Hopscotch vs Maximem Synap: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Hopscotch and Maximem Synap — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Hopscotch
Hopscotch Labs
OpenAI-compatible LLM gateway giving one API and one balance for 500+ models, with fallbacks and spend limits per key, teammate and workspace.
Key features
- Unified OpenAI-Compatible API: Call 500+ models from Anthropic, OpenAI, Google and others by changing only the base URL, API key and model name in the OpenAI SDK.
- Routing Profiles and Fallbacks: Define an ordered list of backup models so requests fail over automatically when a provider errors or returns a 429.
- Spend Limits: Set resetting credit limits per API key, monthly limits per teammate and workspace, and a cap on how fast the account can spend.
- Activity Log and Usage Analytics: See every request with serving provider, outcome, tokens, cost and latency, export to CSV, and break spend down by model, key and teammate.
- Model Catalog with Upstream Pricing: Each model lists context window and per-million-token price, including multiple upstream providers for open-weight models.
- Side-by-Side Playground: Run one prompt on up to three models and compare answers and cost before changing code.
- Bring Your Own Keys: Use your own provider keys free with no monthly cap alongside Hopscotch credit.
Best for
- Multi-Provider Apps: Letting an application switch between Claude, GPT and Gemini without maintaining separate SDKs, accounts or bills.
- Resilient Production Traffic: Automatically falling back to another model when a primary provider is rate-limited or down.
- Team Budget Governance: Giving each developer or environment its own key with a spend ceiling so costs cannot run away.
- Model Evaluation: Comparing output quality and cost of several models on real prompts in the playground before switching.
- Cost Auditing: Exporting per-request logs to attribute LLM spend by model, provider, key and teammate.
Maximem Synap
Maximem
Maximem Synap is an agentic context management platform that gives AI agents persistent, scoped memory with sub-15ms retrieval.
Key features
- Two-Call Memory API: Record messages asynchronously and fetch ranked, prompt-ready context before each reply — writes never block the agent.
- Three-Layer Memory Model: Separate organizational, long-term per-user and short-term session memory with customer and client scoping for multi-tenant isolation.
- Entity Resolution & Structured Extraction: Converts raw dialogue into structured facts and merges aliases of the same person or company automatically.
- Anticipatory Retrieval: Pre-fetches context during the conversation for under 15ms P75 retrieval, fast enough for voice agents.
- Agentic Compaction & Consolidation: Background cycles compact and consciously forget stale information to keep context lean and cut token costs.
- Hybrid Vector, Graph & File Storage: Retrieval nets across semantic vectors, entity relationship graphs and raw documents, with FAST/ACCURATE/HYBRID modes.
- Broad Framework Support: Python and TypeScript SDKs, REST and WebSocket APIs, a hosted MCP endpoint and native adapters for 23 frameworks including LangChain, CrewAI, OpenAI Agents and Claude Agent SDK.
- Enterprise Controls: BYOK model providers, audit trails, webhooks, custom memory schemas, and VPC deployment, SSO/SAML and RBAC on enterprise plans.
Best for
- Personalized Support Agents: Customer service bots remember prior issues and preferences across sessions instead of re-asking users.
- Voice Agents: Real-time voice assistants pull user context in milliseconds without conversational pauses.
- Multi-Tenant SaaS Assistants: B2B products keep each customer's memory isolated while sharing organization-wide policies and product facts.
- Token Cost Reduction: Teams replace full-history prompt stuffing with compact ranked context to lower LLM spend.
- Long-Running Copilots: Coding or productivity assistants recall what a user worked on days or weeks earlier.
- Replacing Homegrown RAG Memory: Engineering teams avoid building and maintaining their own extraction, vector DB and ranking pipeline.