Anjuna Northstar

Anjuna Northstar is a self-hosted platform for collaborative analysis of sensitive or live datasets. It suits AI operations teams and data scientists who need several organizations to work together without exposing raw data or proprietary code. The strongest case is privacy-preserving, model-ready collaboration; teams wanting transparent pricing or a fully managed service should consider other options.

Overview

Northstar creates confidential clean rooms on demand so multiple parties can combine data, run analysis, and receive actionable results while protecting privacy and intellectual property. It is powered by Anjuna Seaglass and runs in customer environments, with no telemetry or runtime dependency on Anjuna-hosted services. That deployment model gives organizations control over where workloads run, but it also means they must operate self-hosted software rather than rely on a vendor-hosted workspace.

The security design uses confidential computing to protect data and code during ingestion, processing, and analysis. Remote attestation, isolation of data in use, and access control are aimed at preventing exposure while collaboration is under way. This is a focused fit for sensitive cross-company projects, not a generic analytics tool for teams without those requirements.

Key features

Collaborative clean rooms

On-demand confidential clean rooms let parties share data for a defined analysis without handing over unrestricted copies. Privacy-safe matching supports collaboration where both sides need results but must protect their underlying information and intellectual property.

Jupyter-based preparation

Northstar uses off-the-shelf Jupyter Notebooks for dataset preparation and manipulation. Data scientists can work in a familiar notebook environment instead of learning a proprietary analysis interface, making it a practical bridge for existing workflows.

Bring existing models and code

Customers can bring their own AI models, tools, and analysis code without modifications. That reduces migration work for teams with established model pipelines and analysis practices, although the platform’s value still depends on those teams being able to deploy and maintain the surrounding self-hosted environment.

Hybrid confidential-computing deployment

Northstar runs on confidential-computing instances across cloud providers and on-premises environments, with hybrid deployment supported. The page names AWS, Microsoft Azure, Google Cloud, AMD, Intel, NVIDIA, Redis, and HashiCorp among its partners. Those options suit organizations balancing existing infrastructure with cloud capacity, while buyers seeking a single hosted endpoint will find the self-hosted approach less convenient.

Pricing

There is one published plan, with pricing supplied by contacting sales. Anjuna invites prospects to request a live demo or discuss a purchase directly. No seat allowances, usage quotas, trial period, renewal terms, or separate support package are stated, so teams should resolve those commercial and operational points before committing. The model is best suited to organizations budgeting for an enterprise deployment rather than small teams comparing fixed monthly plans.

Platforms

Northstar is self-hosted and supports hybrid deployment across cloud and on-premises confidential-computing environments. Jupyter Notebooks provide the user-facing analysis workspace, while the named cloud, processor, database, and infrastructure partners indicate compatibility across a broad deployment stack.

Who it's for

AI operations teams and data scientists are the clearest audience. Northstar fits projects that merge sensitive or live data from multiple sources, run customer-owned models, and require both parties’ information and code to remain protected. It is also appropriate for organizations that need deployment in their own environments and can manage confidential-computing infrastructure. It is a weaker fit for buyers who need a hosted service, a public price list, or a small-team free tier.

Pros and cons

Pros

• Protects data and code during use: confidential computing, remote attestation, isolation, and access control address the risks of collaborative analysis.
• Works with existing practice: off-the-shelf Jupyter Notebooks and unmodified customer models and tools reduce the need to rebuild workflows.
• Deployment control: self-hosting without telemetry or runtime reliance on Anjuna-hosted services suits organizations with strict environment requirements.
• Flexible infrastructure: hybrid operation across cloud and on-premises environments accommodates mixed estates.

Cons

• Sales-led pricing: custom quotes and no stated quotas or trial make early budget comparison difficult.
• Operational responsibility: self-hosting requires the customer to run and manage the deployment.
• Support terms are unclear: the product page does not state a Northstar support plan.

Alternatives

Data Clean Room Software is the broader category to explore when you want to compare clean-room products rather than commit to Northstar’s confidential-computing model.

Amazon Marketing Cloud is a free, web and API product for eligible advertisers, making it a better starting point when marketing use cases and no-cost access matter more than self-hosted deployment.

Placino offers a free tier with 500K datapoints per month, one clean room, and up to three partners, so it suits smaller pilots with explicit usage limits.

AppsFlyer Data Clean Room is worth considering for teams centered on Android, iOS, web, and API measurement workflows.

BigQuery Data Clean Rooms fits organizations that want contributor and subscriber roles for publishing and querying clean-room data.

Aggregion DataLabs & CleanRoom combines API, web, and self-hosted options for buyers seeking another paid deployment choice.

BeeKeeperAI (EscrowAI), Decentriq Data Clean Rooms, and INDICO DataHub 2 are additional alternatives; choose among them based on their respective deployment and commercial models.

Verdict

Choose Northstar when your AI or data-science team needs several parties to analyze sensitive or live data while keeping data and code protected, and when self-hosting is acceptable. Its combination of confidential clean rooms, familiar Jupyter notebooks, and unmodified customer models is the main reason to choose it. Look elsewhere if you require published pricing, a free or low-cost plan, or a vendor-hosted service with clearly stated support terms.