrated AI copilot, offering context-aware code completions tailored to the specific block you are editing. Beyond autocomplete, the Deepnote Agent operates as an autonomous assistant that can generate entire analyses from natural language prompts, create visualisations, and run multi-step code workflows. It is not just a wrapper around GPT — the AI understands your notebook's data context, connected sources, and variable state.

Native SQL and Data Integrations

Unlike Jupyter, where connecting to a database requires boilerplate Python code and credential juggling, Deepnote provides native SQL blocks that run queries directly against connected data warehouses. Jinja templating lets you parameterise queries, and results flow seamlessly into Python blocks for further analysis. The platform supports over 80 data sources out of the box, including Snowflake, BigQuery, Redshift, Databricks, PostgreSQL, and MongoDB, with encrypted credential storage.

Scheduling and Production Deployment

Notebooks can be scheduled to run daily, weekly, or monthly (hourly on Enterprise), turning exploratory analysis into automated reporting pipelines. The platform also supports deploying notebooks as data apps via native Streamlit integration, and exposing notebook outputs through APIs. This bridges the gap between ad hoc exploration and production workflows that teams actually rely on.

Open-Source Notebook Format

The Apache 2.0 open-source release is strategically important. The .deepnote format stores notebooks as human-readable YAML with reactive execution — dependent blocks automatically re-run when inputs change. You can run Deepnote notebooks locally in your preferred IDE, then push to Deepnote Cloud when you need collaboration, scheduling, or managed compute. This eliminates the vendor lock-in concern that haunts every SaaS notebook platform.

Pricing

Deepnote's free tier is genuinely usable: up to three editors, five projects, unlimited viewers, and basic AI code completion. For individual data scientists or small teams exploring the platform, it is a reasonable starting point.

The Team plan costs $59 per editor per month (or $39 billed annually) and unlocks unlimited notebooks, full Deepnote AI, premium data warehouse integrations, scheduled execution, and 30 days of revision history. It includes $280 in CPU credits and $50 in GPU credits monthly. For a team of five editors on annual billing, that works out to roughly $195 per month — competitive with Hex or Databricks for comparable functionality, though not cheap.

Enterprise pricing is custom and adds single-tenant deployment, EU-region data residency, hourly scheduling, advanced access controls, audit logs, and dedicated support with SLAs. For organisations with strict compliance requirements, the Enterprise tier is where the real EU hosting guarantees live.

One tension worth noting: the per-editor pricing model means costs scale linearly with team size. Organisations with large data teams — say, 20+ editors — should model the total cost carefully against alternatives like self-hosted JupyterHub or Databricks.

An Education plan mirrors the Team tier's features at no cost for academic institutions, which is a smart long-term acquisition strategy.

EU Compliance & Privacy

This is where Deepnote's story requires nuance. The product was born in Prague. The founders are Czech. The engineering team sits in the EU. But the legal entity — Deepnote Inc. — is incorporated in the United States. For many European teams, this is fine. For organisations with strict public-sector procurement rules or sovereignty mandates, it warrants closer scrutiny.

On the technical compliance side, the picture is stronger. Deepnote holds SOC 2 Type II certification, encrypts all data at rest with AES-256 and in transit with TLS 1.2+, conducts regular third-party penetration testing, and runs a private bug bounty programme. The company provides a GDPR-compliant Data Processing Addendum.

Default hosting runs on AWS infrastructure. Enterprise customers can request region-specific data residency, including EU-only hosting, and private VPC deployments that keep data entirely within the customer's own cloud environment. For teams that need absolute control over data location, the Enterprise tier delivers — but the free and Team plans do not offer region selection.

The open-source option adds a third path: organisations can run Deepnote entirely on their own infrastructure, with zero data leaving their environment. For compliance-sensitive teams, this is the cleanest solution.

Who It's Best For

Data science teams that collaborate on analysis and need real-time multiplayer editing, shared compute environments, and native data warehouse connections. This is Deepnote's core audience and where it delivers the most value over Jupyter.

Organisations migrating from Jupyter that want a modern, cloud-native alternative without vendor lock-in. The open-source release and Jupyter compatibility make the transition low-risk.

Analytics teams building internal data products who need scheduling, Streamlit app deployment, and API access to notebook outputs — bridging the gap between exploration and production.

European startups and scale-ups comfortable with a US-incorporated entity that has strong Czech engineering roots and SOC 2 certification. Teams needing strict EU data residency should evaluate the Enterprise tier.

The Verdict

Deepnote delivers on its core promise: it makes collaborative data science genuinely pleasant in a way that Jupyter never has. The real-time editing, native SQL blocks, and AI copilot represent meaningful productivity gains for teams, not just cosmetic improvements. The open-source pivot under Apache 2.0 is a bold move that removes the vendor lock-in objection and positions Deepnote as infrastructure rather than just another SaaS tool.

The EU compliance picture is mixed. Technically strong — SOC 2 Type II, encryption, GDPR DPA, optional EU hosting — but the US incorporation is a wrinkle that some procurement teams will flag. The open-source self-hosting option is the cleanest answer for sovereignty-conscious organisations.

At $39-59 per editor per month, the pricing is fair but not cheap, and it scales linearly. For small-to-medium data teams, Deepnote is a compelling upgrade from Jupyter. For large enterprises, the Enterprise tier's custom pricing and dedicated hosting options are where the real value — and the real compliance guarantees — live.

Frequently Asked Questions

Is Deepnote a good replacement for Jupyter?

Yes. Deepnote is designed as a drop-in Jupyter replacement with significant improvements in collaboration, version control, and cloud-native workflow. The open-source .deepnote format is cleaner than .ipynb for git workflows, and you retain full compatibility with existing Jupyter notebooks. The main trade-off is that the richest features — AI copilot, scheduling, premium integrations — require the paid cloud platform.

Can I host Deepnote in the EU?

On the Enterprise plan, you can request EU-specific data residency and private VPC deployment. The free and Team plans run on AWS infrastructure without region selection. Alternatively, you can self-host the open-source version entirely within your own EU infrastructure for complete data sovereignty.

Does Deepnote support GPU computing?

Yes. The Team plan includes $50 in GPU credits per month, and you can select GPU-enabled machines for compute-intensive tasks like model training. Enterprise plans offer custom compute configurations. The free tier is limited to basic CPU instances.

How does Deepnote handle data security?

Deepnote is SOC 2 Type II certified. All data is encrypted at rest (AES-256) and in transit (TLS 1.2+). Integration credentials are encrypted and securely stored. The platform undergoes regular third-party penetration testing and maintains a private bug bounty programme. Enterprise customers get additional controls including audit logs and single-tenant deployment.

Is Deepnote suitable for production data pipelines?

Deepnote can handle lightweight production workflows through scheduled notebook execution and API deployment. However, it is primarily designed for collaborative analysis and exploration rather than heavy-duty orchestration. For complex production pipelines, you would typically use Deepnote for development and prototyping, then deploy to dedicated orchestration tools like Airflow or dbt for production.