| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 15 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Performs deep packet inspection and protocol dissection on network traffic to extract granular details about communication patterns, application behavior, and protocol-level anomalies. Leverages Zeek's battle-tested engine to decode and analyze hundreds of network protocols.
Analyzes behavioral patterns in encrypted network traffic without decrypting payloads, extracting metadata such as certificate information, TLS versions, cipher suites, and communication patterns to identify suspicious encrypted connections.
Enables creation of custom detection rules using Zeek scripting language to identify specific threats, attack patterns, or policy violations. Supports deployment of custom rules to detect organization-specific threats.
Establishes baseline profiles of normal network behavior and enables comparison of current traffic against these baselines to identify deviations. Supports creation of organization-specific network behavior models.
Integrates external threat intelligence feeds with network analysis to automatically correlate observed network activity against known indicators of compromise, malicious IPs, and threat signatures.
Analyzes network traffic volume, bandwidth consumption, and performance metrics to identify capacity issues, traffic patterns, and potential DDoS or resource exhaustion attacks.
Integrates Corelight's network analysis capabilities with existing SIEM platforms, threat intelligence systems, and other security tools through standardized data formats and APIs.
Converts raw network traffic analysis into structured, machine-readable logs organized by connection type, application, and protocol. Generates standardized event records that integrate seamlessly with SIEM platforms and threat intelligence systems.
+7 more capabilities
Store and automatically index high-dimensional vector embeddings in a managed, scalable database without manual infrastructure provisioning. The system handles index optimization and partitioning transparently.
Query stored vectors to find semantically similar items by computing distance metrics between query embeddings and indexed vectors. Returns ranked results based on relevance in sub-100ms latency.
Serve as the retrieval component in Retrieval-Augmented Generation pipelines, providing relevant context documents to language models for grounded responses.
Provide a generous free tier (1 pod, 100K vectors) enabling teams to build and test real applications before committing to paid plans.
Execute searches using both dense vector embeddings and sparse keyword-based vectors simultaneously, combining results to improve relevance by capturing both semantic and lexical similarity.
Filter vector search results based on metadata attributes (tags, categories, timestamps, custom fields) before or during similarity search, enabling faceted and conditional retrieval.
Insert, update, or replace multiple vectors and their metadata in a single batch operation, optimizing throughput for bulk data ingestion without individual API calls.
Pinecone scores higher at 71/100 vs Corelight at 51/100. Corelight leads on ecosystem, while Pinecone is stronger on adoption and quality. Pinecone also has a free tier, making it more accessible.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Partition vector data within a single index using namespaces, enabling logical separation of data (by user, tenant, or dataset) without creating separate indexes.
+4 more capabilities