Comparison
ai-serving vs paddler
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 paddler if paddler offers a streamlined approach to serve LLMs/VLMs with minimal setup complexity, focusing on scaling through simplicity.
Markdown twin · ai-serving alternatives · paddler alternatives
GraphCanon updated Sep 20, 2026
9views this month
vs
Trust & integrity
| Signal | ai-serving | paddler |
|---|---|---|
| Maintenance | Slowing (208d since push) As of Sep 20, 2026 · github_public_v1 | Very active (2d 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 published findings from this source as of 2026-07-11 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
- paddler
- Open-source LLM/VLM load balancer and serving platform for self-hosting at scale
Stars
- ai-serving
- 166
- paddler
- 1.7k
Forks
- ai-serving
- 31
- paddler
- 98
Open issues
- ai-serving
- 3
- paddler
- 30
Language
- ai-serving
- Scala
- paddler
- Rust
Adopt for
- ai-serving
- Ai-Serving is an inference server supporting PMML and ONNX formats via HTTP or gRPC endpoints, easily deployable with Docker.
- paddler
- Paddler offers a streamlined approach to serve LLMs/VLMs with minimal setup complexity, focusing on scaling through simplicity.
Persona
- ai-serving
- -
- paddler
- -
Runtime
- ai-serving
- -
- paddler
- -
License
- ai-serving
- Apache-2.0
- paddler
- Apache-2.0
Last pushed
- ai-serving
- Feb 24, 2026
- paddler
- Sep 17, 2026
Categories
- ai-serving
- Inference & Serving
- paddler
- Inference & Serving
Trust and health
Maintenance
- ai-serving
- Slowing (36%)
- paddler
- Very active (96%)
Days since push
- ai-serving
- 208d
- paddler
- 2d
Open issues (now)
- ai-serving
- 3
- paddler
- 30
Stars delta
- ai-serving
- 0 (30d)
- paddler
- +11 (30d)
Open issues delta
- ai-serving
- 0 (30d)
- paddler
- +1 (30d)
OSV dependency advisories
- ai-serving
- No lockfile (source not queried)
- paddler
- No published findings from this source as of 2026-07-11
Full report
- ai-serving
- Trust report
- paddler
- Trust report
Choose ai-serving if…
- ai-serving is primarily Scala; paddler is Rust.
- 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 paddler if…
- paddler is primarily Rust; ai-serving is Scala.
- Tags unique to paddler: ai, cpu, gpu, llamacpp.
- paddler ships Docker support for self-hosted deployment.
- Need a platform that simplifies deployments around the ggml ecosystem for self-hosting at scale
When NOT to use paddler
- Require extensive customization options in model serving infrastructure that Paddler's minimalistic design does not support
- Seek complex feature sets beyond simple scale-out capabilities, which might be found in more comprehensive platforms
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 (intentee/paddler) · observed Sep 20, 2026
- GitHub forks (intentee/paddler) · observed Sep 20, 2026
- Last push (intentee/paddler) · observed Sep 17, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: ai-serving 166 · paddler 1.7k (synced Sep 20, 2026).
Common questions
- What is the difference between ai-serving and paddler?
- ai-serving: Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints. paddler: Open-source LLM/VLM load balancer and serving platform for self-hosting at scale. See the comparison table for live GitHub stats and shared categories.
- When should I choose ai-serving over paddler?
- Choose ai-serving over paddler when ai-serving is primarily Scala; paddler is Rust; 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 paddler over ai-serving?
- Choose paddler over ai-serving when paddler is primarily Rust; ai-serving is Scala; Tags unique to paddler: ai, cpu, gpu, llamacpp; paddler ships Docker support for self-hosted deployment; Need a platform that simplifies deployments around the ggml ecosystem for self-hosting at scale.
- 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 paddler?
- Require extensive customization options in model serving infrastructure that Paddler's minimalistic design does not support Seek complex feature sets beyond simple scale-out capabilities, which might be found in more comprehensive platforms
- Is ai-serving or paddler more popular on GitHub?
- paddler has more GitHub stars (1,674 vs 166). Stars measure visibility, not whether either tool fits your constraints.
- Are ai-serving and paddler open source?
- Yes - both are open-source projects on GitHub (ai-serving: Apache-2.0, paddler: Apache-2.0).
- Where can I find alternatives to ai-serving or paddler?
- GraphCanon lists graph-backed alternatives at ai-serving alternatives and paddler alternatives (ai-serving markdown twin, paddler 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 paddler?
- ai-serving: Slowing. paddler: 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 paddler?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ai-serving trust report; paddler trust report.