Amazon Nova Multimodal Embeddings vs ShieldGemma 9B
Amazon Nova Multimodal Embeddings (2025) and ShieldGemma 9B (2024) are compact production models from Amazon Web Services (AWS) AI and Google DeepMind. Amazon Nova Multimodal Embeddings ships a not-yet-sourced context window, while ShieldGemma 9B ships a 8k-token context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Amazon Nova Multimodal Embeddings is safer overall; choose ShieldGemma 9B when provider fit matters.
Decision scorecard
Local evidence first| Signal | Amazon Nova Multimodal Embeddings | ShieldGemma 9B |
|---|---|---|
| Best for | multimodal apps | general production evaluation |
| Decision fit | General | Classification |
| Context window | — | 8k |
| Cheapest output | - | - |
| Provider routes | 1 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Amazon Nova Multimodal Embeddings uniquely exposes Multimodal in local model data.
- ShieldGemma 9B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags ShieldGemma 9B for Classification.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Amazon Nova Multimodal Embeddings
Unavailable
No complete token price in local provider data
ShieldGemma 9B
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Amazon Nova Multimodal Embeddings and ShieldGemma 9B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Multimodal before moving production traffic.
- No overlapping tracked provider route is sourced for ShieldGemma 9B and Amazon Nova Multimodal Embeddings; plan for SDK, billing, or endpoint changes.
- Amazon Nova Multimodal Embeddings adds Multimodal in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-12-01 | 2024-07-01 |
| Context window | — | 8k |
| Parameters | — | 9B |
| Architecture | - | decoder only |
| License | Proprietary | Gemma |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Amazon Nova Multimodal Embeddings | ShieldGemma 9B |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers |
Pricing not yet sourced for either model.
Capabilities
| Capability | Amazon Nova Multimodal Embeddings | ShieldGemma 9B |
|---|---|---|
| Vision | No | No |
| Multimodal | Yes | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on multimodal input: Amazon Nova Multimodal Embeddings. Both models share the core language-model surface, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
Pricing coverage is uneven: Amazon Nova Multimodal Embeddings has no token price sourced yet and ShieldGemma 9B has no token price sourced yet. Provider availability is 1 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Amazon Nova Multimodal Embeddings when provider fit are central to the workload. Choose ShieldGemma 9B when provider fit are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Is Amazon Nova Multimodal Embeddings or ShieldGemma 9B open source?
Amazon Nova Multimodal Embeddings is listed under Proprietary. ShieldGemma 9B is listed under Gemma. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for multimodal input, Amazon Nova Multimodal Embeddings or ShieldGemma 9B?
Amazon Nova Multimodal Embeddings has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Amazon Nova Multimodal Embeddings and ShieldGemma 9B?
Amazon Nova Multimodal Embeddings is available on AWS Bedrock. ShieldGemma 9B is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
When should I pick Amazon Nova Multimodal Embeddings over ShieldGemma 9B?
Amazon Nova Multimodal Embeddings is safer overall; choose ShieldGemma 9B when provider fit matters. If your workload also depends on provider fit, start with Amazon Nova Multimodal Embeddings; if it depends on provider fit, run the same evaluation with ShieldGemma 9B.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.