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Comparison

ai-serving vs serve

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 serve if serve is a Python-based framework for building multimodal AI applications using a cloud-native stack, supporting deep learning, microservices, and orchestration.

Markdown twin · ai-serving alternatives · serve alternatives

GraphCanon updated Sep 20, 2026

5views this month

ai-serving logo

ai-serving

autodeployai/ai-serving

★ 166pushed Feb 24, 2026
vs
serve logo

serve

jina-ai/serve

★ 22kpushed Mar 24, 2025

Trust & integrity

Signalai-servingserve
Maintenance
Slowing (208d since push)
As of Sep 20, 2026 · github_public_v1
Dormant (542d since push)
As of Sep 18, 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 18, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of Jul 15, 2026 · osv@v1
No published findings from this source as of 2026-09-18
As of Sep 18, 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
ai-serving trust reportserve trust reportTrust methodology

Tagline

ai-serving
Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints
serve
Build multimodal AI applications with cloud-native stack

Stars

ai-serving
166
serve
22k

Forks

ai-serving
31
serve
2.2k

Open issues

ai-serving
3
serve
26

Language

ai-serving
Scala
serve
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.
serve
Serve is a Python-based framework for building multimodal AI applications using a cloud-native stack, supporting deep learning, microservices, and orchestration.

Persona

ai-serving
-
serve
-

Runtime

ai-serving
-
serve
-

License

ai-serving
Apache-2.0
serve
Serve is available under the Apache-2.0 license, allowing for free use, modification, and distribution.

Last pushed

ai-serving
Feb 24, 2026
serve
Mar 24, 2025

Categories

ai-serving
Inference & Serving
serve
Evaluation & Observability, Inference & Serving, Model Training

Trust and health

Maintenance

ai-serving
Slowing (36%)
serve
Dormant (18%)

Days since push

ai-serving
208d
serve
542d

Open issues (now)

ai-serving
3
serve
26

Stars delta

ai-serving
0 (30d)
serve
-2 (30d)

Open issues delta

ai-serving
0 (30d)
serve
-1 (30d)

OSV dependency advisories

ai-serving
No lockfile (source not queried)
serve
No published findings from this source as of 2026-09-18

Full report

ai-serving
Trust report
serve
Trust report

Choose ai-serving if…

  • ai-serving is primarily Scala; serve is Python.
  • Tags unique to ai-serving: ai-serving, inference-server, onnx, pmml-deployment.
  • 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.
GitHubDetails

Choose serve if…

  • serve is primarily Python; ai-serving is Scala.
  • Pricing: Serve is free to use, but additional services or support might be available for a fee..
  • Requirements: Min 4 GB RAM; Requires Docker; Installation requires Python and pip.; Supports Apple Silicon and Windows installations..
  • Tags unique to serve: cloud-native, cncf, deep-learning, docker.
  • Also covers Evaluation & Observability, Model Training.
  • Use Serve when you need to build applications that integrate multiple types of data, such as text, images, and audio, into a single AI application.

When NOT to use serve

  • Avoid Serve if your project does not require multimodal capabilities and you are looking for a simpler serving solution.
  • Do not use Serve if you are not comfortable with cloud-native technologies and prefer a more traditional deployment approach.
GitHubDetails

Explore

ai-serving trust report →serve trust report →Inference & Serving category →Evaluation & Observability category →Model Training category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: ai-serving 166 · serve 22k (synced Sep 20, 2026).

Common questions

What is the difference between ai-serving and serve?
ai-serving: Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints. serve: Build multimodal AI applications with cloud-native stack. See the comparison table for live GitHub stats and shared categories.
When should I choose ai-serving over serve?
Choose ai-serving over serve when ai-serving is primarily Scala; serve is Python; Tags unique to ai-serving: ai-serving, inference-server, onnx, pmml-deployment; When you need to serve models in both PMML and ONNX formats without manual configuration changes between formats.
When should I choose serve over ai-serving?
Choose serve over ai-serving when serve is primarily Python; ai-serving is Scala; Pricing: Serve is free to use, but additional services or support might be available for a fee.; Requirements: Min 4 GB RAM; Requires Docker; Installation requires Python and pip.; Supports Apple Silicon and Windows installations.; Tags unique to serve: cloud-native, cncf, deep-learning, docker; Also covers Evaluation & Observability, Model Training; Use Serve when you need to build applications that integrate multiple types of data, such as text, images, and audio, into a single AI application.
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 serve?
Avoid Serve if your project does not require multimodal capabilities and you are looking for a simpler serving solution. Do not use Serve if you are not comfortable with cloud-native technologies and prefer a more traditional deployment approach.
Is ai-serving or serve more popular on GitHub?
serve has more GitHub stars (21,861 vs 166). Stars measure visibility, not whether either tool fits your constraints.
Are ai-serving and serve open source?
Yes - both are open-source projects on GitHub (ai-serving: Apache-2.0, serve: Apache-2.0).
Where can I find alternatives to ai-serving or serve?
GraphCanon lists graph-backed alternatives at ai-serving alternatives and serve alternatives (ai-serving markdown twin, serve 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 serve?
ai-serving: Slowing. serve: 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 ai-serving and serve?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ai-serving trust report; serve trust report.

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