Aegisora vs Cadenya: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Aegisora and Cadenya — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Aegisora

Freemium

Narrow control plane for AI agents: enforce least-privilege on tool/API calls, block PII leaks, and generate human-readable audit logs.

Sidecar proxy: deploys inside your VPC as a lightweight proxy so raw payloads and PII never touch third-party infrastructure.
Least-privilege enforcement: block unauthorized tool calls and API requests based on runtime policies.
Fail-closed compliance: on proxy or network disruption, policies default to fail-closed to prevent unverified execution.
Immutable audit logs: every tool call, payload interception, and policy decision recorded as structured, human-readable logs.
PII masking: intercept and mask PII in agent payloads before they leave your perimeter.
Broad LLM & tool integrations: works with OpenAI, Anthropic, Azure AI, AWS Bedrock, GitHub, Slack, and Vercel out of the box.
Governance controls: rule-based and dynamic policy libraries, plus RBAC/SSO and multi-tier approvals in Enterprise.
Compliance-ready: SOC 2 / ISO compliance suite and custom SIEM integration in the Enterprise VPC tier.

Security team enforces which tools an internal agent can call and blocks anything outside its least-privilege scope.
Compliance officer generates SOC 2 / ISO evidence from immutable audit trails of every agent decision.
CISO deploys AI copilots in a regulated environment while keeping raw payloads and PII inside the corporate VPC.
Platform team masks PII in outgoing prompts before they hit a third-party LLM provider.
SecOps investigates an agent incident using a full timeline of tool calls, prompts, and policy decisions.
Enterprise standardizes runtime governance across many agents, LLM providers, and business units.

Paid

A managed agent runtime that layers your tools, agents and objectives so teams can test agent behavior safely and iterate fast.

Unified Tool Layer: Connect MCP servers, OpenAPI specs and existing endpoints once, and expose them to every agent through a single managed interface.
Model-Agnostic Variations: Set a default model and run canary variations on other providers side by side to compare behaviors before promoting a change.
Progressive Tool Discovery: Tool schemas stay out of the context window until an agent asks for them, with configurable max tools per search, search hints and a rerank threshold, so every request gets smaller.
Live Token Metering: Track cost as it accrues across loops, active variations, memory entries and widgets, so usage is visible rather than discovered on the invoice.
Webhooks and SSE Streaming: Push agent events — assistant messages, tool results, approval requests, sub-agent spawns, compaction, timeouts — into your own apps in real time.
Memory Layers: Attach stored documents such as playbooks and policy sets to an agent so its guidance persists across objectives.
Outcome Feedback Scoring: Collect scored comments on each objective, attributed to the variation and model that produced it, to see which behaviors actually work.
Embeddable Widgets: Drop an agent experience into any frontend as a widget rather than building the conversational surface yourself.

Operational Exception Handling: Run an agent that detects stalled shipments or orders and reroutes them through your dispatch API within policy.
Safe Model Migration: Evaluate a new frontier model as a canary variation against live objectives before switching the default.
Wrapping Existing APIs: Turn internal OpenAPI endpoints into agent-callable tools without rewriting the services behind them.
Embedding Agent Chat in a Product: Ship a conversational agent surface into an existing frontend using widgets instead of building it in-house.
Cost Control at Scale: Use progressive discovery and live metering to keep context size and per-loop cost down as agent traffic grows.
Agent Quality Review: Compare scored feedback across variations to understand which prompt or model changes improved real outcomes.