Contextual
Freemium
Individual developers managing local codebases
TrueFoundry's Cognita is a free, self-hosted RAG framework for teams building document-search-powered AI applications without vendor lock-in.
$25/user/month · Free tier: Up to 3 users, 10K requests & tool calls / user / month, 7-day log retention · Checked Oct 19, 2026
Cognita lets data science and platform engineering teams assemble retrieval-augmented generation systems by connecting pluggable components—vector databases, embedding models, rerankers—without writing infrastructure code. The problem it solves: production RAG deployment is fragmented across commercial APIs and open-source libraries, forcing teams to choose between convenience and control. The headline differentiator is its ability to run entirely via docker-compose on local infrastructure while hosting multiple RAG instances in a single application, making it viable for organizations with sensitive document collections. It's free. The honest catch: you're limited to Qdrant or SingleStore for vector storage today, and production use requires standing up separate LLM and embedding model services yourself—so the 'free' price tag assumes you can manage containerized infrastructure.
Bottom line: *Use this if your team can containerize infrastructure and needs to avoid cloud APIs for data; skip it if you want a fully managed SaaS solution.*
For small teams prototyping AI workflows
For teams shipping AI to production
For organizations running AI at scale
View full pricing on truefoundry.com →
Pricing may have changed since last verified. Check the official site for current plans.
Find your fit
More options in RAG Frameworks. Compare pricing, platforms, and features side by side.
Freemium
Individual developers managing local codebases
Freemium
Teams needing deterministic rules plus RAG explanations
TrueFoundry's Cognita is a free, self-hosted RAG framework for teams building document-search-powered AI applications without vendor lock-in.
Cognita lets data science and platform engineering teams assemble retrieval-augmented generation systems by connecting pluggable components—vector databases, embedding models, rerankers—without writing infrastructure code. The problem it solves: production RAG deployment is fragmented across commercial APIs and open-source libraries, forcing teams to choose between convenience and control. The headline differentiator is its ability to run entirely via docker-compose on local infrastructure while hosting multiple RAG instances in a single application, making it viable for organizations with sensitive document collections. It's free. The honest catch: you're limited to Qdrant or SingleStore for vector storage today, and production use requires standing up separate LLM and embedding model services yourself—so the 'free' price tag assumes you can manage containerized infrastructure.
*Use this if your team can containerize infrastructure and needs to avoid cloud APIs for data; skip it if you want a fully managed SaaS solution.*
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