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Decision map

AI

Which model, integration, autonomy, modality, gateway, retrieval handoff, and AI-specific operations decisions does the workload require?

Recommendation

Start with LLM APIs

Application and platform teams building production AI features and deciding among provider APIs, SDK abstractions, bounded agents, modality-specific APIs, gateway controls, and AI-specific observability.

Open the starting decision

Scope

Use this boundary to avoid solving adjacent problems in the wrong layer.

Included

  • Model and provider API selection for a measured workload
  • Provider-native or provider-normalizing AI application SDK integration
  • The no-agent gate and bounded model-directed agent runtime decision
  • Embedding API selection with an explicit handoff to Data-owned Vector Search
  • Image generation, speech recognition, and speech synthesis
  • Multi-provider gateway and model-routing controls
  • LLM and agent-specific traces, evaluations, token and model cost, and behavior controls

Excluded

  • Primary ownership of vector storage, indexing, filtering, and retrieval architecture
  • Deterministic background and scheduled workflow execution
  • Generic application logging and Application Performance Monitoring
  • Media storage and delivery
  • A phantom workflow-orchestration Task or route
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Important tools

These tools represent distinct routes inside this decision area; open the relevant Task before treating any as a default.

Decision sequence

Make the foundational ownership decisions before adding specialized capabilities.

  1. What workload and output modality must be evaluated?

    Define text or reasoning, image, speech recognition, or speech synthesis requirements before selecting a model or provider.

    LLM APIs Image Generation Speech-to-Text Text-to-Speech

  2. How should application code integrate provider calls and tools?

    Choose provider-native access or an SDK abstraction after the model workload is defined, not as a substitute for model evaluation.

    LLM APIs AI SDKs

  3. Does the workflow actually require model-directed autonomy?

    Stop the AI Agents branch when deterministic execution is sufficient; activate runtime, tool, guardrail, state, and human-intervention questions only after the no-agent gate passes.

    AI Agents

  4. Does the application need semantic representations for retrieval or comparison?

    Choose Embedding APIs here, then follow the explicit cross-category handoff to Data-owned Vector Search for storage, indexing, filtering, and retrieval.

    Embedding APIs

  5. Do provider diversity, reliability, budget, policy, or behavior-evaluation needs justify production control layers?

    Evaluate gateway and routing controls separately from AI-specific traces, token and cost semantics, agent behavior, and output evaluation.

    AI Gateways & Model Routing AI Observability

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Decision groups

Each Task owns a distinct user decision rather than a product feature label.

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Common confusions

These boundaries prevent adjacent Tasks from collapsing into one generic shortlist.

LLM APIs and AI SDKs are treated as the same provider decision.
LLM APIs choose model and provider capability; AI SDKs choose the application integration and abstraction layer.
Any multi-step model call is assumed to require an AI Agent.
AI Agents activate only when model-directed autonomy is justified; deterministic work remains with existing Background Jobs or Scheduled Jobs rather than a phantom orchestration page.
Embedding APIs and Vector Search are treated as one AI-owned Task.
Embedding APIs create numerical representations; Data-owned Vector Search stores, indexes, filters, and retrieves them.
Gateway analytics are assumed to cover all AI Observability needs.
Gateways control the request path; AI Observability also covers generations, tools, handoffs, agents, token and cost semantics, and output evaluation across gateway and non-gateway calls.
AI Observability is treated as a replacement for general Logging and Application Performance Monitoring.
Generic telemetry remains in Observability; AI Observability adds model, token, generation, tool, agent, and evaluation semantics.
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What to defer

Do not add specialized identity systems before the product has the requirement they serve.

The workflow can be implemented with deterministic application logic, jobs, or schedules.

Model-directed autonomy introduces runtime, tool, state, guardrail, evaluation, and human-intervention obligations without a justified benefit.

The application uses one provider and does not require centralized fallback, routing, rate, budget, cache, or policy controls.

A gateway adds another request-path control plane that is unnecessary until shared operational requirements exist.

The product has no image or audio workload.

Modality-specific APIs and rights, latency, streaming, storage, and safety constraints are inactive.

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Related starter stacks

See how these decisions appear inside complete application starting points.

Authoritative resources

Standards and primary documentation supporting the Category boundaries.

Official documentationOpenAI model catalog
Official specification repositoryOpenTelemetry GenAI Semantic Conventions

Sources

Claim-level references used for this decision map.

  1. 1
    Model catalog

    OpenAI · Accessed Official

  2. 2
    Providers and Models

    Vercel AI SDK · Accessed Official

  3. 3
    Tools

    Vercel AI SDK · Accessed Official

  4. 4
    A practical guide to building agents

    OpenAI · Accessed Official

  5. 5
    Vector embeddings

    OpenAI · Accessed Official

  6. 6
    Vector search in Elasticsearch

    Elastic · Accessed Official

  7. 7
    Image generation

    OpenAI · Accessed Official

  8. 8
    Audio and speech

    OpenAI · Accessed Official

  9. 9
    AI Gateway overview

    Cloudflare · Accessed Official

  10. 10
    Dynamic routing

    Cloudflare · Accessed Official

  11. 11
    Agents SDK tracing

    OpenAI · Accessed Official

  12. 12
    OpenTelemetry GenAI Semantic Conventions

    OpenTelemetry · Accessed Official

  13. 13
    Generative AI metrics

    OpenTelemetry · Accessed Official

  14. 14
    Signals

    OpenTelemetry · Accessed Official

  15. 15
    Evaluation best practices

    OpenAI · Accessed Official