Comparison
embedbase vs private-gpt
Verdict
Pick embedbase if embedbase is a TypeScript-based API designed to facilitate the creation of Large Language Model (LLM) powered applications via integrations with embeddings and vector databases; pick private-gpt if privateGPT provides a comprehensive API layer to build private, on-premise AI applications leveraging local OpenAI-compatible inference servers. It offers features such as RAG, skills, tools, text-to-SQL functionalities,.
Markdown twin · embedbase alternatives · private-gpt alternatives
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Trust & integrity
| Signal | embedbase | private-gpt |
|---|---|---|
| Maintenance | Dormant (590d since push) As of 6d · github_public_v1 | Very active (0d since push) As of 2d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 6d · github_public_v1 | Not a fork · Organization account As of 2d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 6d · osv@v1 | No lockfile (source not queried) As of 6d · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- embedbase
- A dead-simple API to build LLM-powered apps
- private-gpt
- Complete API layer for private AI applications on local models
Stars
- embedbase
- 524
- private-gpt
- 57k
Forks
- embedbase
- 55
- private-gpt
- 7.6k
Open issues
- embedbase
- 35
- private-gpt
- 7
Language
- embedbase
- TypeScript
- private-gpt
- Python
Adopt for
- embedbase
- Embedbase is a TypeScript-based API designed to facilitate the creation of Large Language Model (LLM) powered applications via integrations with embeddings and vector databases.
- private-gpt
- PrivateGPT provides a comprehensive API layer to build private, on-premise AI applications leveraging local OpenAI-compatible inference servers. It offers features such as RAG, skills, tools, text-to-SQL functionalities,
Persona
- embedbase
- -
- private-gpt
- -
Runtime
- embedbase
- -
- private-gpt
- -
License
- embedbase
- MIT
- private-gpt
- Apache-2.0
Last pushed
- embedbase
- Nov 27, 2024
- private-gpt
- Jul 14, 2026
Categories
- embedbase
- Data & Retrieval, Vector Databases
- private-gpt
- Inference & Serving
Trust and health
Maintenance
- embedbase
- Dormant (18%)
- private-gpt
- Very active (96%)
Days since push
- embedbase
- 590d
- private-gpt
- 0d
Open issues (now)
- embedbase
- 35
- private-gpt
- 7
Full report
- embedbase
- Trust report
- private-gpt
- Trust report
Typed relationship
embedbase alternative private-gptPrivateGPT and embedbase both provide APIs to integrate LLM-powered capabilities into applications, offering alternative methods of achieving similar goals.
Choose embedbase if…
- embedbase is primarily TypeScript; private-gpt is Python.
- License: embedbase is MIT, private-gpt is Apache-2.0.
- PrivateGPT and embedbase both provide APIs to integrate LLM-powered capabilities into applications, offering alternative methods of achieving similar goals.
- Tags unique to embedbase: artificial-intelligence, chatgpt, embeddings, machine-learning.
- Also covers Data & Retrieval, Vector Databases.
- * Use Embedbase if you require direct integration capabilities specifically designed for embeddings and vector databases, like pgvector or Supabase.
When NOT to use embedbase
- * Avoid using Embedbase if your application's technology stack cannot effectively integrate TypeScript, as its primary language support is in this framework and not others like Python.
- * Do not use it when you need extensive customization options for the vector database configurations beyond what pgvector or Supabase offers.
Choose private-gpt if…
- private-gpt is primarily Python; embedbase is TypeScript.
- License: private-gpt is Apache-2.0, embedbase is MIT.
- Requirements: Min 8 GB RAM; Requires Docker.
- PrivateGPT and embedbase both provide APIs to integrate LLM-powered capabilities into applications, offering alternative methods of achieving similar goals.
- Tags unique to private-gpt: ai-tools, local-models, mcp, on-premise.
- Also covers Inference & Serving.
- private-gpt ships Docker support for self-hosted deployment.
- - You need to deploy and operationalize your own locally-run models without relying on cloud APIs.
When NOT to use private-gpt
- - You prefer simplicity and ease-of-use over full control; PrivateGPT requires more setup than using direct cloud-based AI services.
- - Your project does not involve running models locally but strictly relies on public cloud resources for inference server operations.
- - You do not have the technical capability to run an OpenAI-compatible inference server or manage local infrastructure effectively.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (different-ai/embedbase) · observed Jul 11, 2026
- GitHub forks (different-ai/embedbase) · observed Jul 11, 2026
- Last push (different-ai/embedbase) · observed Nov 27, 2024
- License file (MIT) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (zylon-ai/private-gpt) · observed Jul 15, 2026
- GitHub forks (zylon-ai/private-gpt) · observed Jul 15, 2026
- Last push (zylon-ai/private-gpt) · observed Jul 14, 2026
- License file (Apache-2.0) · observed Jul 15, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: embedbase 524 · private-gpt 57k (synced Jul 11, 2026).
Common questions
- What is the difference between embedbase and private-gpt?
- embedbase: A dead-simple API to build LLM-powered apps. private-gpt: Complete API layer for private AI applications on local models. See the comparison table for live GitHub stats and shared categories.
- When should I choose embedbase over private-gpt?
- Choose embedbase over private-gpt when embedbase is primarily TypeScript; private-gpt is Python; License: embedbase is MIT, private-gpt is Apache-2.0; PrivateGPT and embedbase both provide APIs to integrate LLM-powered capabilities into applications, offering alternative methods of achieving similar goals; Tags unique to embedbase: artificial-intelligence, chatgpt, embeddings, machine-learning; Also covers Data & Retrieval, Vector Databases; * Use Embedbase if you require direct integration capabilities specifically designed for embeddings and vector databases, like pgvector or Supabase.
- When should I choose private-gpt over embedbase?
- Choose private-gpt over embedbase when private-gpt is primarily Python; embedbase is TypeScript; License: private-gpt is Apache-2.0, embedbase is MIT; Requirements: Min 8 GB RAM; Requires Docker; PrivateGPT and embedbase both provide APIs to integrate LLM-powered capabilities into applications, offering alternative methods of achieving similar goals; Tags unique to private-gpt: ai-tools, local-models, mcp, on-premise; Also covers Inference & Serving; private-gpt ships Docker support for self-hosted deployment; - You need to deploy and operationalize your own locally-run models without relying on cloud APIs.
- When should I avoid embedbase?
- * Avoid using Embedbase if your application's technology stack cannot effectively integrate TypeScript, as its primary language support is in this framework and not others like Python. * Do not use it when you need extensive customization options for the vector database configurations beyond what pgvector or Supabase offers.
- When should I avoid private-gpt?
- - You prefer simplicity and ease-of-use over full control; PrivateGPT requires more setup than using direct cloud-based AI services. - Your project does not involve running models locally but strictly relies on public cloud resources for inference server operations. - You do not have the technical capability to run an OpenAI-compatible inference server or manage local infrastructure effectively.
- Is embedbase or private-gpt more popular on GitHub?
- private-gpt has more GitHub stars (57,328 vs 524). Stars measure visibility, not whether either tool fits your constraints.
- Are embedbase and private-gpt open source?
- Yes - both are open-source projects on GitHub (embedbase: MIT, private-gpt: Apache-2.0).
- Where can I find alternatives to embedbase or private-gpt?
- GraphCanon lists graph-backed alternatives at embedbase alternatives and private-gpt alternatives (embedbase markdown twin, private-gpt markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, embedbase or private-gpt?
- embedbase: Dormant. private-gpt: Very active. 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 embedbase and private-gpt?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: embedbase trust report; private-gpt trust report.