Commercial
The public repository builds trust through architecture, SDK contracts, and reproducible evidence. Commercial conversations start with a benchmark review and use-case fit discussion; there is no pricing sheet because fit matters more than a rate card at this stage.
Packages
Each path starts from the deployment shape, evidence needs, and operational risk profile rather than a one-size-fits-all tier.
For technical teams validating the public repo, benchmark suite, and plugin contracts before a deeper commercial review. The goal is to prove the runtime shape, reproduce local baselines, and identify the first buyer scenario worth pursuing.
For enterprises moving StreamKernel from evaluation into internal production use. This path focuses on protected builds, deployment packaging, operational support, and destination-specific readiness.
For ISVs embedding StreamKernel into a product or appliance. It covers redistribution rights, plugin boundaries, private builds, and support expectations around customer-owned deployments.
For vendors operating StreamKernel-backed services on behalf of customers. The conversation centers on support, managed-service rights, isolation, upgrade cadence, and evidence handoff.
For defense, government, healthcare, and regulated finance teams with air-gapped or compliance-heavy environments. This path emphasizes auditable inference, policy enforcement, private support, and controlled deployment.
Commercial boundary
The website should be clear about what is public and what requires a commercial conversation.
Architecture, SDK contracts, reproducible evidence, benchmark paths, and repo inspection.
Protected AI builds, production packaging, redistribution, OEM embedding, support, managed services, and negotiated patent rights. StreamKernel includes pending patent claims covering key AI enrichment and model governance methods.
Private support, air-gapped expectations, compliance-heavy environments, and buyer-specific runtime review.
Commercial path
Send the deployment model, target destinations, AI path, and support expectations so the licensing conversation starts with real context.