Comparison
ai-serving vs sie
Verdict
Pick ai-serving if ai-Serving is an inference server supporting PMML and ONNX formats via HTTP or gRPC endpoints, easily deployable with Docker; pick sie if sie is an open-source inference server and production cluster for managing AI model deployment in various domains like NLP, deep learning, and more.
Markdown twin · ai-serving alternatives · sie alternatives
GraphCanon updated Sep 20, 2026
15views this month
vs
Trust & integrity
| Signal | ai-serving | sie |
|---|---|---|
| Maintenance | Slowing (208d since push) As of Sep 20, 2026 · github_public_v1 | Very active (1d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 15, 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
- ai-serving
- Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints
- sie
- Open-source inference server and production cluster for all the models your agent needs.
Stars
- ai-serving
- 166
- sie
- 3.3k
Forks
- ai-serving
- 31
- sie
- 311
Open issues
- ai-serving
- 3
- sie
- 18
Language
- ai-serving
- Scala
- sie
- Python
Adopt for
- ai-serving
- Ai-Serving is an inference server supporting PMML and ONNX formats via HTTP or gRPC endpoints, easily deployable with Docker.
- sie
- sie is an open-source inference server and production cluster for managing AI model deployment in various domains like NLP, deep learning, and more.
Persona
- ai-serving
- -
- sie
- -
Runtime
- ai-serving
- -
- sie
- -
License
- ai-serving
- Apache-2.0
- sie
- Apache-2.0
Last pushed
- ai-serving
- Feb 24, 2026
- sie
- Sep 19, 2026
Categories
- ai-serving
- Inference & Serving
- sie
- Inference & Serving
Trust and health
Maintenance
- ai-serving
- Slowing (36%)
- sie
- Very active (96%)
Days since push
- ai-serving
- 208d
- sie
- 1d
Open issues (now)
- ai-serving
- 3
- sie
- 18
Stars delta
- ai-serving
- 0 (30d)
- sie
- +996 (30d)
Open issues delta
- ai-serving
- 0 (30d)
- sie
- +7 (30d)
Full report
- ai-serving
- Trust report
- sie
- Trust report
Choose ai-serving if…
- ai-serving is primarily Scala; sie is Python.
- Tags unique to ai-serving: ai-serving, grpc, inference-server, onnx.
- When you need to serve models in both PMML and ONNX formats without manual configuration changes between formats.
When NOT to use ai-serving
- Avoid if your team lacks familiarity or willingness to use Scala for deployment through sbt build system for customization needs.
- Not suitable when only one model format, either PMML or ONNX but not both, is needed and a simpler solution would suffice.
- If your project strictly requires a non-Dockerized setup that does not align with using pre-built Docker images.
Choose sie if…
- sie is primarily Python; ai-serving is Scala.
- Requirements: sie operates under Python, necessitating a compatible runtime environment.; To fully leverage sie's capabilities, ensure your project aligns well with Apache-2.0 licensing requirements and practices..
- Tags unique to sie: bge, colbert, data-pipeline, deep-learning.
- Use sie when you need to deploy multiple types of ML models including deep-learning embeddings or retrieval-augmented generation systems.
When NOT to use sie
- Avoid using sie if your project strictly focuses on areas outside the machine learning and deep-learning scope that sie is designed to support.
- Do not choose sie for projects requiring proprietary or specialized backend services that might conflict with its open-source framework.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (autodeployai/ai-serving) · observed Sep 20, 2026
- GitHub forks (autodeployai/ai-serving) · observed Sep 20, 2026
- Last push (autodeployai/ai-serving) · observed Feb 24, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (superlinked/sie) · observed Sep 20, 2026
- GitHub forks (superlinked/sie) · observed Sep 20, 2026
- Last push (superlinked/sie) · observed Sep 19, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: ai-serving 166 · sie 3.3k (synced Sep 20, 2026).
Common questions
- What is the difference between ai-serving and sie?
- ai-serving: Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints. sie: Open-source inference server and production cluster for all the models your agent needs.. See the comparison table for live GitHub stats and shared categories.
- When should I choose ai-serving over sie?
- Choose ai-serving over sie when ai-serving is primarily Scala; sie is Python; Tags unique to ai-serving: ai-serving, grpc, inference-server, onnx; When you need to serve models in both PMML and ONNX formats without manual configuration changes between formats.
- When should I choose sie over ai-serving?
- Choose sie over ai-serving when sie is primarily Python; ai-serving is Scala; Requirements: sie operates under Python, necessitating a compatible runtime environment.; To fully leverage sie's capabilities, ensure your project aligns well with Apache-2.0 licensing requirements and practices.; Tags unique to sie: bge, colbert, data-pipeline, deep-learning; Use sie when you need to deploy multiple types of ML models including deep-learning embeddings or retrieval-augmented generation systems.
- When should I avoid ai-serving?
- Avoid if your team lacks familiarity or willingness to use Scala for deployment through sbt build system for customization needs. Not suitable when only one model format, either PMML or ONNX but not both, is needed and a simpler solution would suffice. If your project strictly requires a non-Dockerized setup that does not align with using pre-built Docker images.
- When should I avoid sie?
- Avoid using sie if your project strictly focuses on areas outside the machine learning and deep-learning scope that sie is designed to support. Do not choose sie for projects requiring proprietary or specialized backend services that might conflict with its open-source framework.
- Is ai-serving or sie more popular on GitHub?
- sie has more GitHub stars (3,293 vs 166). Stars measure visibility, not whether either tool fits your constraints.
- Are ai-serving and sie open source?
- Yes - both are open-source projects on GitHub (ai-serving: Apache-2.0, sie: Apache-2.0).
- Where can I find alternatives to ai-serving or sie?
- GraphCanon lists graph-backed alternatives at ai-serving alternatives and sie alternatives (ai-serving markdown twin, sie 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, ai-serving or sie?
- ai-serving: Slowing. sie: 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 ai-serving and sie?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ai-serving trust report; sie trust report.