The AI Infrastructure
As the AI ecosystem explodes, it's becoming harder to understand where each tool fits. We get asked daily: "Are you like OpenRouter?" "Do I use you with LangChain?" "How is this different from Vercel AI SDK?"
The short answer: Cencori acts as the unified Infrastructure Layer.
We are not just a model router (though we do that). We are not just a frontend library (though we power them). We are the cloud platform that sits between your application code and the raw model providers, handling the "Day 2" problems of security, memory, and orchestration.
Here is exactly how we compare to the major players in the space.
vs OpenRouter
OpenRouter is a pipe. Cencori is a platform.
OpenRouter is fantastic if you just need raw access to a model API. But once you move to production, you need more than just token routing. You need to redact PII, store user memories, and manage agent state.
| Feature | OpenRouter | Cencori |
|---|---|---|
| Model Routing | Yes | Yes (14+ Providers) |
| Unified API | Yes | Yes (OpenAI Compatible) |
| Integrations | No | Vercel, Zapier, Supabase |
| Security | No | PII Redaction, Prompt Injection |
| Memory | No | Adaptive Memory (Vector Store) |
| Workflows | No | Agent Orchestration |
Verdict: Use OpenRouter for prototyping. Use Cencori for production applications that require security and state.
vs LangChain / LangGraph
LangChain is a library you host. Cencori is a serverless backend.
LangChain provides the logic for agents, but you still have to deploy it. You are responsible for the servers, the Redis instances for memory, and the observability stack. Cencori provides these as managed services.
| Feature | LangChain | Cencori |
|---|---|---|
| Model Routing | Yes (via Adapter) | Yes (Native) |
| Unified API | Yes | Yes |
| Integrations | Yes (Code) | Yes (Native & Code) |
| Security | No | PII Redaction, Prompt Injection |
| Memory | Partial (Self-Hosted) | Adaptive Memory (Managed) |
| Workflows | Yes (LangGraph) | Agent Orchestration (Serverless) |
| Hosting | Self-hosted (e.g. EC2, Fargate) | Serverless Cloud |
| State | Redis/Postgres (Manual) | Built-in Persistence |
| Retries | You code it | Built-in (Exponential Backoff) |
| Observability | LangSmith (Separate) | Integrated Dashboard |
Verdict: You can use them together! Write your graph logic in LangChain, but offload the heavy lifting (memory, execution, tracing) to Cencori.
vs Vercel AI SDK
Vercel AI SDK is the Frontend. Cencori is the Backend.
Vercel has built the standard for frontend AI integration—hooks like useChat and useCompletion are best-in-class for React. Cencori is the engine that powers them from the backend.
| Feature | Vercel AI SDK | Cencori |
|---|---|---|
| Model Routing | Yes (Client-side) | Yes (Server-side) |
| Unified API | Yes | Yes |
| Integrations | Yes (Frontend) | Yes (Backend) |
| Security | No (Client-side only) | Enterprise-grade (PII, Injection) |
| Memory | No | Vector Store + Adaptive |
| Workflows | No | Agent Orchestration |
| Role | Frontend Library | Backend Engine |
| Focus | UI State & Streaming | Intelligence & Security |
Better Together: We strongly recommend using the Vercel AI SDK for your UI, and plugging Cencori in as the provider. It's the perfect stack.
vs Vercel AI Gateway
Vercel AI Gateway focuses on network. Cencori focuses on intelligence.
Vercel's gateway is excellent for caching and rate-limiting at the edge. Cencori acts as an application gateway, understanding the content of the requests (to redact info or update memory).
| Feature | Vercel AI Gateway | Cencori |
|---|---|---|
| Model Routing | Yes | Yes |
| Unified API | Yes | Yes |
| Integrations | Limited | Vercel, Zapier, Supabase |
| Security | Basic (Firewall) | AI-Specific (PII, Jailbreak) |
| Memory | Stateless | Stateful (User Memory) |
| Workflows | No | Agent Orchestration |
| Primary Goal | Caching & Rate Limiting | Agent Orchestration |
| Intelligence | Passive (Network Layer) | Active (Application Layer) |
Verdict: Cencori is an Active intelligence layer; Vercel is a Passive network layer.
vs LiteLLM
LiteLLM is a library. Cencori is a managed service.
LiteLLM is a great Python library for normalizing APIs. Cencori does this too, but adds the infrastructure layer (logging, users, security) on top.
| Feature | LiteLLM | Cencori |
|---|---|---|
| Model Routing | Yes (Python Proxy) | Yes (Global Edge) |
| Unified API | Yes | Yes |
| Integrations | No | Vercel, Zapier, Supabase |
| Security | Basic (API Key Mgmt) | Enterprise-grade (PII, Injection) |
| Memory | No | Adaptive Memory |
| Workflows | No | Agent Orchestration |
| Hosting | Self-hosted | Serverless Cloud |
Verdict: Use LiteLLM if you want to manage your own proxy server. Use Cencori if you want a managed platform.
vs Portkey / Helicone
Portkey & Helicone are for Observability. Cencori is for Intelligence.
These tools are excellent "rear-view mirrors"—they tell you exactly what happened. Cencori handles observability too, but primarily focuses on doing things (running agents, redacting data, retrieving memory).
| Feature | Portkey / Helicone | Cencori |
|---|---|---|
| Model Routing | Yes | Yes |
| Unified API | Yes | Yes |
| Integrations | Observability-Focused | Application-Focused |
| Security | Audit Logs Only | Active Redaction & Blocking |
| Memory | No | Adaptive Memory |
| Workflows | No | Agent Orchestration |
| Primary Goal | Logging & Analytics | Building Agents |
Verdict: If you just need logs, Portkey/Helicone are great. If you need Memory and Agents, you need Cencori.
vs Mastra
Mastra is a framework. Cencori is the infrastructure.
Similar to Mastra, Cencori provides tools for building agents. However, Mastra is a framework you run yourself (like Next.js), whereas Cencori is the cloud platform backing it (like Vercel).
| Feature | Mastra | Cencori |
|---|---|---|
| Model Routing | Yes (Local) | Yes (Cloud) |
| Unified API | Yes | Yes |
| Integrations | Yes (Code) | Yes (Native) |
| Security | No | PII Redaction, Prompt Injection |
| Memory | Local / Postgres | Managed Vector Store |
| Workflows | Yes (Local Execution) | Serverless Execution |
| Hosting | Self-hosted | Serverless Cloud |
| State | Postgres (Manual) | Built-in Persistence |
Verdict: Mastra is a promising framework. Cencori is the platform to run it on (or replace the backend parts entirely).
Conclusion
The AI stack is settling into three layers:
- Frontend/Frameworks (Vercel AI SDK, LangChain, Mastra)
- Infrastructure/Intelligence (Cencori)
- Models (OpenAI, Anthropic)
Cencori is laser-focused on that middle layer: making it safe, easy, and reliable to run intelligent workloads in production.