Generative AI platform architecture: one gateway, many models, full control
Good generative AI platform architecture gives every team governed access to multiple models through one LLM gateway, with routing, evaluations, logging and cost controls built in. We design and build that platform with your engineers.
Platform architecture
Why direct model access does not scale past the first few teams
Early generative AI projects usually call a model provider's API directly with a shared key. That works for a pilot. By the tenth team, nobody knows which applications use which models, keys are copied across repositories, sensitive data flows to endpoints nobody approved, and the monthly bill arrives with no way to attribute it to a product or cost center.
Model choice adds pressure. New models arrive frequently, pricing changes, older versions are deprecated on a provider's schedule, and different tasks are best served by different models. Without generative AI platform architecture that separates applications from specific providers, each change becomes a code change in every application, and teams stay on outdated models because switching is too expensive.
Quality is the hardest gap. Generative outputs are probabilistic, so a prompt change or model upgrade can quietly break a workflow that worked yesterday. Teams without shared evaluation tooling test by eye, launch without a baseline and discover regressions from users. A platform has to make measurement the default, not an optional extra.
What you get
LLM gateway design and build
A central gateway in front of every model provider, handling authentication, per-team keys, rate limits, request and response logging, PII redaction, content filtering and cost attribution, deployed in your cloud with your engineers.
Model routing and fallback policy
Rules for which models serve which tasks, based on quality, latency, cost and data sensitivity, with automatic fallback between providers and a documented process for testing and promoting new model versions.
Evaluation framework
Task-specific test sets, automated and model-graded scoring, human review workflows and regression gates wired into your release pipeline, so every prompt, model or retrieval change is measured before it reaches users.
Cost controls and FinOps model
Budgets, quotas and alerts by team and application, caching and prompt optimization guidance, and dashboards that show cost per request and per outcome, so spend is visible and controllable as usage grows.
Observability and tracing
End-to-end traces across prompts, retrieval calls, tool calls and model responses, with latency, token usage, error and safety metrics feeding your existing monitoring stack and incident processes.
Developer platform and onboarding
SDKs, templates, prompt management and self-service onboarding so a new team can get governed model access, logging and evaluation in days, following documented patterns instead of copying another team's code.
Reference generative AI platform architecture
This is the generative AI platform architecture we typically build toward. Each component can be built, bought or assembled from open-source parts; the interfaces between them are what keep the platform flexible.
- LLM gateway
- The single entry point for all model traffic, providing identity, per-application keys, rate limits, logging, redaction and a provider-neutral API so applications never call a vendor endpoint directly.
- Model router
- Policy-driven selection among providers such as Azure OpenAI, Amazon Bedrock, Google Vertex AI, Anthropic and self-hosted open-weight models, based on task, sensitivity, latency, cost and availability.
- Model catalog and lifecycle
- A registry of approved models and versions with their permitted data classes, evaluation results, owners and deprecation dates, driving what the router is allowed to use.
- Evaluation service
- Versioned test sets, automated and model-graded metrics, human review queues and release gates that run on every prompt, model or retrieval change and in sampled production traffic.
- Safety and policy layer
- Input and output filtering, prompt injection detection, PII handling and policy rules informed by the OWASP Top 10 for LLM Applications and NIST AI 600-1, applied consistently at the gateway.
- Cost and usage controls
- Token budgets, quotas, semantic and exact-match caching, and chargeback reporting by team, application and use case, with alerts before budgets are exceeded rather than after.
- Observability and tracing
- OpenTelemetry-compatible traces covering prompts, retrieval, tool calls and responses, feeding dashboards for quality, latency, errors and spend alongside your existing monitoring tools.
How we deliver it
Assess
We inventory current model usage, keys, providers, spend and data flows, interview the teams building generative AI features, and identify the controls, gaps and duplicated tooling that the platform must address first.
Blueprint
We design the generative AI platform architecture: gateway, routing policy, evaluation framework, observability, cost model and developer experience, with decision records on build versus buy for each component and a phased delivery plan.
Build
Our architects and your platform engineers build the gateway, evaluation harness and observability stack as infrastructure as code, then migrate the first applications onto the platform and measure the results against the previous setup.
Govern
We set up model approval and deprecation processes, evaluation gates by risk tier, cost reviews and platform service levels, and train the team that will own the platform as an internal product going forward.
What changes
- Every generative AI application reaches models through one governed gateway with consistent logging and controls.
- Teams can switch or add models by changing routing policy rather than rewriting application code.
- Prompt and model changes are measured against evaluation baselines before they reach users.
- Generative AI spend is attributed to teams and applications, with budgets and alerts that prevent surprises.
Related services
- Enterprise AI Reference ArchitectureA vendor-neutral reference architecture for models, retrieval, agents, data and controls that turns one-off pilots into a reusable platform.
- RAG and Knowledge ArchitectureRetrieval-augmented generation for enterprise content: access control, citations, freshness and answer quality you can measure.
- AI Security ArchitectureSecurity for LLM apps and agents: threat modeling, prompt injection and data leakage controls, agent identity and red teaming.
Generative AI Platform Architecture: common questions
What is generative AI platform architecture?
Generative AI platform architecture is the shared infrastructure that lets many teams build on large language models safely: an LLM gateway, model routing, a model catalog, evaluation tooling, safety filters, observability and cost controls. It separates applications from specific model providers, so an organization can add, switch or retire models while keeping security, quality and spend consistent across teams.
What is an LLM gateway?
An LLM gateway is a service that sits between applications and model providers and handles every model request. It authenticates callers, applies rate limits and budgets, logs requests and responses, redacts sensitive data, enforces content policies and routes traffic to the right model. It gives an enterprise one control point instead of many direct API integrations.
Should we build or buy an LLM gateway?
It depends on your controls and scale. Cloud providers, API management vendors and open-source projects offer gateway capabilities that cover routing, keys and logging well. Enterprises often buy or adopt one of these and build the pieces specific to them, such as data classification rules, chargeback integration and evaluation gates. We document that choice in a decision record.
Why use multiple LLMs instead of standardizing on one?
Using multiple LLMs lets you match each task to the model that gives acceptable quality at the lowest cost and latency, keep a fallback when a provider has an outage, and meet data residency or sensitivity rules that one provider cannot. The platform keeps that manageable by routing through one gateway with shared evaluations and controls.
How do you control generative AI costs?
Generative AI costs are controlled by attributing every request to a team and application, setting budgets and quotas at the gateway, routing simpler tasks to smaller models, caching repeated requests, trimming prompts and retrieved context, and reviewing cost per outcome regularly. Visibility comes first, because teams cannot manage spend they cannot see.
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