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

A side-by-side comparison of Cadenya and FreeLLMAPI — 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.

Tashfeen Ahmed

Freemium

Self-hostable router that aggregates free tiers from 34 LLM providers and 635 free model endpoints behind one OpenAI-compatible /v1 API.

Unified OpenAI-Compatible Endpoint: Exposes 635 free model endpoints from 34 providers behind a single /v1 API that existing OpenAI clients can use unchanged.
Smart Model Router: Selects the best available model per request and automatically falls over to another provider when one returns a rate limit.
Per-Key Quota Tracking: Monitors usage against each provider's free tier cap so requests are spread out and no individual key is exhausted.
Encrypted Key Storage: Provider API keys are stored encrypted rather than in plaintext configuration files.
Custom Endpoint Support: Any additional OpenAI-compatible chat, embedding, image or audio endpoint can be registered alongside the built-in providers.
Self-Hosting via Docker: Ships as a container image on GHCR with a Docker Compose setup, so the gateway runs entirely on your own infrastructure.
Desktop and Mobile Apps: Native builds for macOS and Windows plus an Android app on Google Play for running the router outside a server.
Signed Live Catalog: The model catalog updates itself from a signed feed, so new free models and quota changes arrive without pulling new code.

Zero-Cost Prototyping: Build and test an LLM application against free provider tiers before committing to a paid API contract.
Rate Limit Resilience: Keep a coding agent or chatbot running through provider rate limits by automatically failing over to another free endpoint.
Coding Agent Backend: Point Cursor, Claude Code or any OpenAI-compatible CLI at a single local endpoint instead of juggling provider keys per tool.
Model Comparison: Evaluate responses across hundreds of models from different labs through one consistent API surface.
Private Gateway Deployment: Self-host the router inside a network so provider keys and prompts never pass through a third-party proxy.
Multi-Provider Key Management: Consolidate scattered free-tier accounts into one encrypted store with visibility into remaining quota.