CleanSpeak vs LocalMod

CleanSpeak is a configurable moderation toolset for services that need to filter or review user content. It filters text, images, and video, and can moderate audio but does not filter it. Moderation queues let teams review and escalate material, while custom rules can automate actions by content source, rate users, and trigger actions from those ratings. Editable blocklists detect negative terms and phrases, including altered forms intended to bypass filters. The product includes 22+ base language lists that customers can change or supplement. Tunable text, image, and video models integrate Google Perspective and SightEngine; reports cover moderator activity, content searches, and term or model frequencies. CleanSpeak is available as managed cloud service or as server software installed and maintained by customers. Its synchronous REST APIs return JSON for real-time integration across content filtering, moderation, and user management. The company says one server can filter 10,000 or more messages per second. It serves use cases in apps and gaming, retail and hospitality, and enterprise settings. The advertised trial lasts 30 days and requires no credit card; pricing is available on request.

LocalMod is a free content moderation API for text and images that runs on Linux within a self-hosted setup. The project says it can run offline, keeping user data within the user's infrastructure. Text classifiers check for PII, toxicity, prompt injection, spam, and NSFW text; the PII classifier covers emails, phone numbers, Social Security numbers, and credit card numbers. Image moderation detects explicit or adult images. API endpoints support single and batch text analysis, PII redaction, and image analysis from a URL or uploaded file. The README documents Python use, running an API server, and Docker deployment, and includes an example Discord bot for real-time text and image moderation with auto-delete, timeouts, and logging. Offline operation requires downloading the models once and setting LOCALMOD_OFFLINE=1; the documented download is about 3.5 GB and includes the image model. The README recommends a GPU for production workloads and lists CPU and GPU latency estimates by classifier. LocalMod is MIT licensed.