Table of Contents
For organisations with existing application architecture, the question of whether to deploy AI as a microservice or integrate into the monolith depends on team structure, integration depth, and scale. Standard architecture decision applied to AI specifics.
Microservice (separate AI service): right for shared AI tier across multiple apps, dedicated AI team, multi-tenant SaaS. Monolith integration: right for solo apps, tight integration with business logic, smaller scale. Most production AI in 2026: microservice with OpenAI-compatible API; consumed by multiple apps.
Comparison
| Aspect | AI as microservice | Integrated into monolith |
|---|---|---|
| Reusability | Across apps | Within one app |
| Team ownership | Dedicated AI team | Backend team |
| Deploy independence | Yes | No |
| Latency | Network hop (+5-20ms) | In-process |
| Ops complexity | Two services | One |
| Best for | Multi-app, multi-tenant | Single-app, tight integration |
When each
- Microservice: organisation has multiple apps consuming AI, dedicated AI platform team, multi-tenant SaaS, OpenAI-compatible API surface
- Monolith integration: single application, tight business-logic integration, very small team, latency-sensitive (in-process avoids network hop)
Verdict
For most production AI in 2026, microservice with OpenAI-compatible API is the right shape. Reusability across apps + dedicated team + independent deploy + standard surface area. Integrated monolith only for very specific cases (single app, latency-critical, very small team). Build AI as a service; consume from monolith if needed.
Bottom line
Microservice with OpenAI-compatible API. See AI DX.
