FastRouter.ai vs Zep: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of FastRouter.ai and Zep — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
FastRouter.ai
FastRouter
Enterprise LLM gateway that routes, compares and monitors 200+ models through a single OpenAI-compatible API with zero markup.
Key features
- Unified OpenAI-Compatible API: Access 200+ models for text, image, video, embeddings and speech from one endpoint and swap models without code changes.
- Smart Auto Router: Routes each request to the best model based on cost, latency, throughput or output quality.
- Automatic Failover: Retries across providers, fallback model lists and virtual model aliases keep applications running during failures.
- Cost Control and Governance: Project and API key limits, member roles and access controls prevent spend spikes.
- Model Council and Playground: Compare latency, quality and cost side by side and combine models to cross-check outputs.
- Observability and Insights: Unified dashboards, activity logs, alerts and weekly Insights that rank cost-saving changes.
- Self-Hosting: Run the full gateway in your own cloud so prompts, responses, logs and provider keys stay in your network.
Best for
- An engineering team replaces several provider SDKs with one OpenAI-compatible endpoint to simplify integration.
- A product team cuts LLM spend by routing simple requests to cheaper models and reserving premium models for hard ones.
- A platform team keeps a customer-facing app online by defining fallback model lists across providers.
- A finance-conscious leader sets per-project and per-key limits to avoid surprise bills.
- A regulated enterprise self-hosts the gateway so prompts and provider keys never leave its network.
- A developer compares candidate models for latency, quality and cost in the playground before shipping.
Zep
Zep Software, Inc.
Context engineering platform providing long-term memory, temporal knowledge graphs, Graph RAG, and automated context assembly for AI agents.
Key features
- Persistent Long-Term Memory: Stores full chat histories and conversation artifacts persistently to enable recall across long time spans, improving continuity in conversational experiences.
- Temporal Knowledge Graph (Graphiti): Builds a temporal knowledge graph with valid_at and invalid_at timestamps to track changing user state, preferences, and relationships over time for accurate contextual reasoning.
- Asynchronous Summaries & Artifacts: Automatically generates summaries, classifications, and structured artifacts from messages asynchronously to avoid adding latency to the user chat experience.
- Embeddings & Vector Search: Embeds messages and summaries to enable fast semantic search and retrieval of relevant past conversation snippets and business data.
- Document Collections: Provides a simple document-collection abstraction for vector search to complement memory features without being a general-purpose vector database.
- SDKs & Integrations: Official SDKs for Python, TypeScript/JavaScript, and Go with integrations for LangChain and LlamaIndex to simplify adoption in existing agent stacks.
- Managed Cloud Service (Zep Cloud): Offers a managed deployment with low latency, high availability, and additional capabilities like dialog classification and structured data extraction.
- Graph RAG & Automated Context Assembly: Combines graph-aware retrieval augmented generation with automated assembly of context from chat history and business data to reduce hallucinations and improve relevance.
- Persistent chat history storage and retrieval for AI assistants
- Automated generation of summaries and other conversation artifacts
- Message and summary embeddings to enable semantic search
- Document Collections abstraction for vector/document search
- Temporal knowledge graph (Graphiti) with valid_at/invalid_at to track state changes
- Automated context assembly for prompt construction (agent memory)
- Cloud managed offering (Zep Cloud) with low latency, HA, scalability, dialog classification, and structured data extraction
- Official SDKs: Python (zep-cloud / zep-python), TypeScript/JavaScript (@getzep/zep-cloud / zep-js), Go (zep-go)
- Asynchronous processing pipeline to avoid blocking user chat experience
- Client libraries with features like automatic retries and exponential backoff
Best for
- Personalized Conversational Assistants: Maintain long-term user memory so assistants remember user preferences, prior conversations, and context across sessions to deliver personalized responses.
- Customer Support with Historical Context: Provide support agents or bots immediate access to past conversation threads, summaries, and structured artifacts to resolve recurring or complex issues faster.
- Reducing Hallucinations in LLMs: Use embeddings, graph-aware retrieval, and structured context assembly to ground model responses in verifiable past interactions and business data.
- Temporal User Profiling: Track changing user attributes and preferences over time using the temporal knowledge graph to drive targeted recommendations and dynamic personalization.
- Agent State Tracking and Change History: Record state transitions with valid/invalid timestamps so agents can reason about when facts were true and how user situations evolved.
- Augmenting RAG Workflows: Improve retrieval-augmented generation by assembling relevant chat-derived context and document collections to include only what matters in prompts.
- Scaling Memory for Production: Persist conversation data to databases and use Zep Cloud for low-latency, scalable memory services in production AI applications.
- Personalized conversational agents that recall historical user interactions
- Reducing hallucinations by providing relevant past-context to LLM prompts
- RAG workflows combining chat memory and document vectors
- Customer support assistants that persist and search prior tickets/conversations
- Stateful agents that need to reason about temporal changes in user data or preferences
- Analytics and insights from long-term conversation archives
- Embedding-based semantic search over conversation content and summaries