NeoSignal Stack Builder: Build Compatible AI Infrastructure with Confidence

You've chosen Claude Opus 4.5 as your model. You're considering H100s for inference. Your team prefers AWS. But will these components actually work well together? Does Claude have optimized kernels for H100? Does AWS have good H100 availability in your region? Does your framework of choice support the specific API patterns Claude uses?

NeoSignal Stack Builder showing component selection with compatibility matrix

NeoSignal Stack Builder answers these questions before you commit. Select components across five infrastructure categories—Model, Accelerator, Cloud, Framework, Agent—and see real-time compatibility analysis. The screenshot shows Claude Opus 4.5 (score 96), NVIDIA H100 (score 92), and AWS (score 87) selected, with an overall 94% compatibility rating. The matrix reveals pairwise scores: Claude to AWS at 95%, H100 to AWS at 92%. No guessing, no trial and error—just validated compatibility before you write a line of code.

The benefit: you build infrastructure that works together from day one. The chat panel offers suggestions like "Help me build an optimal inference stack" based on your selections.

Detailed Walkthrough

The Compatibility Problem

AI infrastructure involves multiple interconnected components, each with dependencies and constraints:

• Models need specific hardware support, API compatibility, and framework integrations
• Accelerators require driver support, cloud availability, and optimization libraries
• Cloud providers offer varying GPU types, regions, pricing, and networking capabilities
• Frameworks support different model formats, parallelism strategies, and serving patterns
• Agents depend on model capabilities, tool integrations, and runtime environments

These components don't exist in isolation. A model optimized for one GPU architecture may perform poorly on another. A framework that excels with OpenAI APIs may lack Claude integration. A cloud provider with great H100 availability may have poor TPU support.

NeoSignal Stack Builder maps these relationships and surfaces compatibility issues before they become production problems.

How the Stack Builder Works

The Stack Builder operates on three interconnected systems: component selection, compatibility calculation, and recommendation engine.

Component Selection presents five category slots:

Click any slot to open a selection modal with searchable component list. Each component shows its NeoSignal score (0-100) and category-specific metrics.

Compatibility Calculation computes pairwise scores between all selected components. NeoSignal maintains a compatibility database with scores for ~2,000 component pairs, sourced from:

• Official integration documentation
• Community benchmarks and reports
• Framework support matrices
• Cloud service availability data
• Production deployment patterns

When you select 3 components, the matrix shows 3 pairwise comparisons. Select all 5 slots, and you see 10 pairwise scores. The overall compatibility percentage is the weighted average, with critical pairs (model-accelerator, model-framework) weighted higher.

Recommendation Engine suggests improvements based on your selections:

• "Consider vLLM for better Claude inference performance"
• "H200 has better availability on CoreWeave than AWS"
• "LangChain has stronger agent integration than LlamaIndex"

The Compatibility Matrix

The matrix visualization shows compatibility scores in a grid format:

              | NVIDIA H100 | AWS    |
--------------+-------------+--------+
Claude Opus   |      ?      |  95%   |
NVIDIA H100   |     ---     |  92%   |

Color coding indicates compatibility quality:

• Green (90%+): Excellent—components work seamlessly together
• Yellow (60-89%): Moderate—functional but may have limitations
• Red (<60%): Poor—significant friction expected

A "?" indicates no direct compatibility data—NeoSignal hasn't mapped this specific pair. This often occurs between components in different ecosystems (e.g., a model and an unrelated accelerator with no direct relationship).

Component Selection Interface

Click a slot to open the selection modal. The interface shows:

Search Bar: Filter components by name ("H100", "Claude", "vLLM")

Component Cards: Each card displays:

• Component name and logo
• NeoSignal score with trend indicator (rising ↑, stable →, declining ↓)
• Key metrics (context window for models, memory for accelerators)
• Category tag

Score Sorting: Components sorted by score, highest first. Top-rated options appear immediately.

When you select a component, the modal closes and the compatibility matrix updates instantly. Change your mind? Click the X on any selected component to clear the slot.

Calculating Overall Compatibility

The overall compatibility score (shown as "Compatibility: 94%" in the header) uses weighted averaging:

Overall = Σ(pair_score × pair_weight) / Σ(pair_weight)

Weight assignments:

• Model ↔ Accelerator: 1.5x (most critical for performance)
• Model ↔ Framework: 1.5x (determines integration complexity)
• Model ↔ Cloud: 1.2x (affects availability and pricing)
• Other pairs: 1.0x (standard weight)

A stack with all 90%+ pairwise scores yields 90%+ overall. A single poor pairing (say, 50% between model and framework) can drag overall compatibility down significantly—as it should, since one incompatibility can doom the entire stack.

