Pangea AI Guard vs Qpoint

Pangea AI Guard protects data and interactions in applications that use large language models. It detects prompt injection, jailbreak attempts, malicious entities, personal and confidential information, secrets, language, code, competitors, custom entities, and restricted topics. Early-access features address images and prompt hardening. Configurable recipes combine detectors and can report, block, redact, encrypt, or defang detected content at stages including prompts, RAG ingestion, model responses, and agent tool inputs or outputs. Detection uses heuristics, machine-learning classifiers, and fine-tuned LLMs. Applications connect through APIs, SDKs, or gateway integrations for Portkey, Kong, and LiteLLM. SDK documentation covers Python, Node.js, Go, Java, and C#. Deployment options are SaaS, Edge, and Private Cloud; Edge and Private Cloud keep processing within the customer’s cloud boundary. Enabling AI Guard activates an Activity Log for calls, inputs, outputs, detections, and context. The current request limit is 20 KiB of text.

Qpoint monitors AI agents and applies policies where they run. It captures file activity, tool calls, outbound network requests and process execution, attributing events to agents and sessions. Policies can block secret reads, reject unapproved endpoints, redact outbound content and deny unapproved tool calls. Qpoint describes its endpoint deployment as a single binary, without gateways, sidecars or SDK integration; it can also run in containers, virtual machines, clusters and CI runners. It supports Linux, macOS, Windows and web, and lists Kubernetes support, network visibility and application tracing. Events can be exported to SIEM and observability systems, with Splunk, Datadog and Elastic named. The free Community plan covers up to 25 endpoints; Team covers up to 500, and Enterprise has unlimited endpoints. Enterprise includes SSO and role-based access control. Qpoint states that it has achieved SOC 2 Type II compliance.