Comparison
aikit vs private-gpt
Verdict
Pick aikit if aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies; 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 · aikit alternatives · private-gpt alternatives
GraphCanon updated Sep 20, 2026
13views this month
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Trust & integrity
| Signal | aikit | private-gpt |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 19, 2026 · github_public_v1 | Very active (0d since push) As of Sep 8, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 19, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 8, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- aikit
- Fine-tune, build, and deploy open-source LLMs easily!
- private-gpt
- Complete API layer for private AI applications on local models
Stars
- aikit
- 539
- private-gpt
- 57k
Forks
- aikit
- 57
- private-gpt
- 7.6k
Open issues
- aikit
- 37
- private-gpt
- 8
Language
- aikit
- Go
- private-gpt
- Python
Adopt for
- aikit
- Aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies.
- 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
- aikit
- -
- private-gpt
- -
Runtime
- aikit
- -
- private-gpt
- -
License
- aikit
- MIT
- private-gpt
- Apache-2.0
Last pushed
- aikit
- Sep 18, 2026
- private-gpt
- Sep 7, 2026
Categories
- aikit
- Inference & Serving, LLM Frameworks, Model Training
- private-gpt
- Inference & Serving
Trust and health
Open issues (now)
- aikit
- 37
- private-gpt
- 8
Stars delta
- aikit
- +5 (30d)
- private-gpt
- +82 (30d)
Open issues delta
- aikit
- -6 (30d)
- private-gpt
- +5 (30d)
Full report
- aikit
- Trust report
- private-gpt
- Trust report
Choose aikit if…
- aikit is primarily Go; private-gpt is Python.
- License: aikit is MIT, private-gpt is Apache-2.0.
- Tags unique to aikit: buildkit, chatgpt, docker, fine-tuning.
- Also covers LLM Frameworks, Model Training.
- - You need a flexible solution specifically built using Go and prefer its concurrency model.
When NOT to use aikit
- - You have a preference or requirement for Python-based tools due to the lack of native support in Aikit.
- - If your deployment setup strictly uses cloud-specific platforms and you do not use Kubernetes or Docker, as Aikit heavily integrates with containerized environments like these.
Choose private-gpt if…
- private-gpt is primarily Python; aikit is Go.
- License: private-gpt is Apache-2.0, aikit is MIT.
- Requirements: Min 8 GB RAM; Requires Docker.
- Tags unique to private-gpt: ai-tools, local-models, mcp, on-premise.
- - 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 (kaito-project/aikit) · observed Sep 20, 2026
- GitHub forks (kaito-project/aikit) · observed Sep 20, 2026
- Last push (kaito-project/aikit) · observed Sep 18, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (zylon-ai/private-gpt) · observed Sep 20, 2026
- GitHub forks (zylon-ai/private-gpt) · observed Sep 20, 2026
- Last push (zylon-ai/private-gpt) · observed Sep 7, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: aikit 539 · private-gpt 57k (synced Sep 20, 2026).
Common questions
- What is the difference between aikit and private-gpt?
- aikit: Fine-tune, build, and deploy open-source LLMs easily!. 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 aikit over private-gpt?
- Choose aikit over private-gpt when aikit is primarily Go; private-gpt is Python; License: aikit is MIT, private-gpt is Apache-2.0; Tags unique to aikit: buildkit, chatgpt, docker, fine-tuning; Also covers LLM Frameworks, Model Training; - You need a flexible solution specifically built using Go and prefer its concurrency model.
- When should I choose private-gpt over aikit?
- Choose private-gpt over aikit when private-gpt is primarily Python; aikit is Go; License: private-gpt is Apache-2.0, aikit is MIT; Requirements: Min 8 GB RAM; Requires Docker; Tags unique to private-gpt: ai-tools, local-models, mcp, on-premise; - You need to deploy and operationalize your own locally-run models without relying on cloud APIs.
- When should I avoid aikit?
- - You have a preference or requirement for Python-based tools due to the lack of native support in Aikit. - If your deployment setup strictly uses cloud-specific platforms and you do not use Kubernetes or Docker, as Aikit heavily integrates with containerized environments like these.
- 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 aikit or private-gpt more popular on GitHub?
- private-gpt has more GitHub stars (57,497 vs 539). Stars measure visibility, not whether either tool fits your constraints.
- Are aikit and private-gpt open source?
- Yes - both are open-source projects on GitHub (aikit: MIT, private-gpt: Apache-2.0).
- Where can I find alternatives to aikit or private-gpt?
- GraphCanon lists graph-backed alternatives at aikit alternatives and private-gpt alternatives (aikit 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, aikit or private-gpt?
- aikit: Very active. 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 aikit and private-gpt?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: aikit trust report; private-gpt trust report.