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

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

Auriko

Freemium

Cache-aware LLM router and inference platform with one API across major providers and zero provider price markup.

Unified API: One OpenAI-compatible endpoint fronts OpenAI, Anthropic, Google, xAI, Fireworks, Together, DeepSeek, Moonshot and more.
Cache-Aware Routing: Routes each request using cost estimates that account for each provider's cache hit behavior and workload patterns.
Multiple Focus Modes: Optimize routing for cost, time-to-first-token, throughput or balanced modes, with optional custom weights.
Deterministic Routing (Pro): Always picks the highest-scoring eligible route so production behavior is reproducible.
Bring Your Own Key: BYOK support lets teams keep existing provider contracts and quotas while still benefiting from the router.
Fallback & Load Balancing: Automatic fallback and load-balanced routing keep apps up when a single provider degrades.

Production LLM Cost Reduction: Engineering teams cut inference bills by routing chat and RAG traffic to the cheapest cache-friendly provider.
Reliability Fallback: Ops teams shield user-facing agents from provider outages via automatic fallback routes.
Latency-Sensitive Apps: Real-time products optimize for time-to-first-token when the user is watching a stream.
BYOK Enterprise Deployments: Enterprises route through Auriko while keeping token spend on their own provider contracts.
Multi-Model A/B Testing: Product teams experiment with different backend models without rewriting client code.

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.