What features do Solana Networks TrafficWiz vs Nubeva SKI offer?

**Solana Networks TrafficWiz** differentiates with Encrypted traffic classification using deep learning without payload decryption, Support for TLS 1.3, QUIC, and DNS-over-TLS (DoT) traffic identification, Real-time inline inspection and offline PCAP file analysis. **Nubeva SKI** differentiates with Real-time session key extraction from TLS processes in memory, Read-only sensor architecture requiring no code or application changes, Multiple deployment formats including C libraries, standalone agents, and containers.

Who makes Solana Networks TrafficWiz vs Nubeva SKI?

**Solana Networks TrafficWiz** is developed by Solana Networks. **Nubeva SKI** is developed by nubeva technologies. Vendor maturity, funding stage, and team size can be important factors when evaluating long-term viability and support quality.

Is Solana Networks TrafficWiz a good alternative to Nubeva SKI?

Solana Networks TrafficWiz and Nubeva SKI serve similar Network Detection and Response use cases: both are Network Detection and Response tools, both cover TLS, Network Visibility. Review the feature comparison above to determine which fits your requirements.

Solana Networks TrafficWiz vs Nubeva SKI (2026) | Compare Network Detection and Response Tools

Q: What is the difference between Solana Networks TrafficWiz vs Nubeva SKI?

**Solana Networks TrafficWiz**: Deep learning-based encrypted traffic classification up to 25 Gbps w/o decryption. built by Solana Networks. headquartered in Canada. Core capabilities include Encrypted traffic classification using deep learning without payload decryption, Support for TLS 1.3, QUIC, and DNS-over-TLS (DoT) traffic identification, Real-time inline inspection and offline PCAP file analysis.. **Nubeva SKI**: TLS decryption solution that extracts session keys from memory for traffic inspection. built by nubeva technologies. headquartered in United States. Core capabilities include Real-time session key extraction from TLS processes in memory, Read-only sensor architecture requiring no code or application changes, Multiple deployment formats including C libraries, standalone agents, and containers.. Both serve the Network Detection and Response market but differ in approach, feature depth, and target audience.

Solana Networks TrafficWiz

Deep learning-based encrypted traffic classification up to 25 Gbps w/o decryption.

Network Detection and Response

Commercial

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Nubeva SKI

TLS decryption solution that extracts session keys from memory for traffic inspection

Network Detection and Response

Commercial

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Side-by-Side Comparison

Feature

Solana Networks TrafficWiz

Nubeva SKI

Pricing Model

Commercial

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Core Features

Encrypted traffic classification using deep learning without payload decryption
Support for TLS 1.3, QUIC, and DNS-over-TLS (DoT) traffic identification
Real-time inline inspection and offline PCAP file analysis
Processing of live traffic at up to 25 Gbps with low latency
Optional Labeler, Modeler, and Classifier toolset for custom AI model training and deployment
Flow metadata, timing pattern, and statistical signature analysis
Lawful intercept support with privacy-by-design (no decryption required)
Compliance alignment with ISO/IEC 27001 and NIST Cybersecurity Framework

Real-time session key extraction from TLS processes in memory
Read-only sensor architecture requiring no code or application changes
Multiple deployment formats including C libraries, standalone agents, and containers
Key export via file writing, local piping, or secure network forwarding
Support for keystore databases and key server infrastructure
Decrypt C Library and turnkey Decrypt Container
Key destruction after decryption to maintain forward secrecy
Optional key archiving for forensic purposes

Integrations

Network probes

Firewalls

SIEM platforms

SD-WAN

Big data analytics environments

Next-Generation Firewalls (NGFWs)

Routers

No integrations listed

Community

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Solana Networks TrafficWiz vs Nubeva SKI: 2026 Comparison

Solana Networks TrafficWiz

Nubeva SKI

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