CrbonFree vs Maximem Synap: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of CrbonFree and Maximem Synap — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
CrbonFree
Crbon Labs Inc.
CrbonFree meters the energy and carbon of every AI token across providers, with published factors, uncertainty bands and audit-ready retirement receipts.
Key features
- Per-Token Carbon Metering: Counts every token by model and provider and converts it to energy, CO2e and water using versioned, published factors.
- Multi-Provider Coverage: Connects OpenAI, Anthropic, OpenRouter, AWS Bedrock, Google Vertex AI, Azure Foundry, Vercel and Cloudflare AI Gateways into one report.
- Many Ingestion Surfaces: Read-only provider keys, TypeScript and Python SDKs, an OAuth-secured remote MCP server, a CLI for local coding agents and a Chrome/Edge extension for ChatGPT, Gemini and Claude.
- Layered Methodology with Uncertainty: Reports model energy, data-centre overhead and embodied hardware/training carbon separately, each with a ±28.3% uncertainty band.
- Metadata-Only Privacy: Receives only model, token counts and timestamps — prompt and completion content never leaves your systems.
- Signed Retirement Receipts: Paid plans issue receipts listing credit serial numbers on the American Carbon Registry or BCarbon Registry, publicly verifiable.
- Monthly Audit Packs: Zip exports with a manifest hash plus badges and certificates to back published climate claims.
- Dashboard & REST API: One view of tokens, cost and carbon across projects with CSV/JSON export and twelve read endpoints with an OpenAPI spec.
Best for
- Scope 3 Reporting: Sustainability teams produce defensible AI emissions figures for disclosures instead of spend-based guesses.
- Engineering Carbon Visibility: Track the footprint of coding agents like Claude Code across a dev team via the CLI.
- Model Selection by Footprint: Compare energy and carbon per provider and model tier when choosing models for production workloads.
- Carbon Offsetting with Proof: Retire credits against measured AI usage and share verifiable receipts with auditors or customers.
- Board and Auditor Requests: Hand auditors monthly audit packs with verifiable hashes when asked for AI emissions data.
- Personal Chat Usage Tracking: Individuals estimate the footprint of their ChatGPT, Gemini and Claude sessions with the browser extension.
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.