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Comparison

ai-serving vs budgetml

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 budgetml if budgetML is a Python library that leverages Google Cloud Preemptible instances to decrease the cost of deploying machine learning models for inference.

Markdown twin · ai-serving alternatives · budgetml alternatives

GraphCanon updated 5d

ai-serving logo

ai-serving

autodeployai/ai-serving

★ 166pushed Feb 24, 2026
vs
budgetml logo

budgetml

ebhy/budgetml

★ 1.3kpushed Feb 12, 2024

Trust & integrity

Signalai-servingbudgetml
Maintenance
Slowing (171d since push)
As of 5d · github_public_v1
Dormant (901d since push)
As of 2w · github_public_v1
Provenance
Not a fork · Organization account
As of 5d · github_public_v1
Not a fork · Organization account
As of 2w · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of 1mo · osv@v1
No published findings from this source as of 2026-07-11
As of 1mo · 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 reportbudgetml trust reportTrust methodology

Tagline

ai-serving
Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints
budgetml
Deploys ML inference service economically

Stars

ai-serving
166
budgetml
1.3k

Forks

ai-serving
31
budgetml
65

Open issues

ai-serving
3
budgetml
4

Language

ai-serving
Scala
budgetml
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.
budgetml
BudgetML is a Python library that leverages Google Cloud Preemptible instances to decrease the cost of deploying machine learning models for inference.

Persona

ai-serving
-
budgetml
-

Runtime

ai-serving
-
budgetml
-

License

ai-serving
Apache-2.0
budgetml
The tool is distributed under the Apache-2.0 license, allowing for free use in both open source and commercial projects.

Last pushed

ai-serving
Feb 24, 2026
budgetml
Feb 12, 2024

Categories

ai-serving
Inference & Serving
budgetml
Inference & Serving

Trust and health

Maintenance

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

Days since push

ai-serving
171d
budgetml
901d

Open issues (now)

ai-serving
3
budgetml
4

Stars delta

ai-serving
0 (30d)
budgetml
Unknown

Open issues delta

ai-serving
0 (30d)
budgetml
Unknown

OSV dependency advisories

ai-serving
No lockfile (source not queried)
budgetml
No published findings from this source as of 2026-07-11

Full report

ai-serving
Trust report
budgetml
Trust report

Choose ai-serving if…

  • ai-serving is primarily Scala; budgetml 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.
GitHubDetails

Choose budgetml if…

  • budgetml is primarily Python; ai-serving is Scala.
  • Pricing: Free to use, but users will incur costs based on their usage of Google Cloud Preemptible instances..
  • Requirements: Requires a working Python environment and access to Google Cloud services to deploy on Preemptible VMs; The library is available via PyPI or can be installed directly from GitHub for the latest features, at users' own risk.
  • Tags unique to budgetml: api, data-science, deployment, fastapi.
  • When you are looking to reduce costs significantly and have flexibility in your deployment schedule, since Preemptible instances can be interrupted

When NOT to use budgetml

  • If you need absolute certainty that your ML service will not be interrupted at any point during operation
  • Not suitable for continuous and uninterrupted services, as Google Cloud Preemptible instances can be terminated with short notice
GitHubDetails

Explore

ai-serving trust report →budgetml trust report →Inference & Serving 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 · budgetml 1.3k (synced Aug 14, 2026).

Common questions

What is the difference between ai-serving and budgetml?
ai-serving: Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints. budgetml: Deploys ML inference service economically. See the comparison table for live GitHub stats and shared categories.
When should I choose ai-serving over budgetml?
Choose ai-serving over budgetml when ai-serving is primarily Scala; budgetml 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 budgetml over ai-serving?
Choose budgetml over ai-serving when budgetml is primarily Python; ai-serving is Scala; Pricing: Free to use, but users will incur costs based on their usage of Google Cloud Preemptible instances.; Requirements: Requires a working Python environment and access to Google Cloud services to deploy on Preemptible VMs; The library is available via PyPI or can be installed directly from GitHub for the latest features, at users' own risk; Tags unique to budgetml: api, data-science, deployment, fastapi; When you are looking to reduce costs significantly and have flexibility in your deployment schedule, since Preemptible instances can be interrupted.
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 budgetml?
If you need absolute certainty that your ML service will not be interrupted at any point during operation Not suitable for continuous and uninterrupted services, as Google Cloud Preemptible instances can be terminated with short notice
Is ai-serving or budgetml more popular on GitHub?
budgetml has more GitHub stars (1,343 vs 166). Stars measure visibility, not whether either tool fits your constraints.
Are ai-serving and budgetml open source?
Yes - both are open-source projects on GitHub (ai-serving: Apache-2.0, budgetml: Apache-2.0).
Where can I find alternatives to ai-serving or budgetml?
GraphCanon lists graph-backed alternatives at ai-serving alternatives and budgetml alternatives (ai-serving markdown twin, budgetml 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 budgetml?
ai-serving: Slowing. budgetml: 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 budgetml?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ai-serving trust report; budgetml trust report.

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