Hopper vs
Runtime SCA

Less Noise. More Clarity. Faster Decisions.

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Hopper's reachability functionality is a gamechanger. Even with our runtime tools, it still really matters to know the reachability in our codebase, enabling the "shift left" in our security program

Omri Vaizman
Security Software Engineer

Runtime SCA tools observe application behavior after deployment, using traffic sampling or eBPF, but their visibility stops at what runs in production. They miss where the issue came from, how to fix it, and entire layers of the stack, like front-end, serverless, Windows, or any environment without runtime traffic.

Hopper delivers the same level of insight without agents or production traffic. By simulating runtime behavior at build-time through deep static analysis, exposing real, reachable risks with full code and dependency tracing. You get broader coverage, faster deployment, and developer-ready remediation that runtime tools can’t provide.

Trusted by Leading Software Teams

Why Customers Choose Hopper

Agentless and Instant

Hopper connects directly to Git with read-only access, with no agents, no production traffic, and no environment setup required. It deploys in minutes and delivers results immediately.

Broader and Deeper Coverage

Unlike runtime tools limited to modern Linux apps, Hopper analyzes the full codebase across Windows, client-side, serverless, and on-prem environments. It maps every reachable path and dependency, not just what happens to execute.

Precise Findings Developers Can Act On

Hopper connects directly to Git with read-only access, with no agents, no production traffic, and no environment setup required. It deploys in minutes and delivers results immediately.

Features Comparison Chart

Capability
Hopper
Runtime SCA
Analysis Timing
Build-time (shift-left) for earlier, faster insight and remediation before code is deployed
Post-deployment only, exposing teams to runtime blind spots and delayed fixes
Coverage Scope
Full application coverage across environments (client-side, serverless, Windows, Linux, on-prem)
Limited to executed paths in modern Linux environments; no visibility into client-side, serverless, or on-prem code
Dormant or Rare Code Paths
Analyzed through static path tracing, including logic not triggered during normal execution
Rare vulnerable code paths could be missed due to runtime sample rate; visibility depends on traffic and test coverage
Deployment Model
Read-only Git access
Requires runtime agents, code hooks, or traffic capture to observe behavior
Use Case Fit
Supports all software models including shipped, embedded, OEM, hybrid, and B2B2X deployments
Poor fit for software delivered to customers or devices, including on-prem and embedded use cases
Industry Fit
Support for high-assurance sectors including automotive, medical, critical infrastructure, IoT, desktop, mobile, networking, and cybersecurity; cloud native or not
Limited to cloud-native environments; not suited for distributed or device-based software models
Performance and Resource Impact
No impact on production performance or infrastructure
Can add latency, increase memory and CPU usage, and interfere with observability in live environments
False Negatives
Low, due to exhaustive reachability analysis
High, due to limited runtime sampling that misses rare code paths
Developer Remediation Context
Shows exact fix location with linked call graph, manifest file, transitive vs direct path clarity, full dependency tree analysis, and source code navigation
Varies by tool; often lacks context, treats all dependencies as direct, and provides little or no clear fix guidance, especially for shaded or nested packages
Time to Value
Under 5 minutes, no production involvement
Can take days or weeks per environment