Save and Share Stacks

Click "Save Stack" to persist your configuration. Saved stacks become artifacts in your NeoSignal account, accessible from the Saved panel. Each artifact captures:

• All selected components
• Compatibility scores at save time
• Stack creation timestamp

Share stack URLs with teammates. "Check out this inference stack I'm considering"—they can load the exact configuration and see compatibility analysis. Useful for architecture discussions and vendor evaluations.

Stacks auto-save as you build (indicated by "Saving..." → "Saved" status). Close the browser, come back later, your in-progress stack persists.

Stack Building Scenarios

Inference Stack Planning: You need to serve Claude at scale. Select Claude Opus 4.5, then explore accelerators. The builder shows H100 and H200 have excellent compatibility; AMD MI300X shows moderate compatibility (some kernel optimizations missing). Select AWS as cloud—95% compatibility with Claude (strong API integration). Add vLLM as framework—excellent serving performance. Overall stack compatibility: 92%.

Training Infrastructure: Building a training cluster for a 70B model. Select Llama 3.1 70B, NVIDIA B200 (high memory for large models), CoreWeave (best B200 availability), PyTorch (native training support). The builder confirms strong compatibility across all pairs. Add Weights & Biases for experiment tracking—no compatibility concerns.

Agent Development Stack: Building an AI agent product. Select Claude Code as your agent runtime, Claude Opus 4.5 as the underlying model, AWS for deployment. The builder shows 98% compatibility between Claude Code and Claude Opus—expected, same ecosystem. Add LangChain for orchestration. Overall compatibility: 94%.

Multi-Cloud Evaluation: Comparing cloud providers for the same workload. Build a stack with your model and framework, then swap cloud providers. AWS shows 87 score. CoreWeave shows 94. Google Cloud shows 89. The compatibility scores help differentiate—CoreWeave's GPU-focused infrastructure yields better compatibility for inference workloads.

Chat Integration

The Stack Builder integrates with NeoSignal AI chat. With components selected, ask:

• "Help me build an optimal inference stack" — Gets recommendations considering your current selections
• "What components work best together?" — Suggests high-compatibility combinations
• "Recommend a cost-effective training stack" — Balances compatibility with pricing
• "What are the key compatibility factors?" — Explains what drives scores

The chat understands your selection context. "Why is H100 compatibility low?" gets analysis specific to your selected model and framework, not generic H100 information.

The Compatibility Database

NeoSignal maintains compatibility scores for component pairs through:

Integration Documentation: Official docs state which frameworks support which models, which clouds offer which GPUs.

Benchmark Data: Performance benchmarks reveal how well components work together. A model running 2x slower on one GPU architecture than another indicates compatibility friction.

Community Reports: Developer experience reports, GitHub issues, and forum discussions surface real-world compatibility challenges.

Support Matrices: Framework compatibility tables (PyTorch model support, vLLM backend support) map to pairwise scores.

Scores update as the ecosystem evolves. When vLLM adds Claude support, compatibility scores update. When AWS launches new GPU regions, cloud compatibility improves.

Understanding Score Gaps

Sometimes the matrix shows "?" for a pairing. This indicates:

• No direct relationship: A model and accelerator have no direct compatibility concept (models run on frameworks that run on accelerators)
• Insufficient data: NeoSignal hasn't mapped this specific pair yet
• New components: Recently added components may not have full compatibility coverage

Missing data doesn't mean incompatibility—it means you should verify independently or ask NeoSignal chat for guidance.

From Builder to Deployment

The Stack Builder validates architectural decisions before implementation. Use it at these points:

Project kickoff: Before writing code, validate that your planned components work together. Avoid discovering incompatibilities during integration.

Vendor evaluation: Comparing cloud providers or frameworks? Build equivalent stacks with different components and compare compatibility scores.

Migration planning: Moving from one infrastructure to another? Build your target stack and verify compatibility before committing engineering resources.

Architecture review: Present stack configurations in technical reviews. Compatibility scores provide objective data for decision-making.

The goal isn't to achieve 100% compatibility on every stack—it's to understand tradeoffs before you commit. A 75% compatibility stack might be acceptable if the incompatible pair has a workaround. Better to know upfront than discover during production rollout.

Stack Builder joins Memory Calculator, TCO Calculator, and other NeoSignal tools in making AI infrastructure decisions approachable through data. Build your stack, check compatibility, then proceed with confidence.