AI integration services: add AI features to your SaaS without stalling the roadmap

Which integration pattern fits your product?

There are four patterns, and picking the simplest one that meets the need is most of the design. Direct model calls cover summarisation, drafting, classification and extraction: a prompt, structured output and an evaluation set, with no new data infrastructure. RAG over customer data is needed when answers must come from each tenant's own content, and brings an index, permission-aware retrieval and freshness to manage. Agents inside the product let the model take actions through your APIs; they are powerful and still comparatively rare in production, with Menlo Ventures, December 2025 finding that only 16 percent of enterprise and 27 percent of startup deployments qualify as true agents, most being fixed-sequence or routing workflows. Batch ML suits recommendations, scoring and forecasting that run on a schedule rather than per request. For the full build of a model-centred product rather than a feature, see LLM application development.

How do you ship AI without slowing the core roadmap?

Keep the AI layer and the product layer as separate streams with a clear interface. The embedded AI engineer owns prompts, retrieval, evaluation, model abstraction and cost control; your developers own the UI, the data model changes and the billing hooks, which they know better than anyone. Using AI tools is not the same as building AI features: Stack Overflow's 2025 Developer Survey found 84 percent of developers use or plan to use AI tools in their work, yet 46 percent actively distrust the accuracy of those tools' output. That gap is exactly what evaluation discipline closes, and it is the skill a product team most often lacks. One or two engineers with that discipline, matched in days, let the rest of the team keep its velocity.

How do you handle multi-tenancy, data isolation and per-tenant cost?

Isolation has to be structural. Each tenant gets its own namespace or partition in the vector store and cache, prompts never mix content across tenants, logs and traces are tagged by tenant and scrubbed of PII where policy requires, and retrieval filters by the identity of the person asking before anything reaches the model. Provider choice follows your customers' contracts: a hosted API with a data-processing agreement, a cloud-hosted model through Bedrock or Vertex AI inside your region, or an open-weight model served with vLLM in your own account when nothing may leave it.

• Metering: count tokens and requests per tenant from day one, so you can price the feature by plan and spot the account that is costing more than it pays.
• Budgets and limits: hard token budgets per request, rate limits per seat and a kill switch per tenant, enforced in code rather than in a dashboard.
• Cost shaping: prompt caching for repeated context, routing simple requests to a smaller model, streaming for perceived latency and batch endpoints for anything that is not interactive.

What quality gates should run before launch, and what drives the cost?

An AI feature should not ship on a demo. The gate is an evaluation set built from real, anonymised customer inputs, scored automatically on every change and reviewed by hand on the hardest cases; a feature flag with a staged rollout; dashboards for quality, latency percentiles and cost per tenant; and a rollback that covers the model version and the prompt, not just the code. Our guide to deploying LLM apps to production has the full checklist.

Cost is driven by the pattern (direct calls are cheapest, agents and per-tenant RAG are most involved), the reliability bar and how it must be proven, the state of the data you want the model to use, the number of internal APIs the feature touches, compliance requirements and the depth of multi-tenant work. We scope all of this before the one-week trial. If you are still weighing whether to build the feature at all, our guide to build vs buy for AI walks through the decision.