NOMA

| Aspect | Noma Security | Protect AI | Lakera (Lakera Guard) | |--------|---------------|------------|------------------------| | Starting Price | Not publicly listed; reported to use sales-led or usage-based pricing | Not publicly listed; enterprise pricing available via vendor sales | Not publicly listed; vendor documents tiered plans but enterprise pricing requires vendor contact | | Suitable For | Enterprises securing agentic AI across discovery, posture, red teaming, and runtime enforcement | Teams focused on model security and AI pipeline controls; validate module coverage for agentic governance | Teams focused primarily on runtime guardrails against LLM threats and developer-centric runtime enforcement | | Key Strength | Emphasis on combining inventory/discovery with runtime enforcement and research activity in agentic vulnerability disclosures | Focus on model lifecycle and AI pipeline security controls; verify module depth for specific needs | Focus on runtime guardrails and developer-focused integration speed; suitable for teams needing lightweight runtime enforcement | | Key Weakness | Limited verified third-party user reviews; opaque pricing; public evidence of efficacy and false-positive rates is limited | Opaque pricing and broad platform scope require validation of module depth | May be narrower in governance and discovery capabilities compared with fuller enterprise governance platforms | - Direct competitors and differentiators - Protect AI vs Noma: Protect AI emphasizes model security and pipeline controls; buyers should validate relative strength for agentic tool-call governance. - HiddenLayer vs Noma: HiddenLayer is associated with model security and lifecycle controls; Noma emphasizes runtime protection and discovery/governance. - Lakera vs Noma: Lakera focuses on runtime guardrails and prompt-injection defenses; Noma positions broader discovery, posture, and agent execution controls. - Feature comparison: areas of relative strength and gaps - Areas where Noma appears to focus (based on vendor and press materials): - Agent-focused runtime security: monitoring prompts, responses, tool calls, and MCP interactions - Combining inventory/discovery with runtime enforcement in a single offering - Areas requiring validation relative to competitors: - Depth of model artifact scanning and ML supply chain validation - Breadth of governance features (policy-as-code, approvals, attestations, model risk management workflows) - Pricing comparison - Noma and several competitors keep enterprise pricing behind sales; Lakera documents tier names but not enterprise price points. - Customer comparison and competitive selection - Public direct customer comparisons are limited due to scarce third-party reviews. Community shortlists show multiple vendors are commonly considered in procurement processes. - Market share and adoption trends - Market share is difficult to determine because the category is emerging and many vendors are private. Noma reports growth and enterprise adoption; independent verification of market share is not available.