Comparison
budgetml vs serve
Verdict
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; pick serve if serve offers dedicated support for deploying and scaling PyTorch models with features tailored towards large language model deployment, such as integration with Hugging Face.
Markdown twin · budgetml alternatives · serve alternatives
GraphCanon updated 3w
Trust & integrity
| Signal | budgetml | serve |
|---|---|---|
| Maintenance | Dormant (901d since push) As of 3w · github_public_v1 | Archived (360d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 3w · github_public_v1 |
| OSV dependency advisories | No published findings from this source as of 2026-07-11 As of 1mo · osv@v1 | No lockfile (source not queried) 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 |
Tagline
- budgetml
- Deploys ML inference service economically
- serve
- Serve, optimize and scale PyTorch models in production
Stars
- budgetml
- 1.3k
- serve
- 4.3k
Forks
- budgetml
- 65
- serve
- 882
Open issues
- budgetml
- 4
- serve
- 443
Language
- budgetml
- Python
- serve
- Java
Adopt for
- budgetml
- BudgetML is a Python library that leverages Google Cloud Preemptible instances to decrease the cost of deploying machine learning models for inference.
- serve
- Serve offers dedicated support for deploying and scaling PyTorch models with features tailored towards large language model deployment, such as integration with Hugging Face.
Persona
- budgetml
- -
- serve
- -
Runtime
- budgetml
- -
- serve
- -
License
- budgetml
- The tool is distributed under the Apache-2.0 license, allowing for free use in both open source and commercial projects.
- serve
- Apache-2.0
Last pushed
- budgetml
- Feb 12, 2024
- serve
- Aug 6, 2025
Categories
- budgetml
- Inference & Serving
- serve
- Inference & Serving
Trust and health
Maintenance
- budgetml
- Dormant (18%)
- serve
- Archived (8%)
Days since push
- budgetml
- 901d
- serve
- 360d
Archived on GitHub
- budgetml
- No
- serve
- Yes
Open issues (now)
- budgetml
- 4
- serve
- 443
OSV dependency advisories
- budgetml
- No published findings from this source as of 2026-07-11
- serve
- No lockfile (source not queried)
Full report
- budgetml
- Trust report
- serve
- Trust report
Shared compatibility
- Python · budgetml: Python runtime · serve: Python runtime
Choose budgetml if…
- budgetml is primarily Python; serve is Java.
- 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
Choose serve if…
- serve is primarily Java; budgetml is Python.
- Tags unique to serve: cpu, deep-learning, docker, gpu.
- If you are working primarily with PyTorch-based machine-learning projects that require production-grade deployments.
When NOT to use serve
- Avoid if your primary model development is not in PyTorch or requires deployment using a language other than Java.
- Not suitable if you do not require the fine-grained control and optimization provided by tools such as VLLM or TensorRT-LLM.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (ebhy/budgetml) · observed Aug 2, 2026
- GitHub forks (ebhy/budgetml) · observed Aug 2, 2026
- Last push (ebhy/budgetml) · observed Feb 12, 2024
- License file (Apache-2.0) · observed Aug 2, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (pytorch/serve) · observed Aug 2, 2026
- GitHub forks (pytorch/serve) · observed Aug 2, 2026
- Last push (pytorch/serve) · observed Aug 6, 2025
- License file (Apache-2.0) · observed Aug 2, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: budgetml 1.3k · serve 4.3k (synced Aug 2, 2026).
Common questions
- What is the difference between budgetml and serve?
- budgetml: Deploys ML inference service economically. serve: Serve, optimize and scale PyTorch models in production. See the comparison table for live GitHub stats and shared categories.
- When should I choose budgetml over serve?
- Choose budgetml over serve when budgetml is primarily Python; serve is Java; 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 choose serve over budgetml?
- Choose serve over budgetml when serve is primarily Java; budgetml is Python; Tags unique to serve: cpu, deep-learning, docker, gpu; If you are working primarily with PyTorch-based machine-learning projects that require production-grade deployments.
- 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
- When should I avoid serve?
- Avoid if your primary model development is not in PyTorch or requires deployment using a language other than Java. Not suitable if you do not require the fine-grained control and optimization provided by tools such as VLLM or TensorRT-LLM.
- Is budgetml or serve more popular on GitHub?
- serve has more GitHub stars (4,350 vs 1,343). Stars measure visibility, not whether either tool fits your constraints.
- Are budgetml and serve open source?
- Yes - both are open-source projects on GitHub (budgetml: Apache-2.0, serve: Apache-2.0).
- Where can I find alternatives to budgetml or serve?
- GraphCanon lists graph-backed alternatives at budgetml alternatives and serve alternatives (budgetml 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, budgetml or serve?
- budgetml: Dormant. serve: Archived. 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 budgetml and serve?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: budgetml trust report; serve trust report.