Comparison
ai-serving vs Server
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 Server if server is a standalone HTTP-based inference server for deploying Rubix ML estimators using PHP.
Markdown twin · ai-serving alternatives · Server alternatives
GraphCanon updated Sep 20, 2026
12views this month
vs
Trust & integrity
| Signal | ai-serving | Server |
|---|---|---|
| Maintenance | Slowing (208d since push) As of Sep 20, 2026 · github_public_v1 | Slowing (201d 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 15, 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
- Server
- Standalone inference server for Rubix ML estimators.
Stars
- ai-serving
- 166
- Server
- 63
Forks
- ai-serving
- 31
- Server
- 13
Open issues
- ai-serving
- 3
- Server
- 1
Language
- ai-serving
- Scala
- Server
- PHP
Adopt for
- ai-serving
- Ai-Serving is an inference server supporting PMML and ONNX formats via HTTP or gRPC endpoints, easily deployable with Docker.
- Server
- Server is a standalone HTTP-based inference server for deploying Rubix ML estimators using PHP.
Persona
- ai-serving
- -
- Server
- -
Runtime
- ai-serving
- -
- Server
- -
License
- ai-serving
- Apache-2.0
- Server
- MIT
Last pushed
- ai-serving
- Feb 24, 2026
- Server
- Mar 3, 2026
Categories
- ai-serving
- Inference & Serving
- Server
- Inference & Serving
Trust and health
Days since push
- ai-serving
- 208d
- Server
- 201d
Open issues (now)
- ai-serving
- 3
- Server
- 1
Full report
- ai-serving
- Trust report
- Server
- Trust report
Choose ai-serving if…
- ai-serving is primarily Scala; Server is PHP.
- License: ai-serving is Apache-2.0, Server is MIT.
- 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 Server if…
- Server is primarily PHP; ai-serving is Scala.
- License: Server is MIT, ai-serving is Apache-2.0.
- Tags unique to Server: api, http-server, inference-engine, infrastructure.
- When you are working with machine learning models trained in the Rubix ML framework and need to deploy them via a PHP-based infrastructure.
When NOT to use Server
- Avoid using if your primary technology stack is not based on PHP, as it would necessitate integration with a non-native language environment, increasing complexity.
- Do not use this tool for large-scale deployments requiring high throughput and low latency typical of more robust languages like Python or Rust.
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 (RubixML/Server) · observed Sep 20, 2026
- GitHub forks (RubixML/Server) · observed Sep 20, 2026
- Last push (RubixML/Server) · observed Mar 3, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: ai-serving 166 · Server 63 (synced Sep 20, 2026).
Common questions
- What is the difference between ai-serving and Server?
- ai-serving: Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints. Server: Standalone inference server for Rubix ML estimators.. See the comparison table for live GitHub stats and shared categories.
- When should I choose ai-serving over Server?
- Choose ai-serving over Server when ai-serving is primarily Scala; Server is PHP; License: ai-serving is Apache-2.0, Server is MIT; 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 Server over ai-serving?
- Choose Server over ai-serving when Server is primarily PHP; ai-serving is Scala; License: Server is MIT, ai-serving is Apache-2.0; Tags unique to Server: api, http-server, inference-engine, infrastructure; When you are working with machine learning models trained in the Rubix ML framework and need to deploy them via a PHP-based infrastructure.
- 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 Server?
- Avoid using if your primary technology stack is not based on PHP, as it would necessitate integration with a non-native language environment, increasing complexity. Do not use this tool for large-scale deployments requiring high throughput and low latency typical of more robust languages like Python or Rust.
- Is ai-serving or Server more popular on GitHub?
- ai-serving has more GitHub stars (166 vs 63). Stars measure visibility, not whether either tool fits your constraints.
- Are ai-serving and Server open source?
- Yes - both are open-source projects on GitHub (ai-serving: Apache-2.0, Server: MIT).
- Where can I find alternatives to ai-serving or Server?
- GraphCanon lists graph-backed alternatives at ai-serving alternatives and Server alternatives (ai-serving markdown twin, Server 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 Server?
- ai-serving: Slowing. Server: Slowing. 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 Server?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ai-serving trust report; Server trust report.