--- title: "chroma vs stock-rnn" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/chroma-core-chroma-vs-lilianweng-stock-rnn" tools: ["chroma-core-chroma", "lilianweng-stock-rnn"] --- # chroma vs stock-rnn *GraphCanon updated Jul 11, 2026* ## Verdict Pick chroma when chroma is primarily Rust; stock-rnn is Python; pick stock-rnn when stock-rnn is primarily Python; chroma is Rust. [chroma](https://www.trychroma.com/) reports 29k GitHub stars, 2.4k forks, and 728 open issues, last pushed Jul 10, 2026. [stock-rnn](https://lilianweng.github.io/lil-log) has 2.0k stars, 673 forks, and 24 open issues, last pushed Jul 28, 2022. Figures are from public GitHub metadata via [chroma's repository](https://github.com/chroma-core/chroma) and [stock-rnn's repository](https://github.com/lilianweng/stock-rnn). | | [chroma](/tools/chroma-core-chroma.md) | [stock-rnn](/tools/lilianweng-stock-rnn.md) | | --- | --- | --- | | Tagline | Search infrastructure for AI | Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings. | | Stars | 28,763 | 1,976 | | Forks | 2,377 | 673 | | Open issues | 728 | 24 | | Language | Rust | Python | | Adopt for | Chroma is an open-source data infrastructure for AI designed to support vector, hybrid, and full-text search capabilities with high performance. | - | | Persona | - | - | | Runtime | - | - | | License | Chroma is released under the Apache 2.0 license. | - | | Categories | Data & Retrieval, Vector Databases | Model Training, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [chroma](/tools/chroma-core-chroma.md) | [stock-rnn](/tools/lilianweng-stock-rnn.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 1444d | | Open issues (now) | 728 | 24 | | Owner type | Organization | User | | Security scan | 8 low (8 low) | No lockfile | | Full report | [trust report](/tools/chroma-core-chroma/trust.md) | [trust report](/tools/lilianweng-stock-rnn/trust.md) | ## Decision facts: chroma - **Pricing:** freemium - The open-source version is free to use and modify; the hosted service (Chroma Cloud) has a freemium model offering $5 of initial credits. - **Requirements:** Min 1 GB RAM - **Adopt for:** Chroma is an open-source data infrastructure for AI designed to support vector, hybrid, and full-text search capabilities with high performance. - **License detail:** Chroma is released under the Apache 2.0 license. ## Choose when ### Choose chroma if… - chroma is primarily Rust; stock-rnn is Python. - Pricing: The open-source version is free to use and modify; the hosted service (Chroma Cloud) has a freemium model offering $5 of initial credits.. - Requirements: Min 1 GB RAM. - Tags unique to chroma: agents, ai-agents, database, full-text-search. - Also covers Data & Retrieval. - - When you require a high-performance data infrastructure that can handle complex query needs for AI applications. - If your project necessitates fast, cost-effective, and scalable serverless services ### Choose stock-rnn if… - stock-rnn is primarily Python; chroma is Rust. - Tags unique to stock-rnn: embeddings, lstm, python, rnn-tensorflow. - Also covers Model Training. ## When NOT to use chroma - - In scenarios where a more mature or enterprise-grade solution is required, as Chroma might be rapidly evolving and not yet fully stabilized. - - If your project requires extensive customization at the lower levels that the relatively new tool might not support comprehensively yet - - When the specific need for an AI application does not benefit from vector, hybrid, or full-text search capabilities that Chroma excels in. ## When NOT to use stock-rnn - Last GitHub push was 1445 days ago (dormant maintenance, Jul 28, 2022). Validate activity before betting a new project on stock-rnn. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## Common questions ### What is the difference between chroma and stock-rnn? chroma: Search infrastructure for AI. stock-rnn: Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.. See the comparison table for live GitHub stats and shared categories. ### When should I choose chroma over stock-rnn? Choose chroma over stock-rnn when chroma is primarily Rust; stock-rnn is Python; Pricing: The open-source version is free to use and modify; the hosted service (Chroma Cloud) has a freemium model offering $5 of initial credits.; Requirements: Min 1 GB RAM; Tags unique to chroma: agents, ai-agents, database, full-text-search; Also covers Data & Retrieval; - When you require a high-performance data infrastructure that can handle complex query needs for AI applications. - If your project necessitates fast, cost-effective, and scalable serverless services. ### When should I choose stock-rnn over chroma? Choose stock-rnn over chroma when stock-rnn is primarily Python; chroma is Rust; Tags unique to stock-rnn: embeddings, lstm, python, rnn-tensorflow; Also covers Model Training. ### When should I avoid chroma? - In scenarios where a more mature or enterprise-grade solution is required, as Chroma might be rapidly evolving and not yet fully stabilized. - If your project requires extensive customization at the lower levels that the relatively new tool might not support comprehensively yet - When the specific need for an AI application does not benefit from vector, hybrid, or full-text search capabilities that Chroma excels in. ### When should I avoid stock-rnn? Last GitHub push was 1445 days ago (dormant maintenance, Jul 28, 2022). Validate activity before betting a new project on stock-rnn. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### Is chroma or stock-rnn more popular on GitHub? chroma has more GitHub stars (28,763 vs 1,976). Stars measure visibility, not whether either tool fits your constraints. ### Are chroma and stock-rnn open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to chroma or stock-rnn? GraphCanon lists graph-backed alternatives at [chroma alternatives](/tools/chroma-core-chroma/alternatives) and [stock-rnn alternatives](/tools/lilianweng-stock-rnn/alternatives) ([chroma markdown twin](/tools/chroma-core-chroma/alternatives.md), [stock-rnn markdown twin](/tools/lilianweng-stock-rnn/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/chroma-core-chroma-vs-lilianweng-stock-rnn.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, chroma or stock-rnn? chroma: Very active. stock-rnn: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for chroma and stock-rnn? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [chroma trust report](/tools/chroma-core-chroma/trust); [stock-rnn trust report](/tools/lilianweng-stock-rnn/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=chroma-core-chroma`](/api/graphcanon/graph?tool=chroma-core-chroma) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_