CrbonFree vs Koreshield: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of CrbonFree and Koreshield — 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.
Koreshield
Koreshield Labs Ltd.
Runtime trust layer that scans customer input, retrieved context and proposed tool calls in AI support workflows before they become trusted instructions.
Key features
- Customer Input Scanning: Inspects messages, attachments and externally controlled text before model execution so injected instructions never reach the model as trusted input.
- Retrieved Context Screening: Keeps poisoned tickets, CRM notes and RAG documents from inheriting system-level authority inside the prompt.
- Proposed Action Evaluation: Checks tool calls against trust, approval and authorization limits before they are executed.
- Detect and Enforce Modes: Run in detect mode to record threats without interrupting traffic, then enable enforcement only where fallback behavior is ready.
- Decision Evidence Trail: Records severity, decision and reason for every request, retained for 7 or 30 days depending on plan, for review and audits.
- Scan API with Key Management: Server-side API keys and endpoints such as /v1/rag/scan make integration with existing support stacks straightforward.
- Flexible Deployment: Use the hosted console or run the FastAPI + PostgreSQL service in your own infrastructure, with VPC, self-hosted and air-gapped options for Enterprise.
Best for
- Prompt Injection Defense: Blocking malicious instructions hidden in customer messages before an AI support agent acts on them.
- RAG Poisoning Protection: Preventing tampered knowledge-base articles or ticket histories from overriding system prompts.
- Agent Action Guardrails: Holding refunds, account changes or other tool calls for policy review when trust limits are exceeded.
- Safe Production Rollout: Measuring real threat and false-positive rates in detect mode before enforcing on live traffic.
- Security Audits: Exporting decision evidence to show auditors and security teams why each request was allowed or held.