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

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

Cadenya

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.

Freemium

Runtime AI-agent governance and enforcement platform with sub-20ms policy checks, kill switches, and compliance-ready audit logs.

Runtime Policy Enforcement: Semantic check plus policy eval on every tool call in a p50 of under 20ms, either passing, holding, or denying the action before it touches the real world.
Emergency Kill Switch: One-click, server-side stop that halts any single agent or an entire org's fleet in under 6ms (measured).
Immutable Audit Trail: Cryptographically hash-chained, append-only records of every attempted action, classification, and decision — verifiable end-to-end for auditors.
Real-time Traces: Structured logs capturing input/output, model, token counts, latency percentiles, and cost per action with a searchable timeline and parent-child span tree.
Tiered Autonomy Governance: Assign each agent observe, advise, act-with-approval, or autonomous level, auto-apply the matching policy bundle, and flag drift when an agent outgrows its guardrails.
Real-time Cost Circuit Breaker: Synchronous spend caps per org, agent, user, or workspace across 1m/1h/1d/1mo windows, enforced in the policy path with burn-rate alerts before the budget is breached.
Compliance Framework Coverage: Auto-generated reports for SOC 2 Type II, HIPAA, GDPR, ISO 27001, EU AI Act, PCI DSS, and NIST AI RMF with row-level PostgreSQL isolation and PII scrubbing.
Multi-Framework SDKs: Python and TypeScript instrumentation that plugs into OpenAI, Anthropic, LangChain, LlamaIndex, CrewAI, AutoGen, and MCP in about three lines of code.

Enterprise AI Rollout: Give a platform team a single control plane to safely deploy autonomous customer-support, data-analyst, and code-review agents in production.
EU AI Act & SOC 2 Evidence: Generate cryptographically signed logs and pre-mapped reports auditors can accept for high-risk AI systems.
Prompt-Injection & Data-Exfil Defense: Block agents from calling risky tools or exposing PII when a user prompt or document tries to hijack their behavior.
Agent Cost Control: Cap spend synchronously per agent, team, or workspace so a runaway loop or misconfigured model cannot burn the monthly budget.
Air-Gapped or Regulated Environments: Self-host the full stack on Docker or Kubernetes inside a defense, health, or finance network with zero customer data leaving the perimeter.
Human-in-the-Loop Approvals: Route irreversible actions (payments, deletes, external sends) into a hold queue that pauses the agent until an approver signs off.