How We Build Secure and Customizable AI Platforms for B2B Clients

We design and implement multi-tenant AI infrastructure that handles from 10 to over 1000 B2B clients while maintaining data isolation, performance, and customization flexibility. In 3–5 months, we build the platform from scratch or migrate an existing one — turnkey, with documentation and team training. Typical investment ranges from $50,000 to $150,000.

Common Pain Points in Building an AI SaaS Platform

Data isolation is the primary headache. If one tenant accidentally accesses another's model, it means reputation loss and legal risks. Row-Level Security in PostgreSQL solves this at the DB level but doesn't protect against ML artifact leaks. We use S3 prefixes plus IAM policies for each tenant.

The second block is performance under growth. Shared schema is cheaper, but with 100+ tenants query latency increases. Without proper indexing on tenant_id, queries slow down. We design sharding upfront and use connection pools with tenant-aware routing.

The third is AI customization for each client. Tenants want their own prompts, models, and limits. Without a TenantAwareInferenceService, administration becomes chaos. Request a consultation — we will help you build the right architecture.

Isolation Models and Methods Comparison

For AI workloads, the second option is optimal: Shared DB + Separate Schema for transactions plus separate S3 prefixes for ML models. This provides a balance between cost and flexibility. Infrastructure savings reach up to $10,000 per month for 50+ tenants.

How to Ensure Data Isolation Between Tenants?

We use Row-Level Security in PostgreSQL. Each query is automatically filtered by tenant_id. Example policy:

-- Enable RLS for tenant data isolation
ALTER TABLE predictions ENABLE ROW LEVEL SECURITY;

-- Policy: each tenant sees only their data
CREATE POLICY tenant_isolation ON predictions
    USING (tenant_id = current_setting('app.current_tenant_id')::UUID);

Middleware on FastAPI sets the tenant context for each request (see code below). This guarantees that no query "leaks" between tenants.

# FastAPI middleware to set tenant context
@app.middleware("http")
async def tenant_context_middleware(request: Request, call_next):
    tenant_id = await resolve_tenant(request)
    request.state.tenant_id = tenant_id

    async with db.acquire() as conn:
        await conn.execute(
            f"SET LOCAL app.current_tenant_id = '{tenant_id}'"
        )
        request.state.db_conn = conn
        response = await call_next(request)

    return response

Tenant-Specific AI Configuration

@dataclass
class TenantAIConfig:
    tenant_id: str
    allowed_models: list[str]
    system_prompt_override: str = None
    monthly_token_limit: int = 1_000_000
    concurrent_request_limit: int = 10
    custom_models: list[str] = None
    prediction_log_retention_days: int = 90
    pii_detection_enabled: bool = True
    audit_log_enabled: bool = True

class TenantAwareInferenceService:
    async def predict(self, tenant_id: str, model_name: str,
                       inputs: dict) -> dict:
        config = await self.get_tenant_config(tenant_id)

        if model_name not in config.allowed_models:
            raise PermissionError(f"Model '{model_name}' not allowed")

        if not await self.rate_limiter.check(tenant_id, config.concurrent_request_limit):
            raise RateLimitError("Concurrent request limit exceeded")

        if config.system_prompt_override and 'system' in inputs:
            inputs['system'] = config.system_prompt_override + "\n\n" + inputs['system']

        if config.pii_detection_enabled:
            inputs = await self.pii_detector.redact(inputs)

        result = await self.inference_engine.run(model_name, inputs)

        await self.audit_log.record(tenant_id, model_name, inputs, result)

        return result

What's Included in the Work (Deliverables)

• Documentation: Full architecture docs, API reference, deployment guide, runbook.
• Access: Source code repository, CI/CD pipeline, monitoring dashboards (Grafana + Prometheus).
• Training: 2-day hands-on workshop for your team, plus recorded sessions.
• Support: 3 months of post-launch support (SLA-based), with optional extended maintenance.
• Code artifacts: All configuration files, Dockerfiles, Kubernetes manifests, Terraform scripts.

Work Process and Scope

1. Analytics — audit of current infrastructure, defining isolation and scale requirements.
2. Design — DB schema, API contracts, stack selection (PyTorch, LangChain, PostgreSQL, S3).
3. Implementation — coding, RLS setup, creating TenantAwareInferenceService, integrating LLMs (GPT-4, Claude, LLaMA), fine-tuning, vector DBs (ChromaDB, pgvector), RAG pipelines.
4. Testing — load tests, pentesting for data isolation. We achieve 99.9% uptime and p99 latency <200ms.
5. Deployment — CI/CD, monitoring (Grafana + Prometheus), documentation.
6. Support — SLA, refinements for new requirements.

Our engineers have 5+ years of MLOps experience and 20+ implemented AI platforms. We use proven solutions: PostgreSQL RLS, Kubernetes, vLLM for inference. We guarantee compliance with GDPR and 152-FZ.

class TenantOnboardingService:
    async def provision_tenant(self, signup_data: dict) -> Tenant:
        tenant = await self.db.create_tenant(signup_data)
        await self.db_manager.create_schema(tenant.id)
        await self.db_manager.run_migrations(tenant.id)
        await self.storage.create_tenant_prefix(tenant.id)
        await self.config_store.create_default_config(tenant.id)
        api_key = await self.auth.create_api_key(tenant.id, scope="all")
        await self.email.send_welcome(tenant, api_key)
        return tenant, api_key

Typical Mistakes in Multi-Tenant Implementation

1. Lack of tenant-aware caching — cache from one tenant could serve data to another. Use tenant_id as part of the cache key.
2. Weak isolation at the application level — filtering by tenant_id in code rather than at the DB level risks accidental leaks. Always combine RLS with middleware checks.
3. Wrong choice of multi-tenancy model — for a small number of tenants, shared schema works, but as it grows, latency spikes. Plan for potential migration to separate schema without downtime.

Why Our Architecture Is More Cost-Effective?

Compare: Shared DB + Separate Schema is 3–5 times cheaper than a separate database per tenant when serving 50+ clients. Infrastructure savings reach up to $10,000 per month for 50+ tenants. And performance — p99 latency below 200ms even with 1000 concurrent requests (thanks to connection pooling and per-tenant indexes). Return on investment occurs within 6 months after launch.

Timeline and Cost

Development takes from 3 to 5 months depending on the complexity of AI modules and the number of tenants. The exact cost is determined after an audit — contact us for a consultation and get a preliminary assessment of your project. Typical budget is $50,000–$150,000.

MLOps: Infrastructure for Training, Deploying, and Monitoring ML Models

The model is trained, metrics — F1 0.94 on validation. Three months later in production, quality drops by 12%. No one knows when — there is no monitoring. It's impossible to retrain quickly — the training script is in a Jupyter notebook of a data scientist who has already left. Data for retraining is collected manually from three disparate systems. About half of the projects come to us with this pain. We build a turnkey MLOps platform: from experiment tracking to automatic deployment and data drift monitoring. We will assess your infrastructure in 1–2 weeks, and in 4–6 weeks you will get a basic MLOps core running in production. Our team has 10+ years of experience in ML infrastructure, over 50 implementations.

How does MLOps infrastructure benefit your ML projects?

Experiment Tracking and Reproducibility

Without tracking, an ML project turns into chaos: it's unclear which checkpoint is better, which hyperparameters were used, which dataset. Reproducing a result a month later is a quest.

Why is experiment tracking the foundation of reproducibility?

MLflow is an open source standard for tracking. It logs parameters, metrics, artifacts (models, graphs), and code. MLflow Model Registry is a centralized model storage with versioning and lifecycle stages (Staging → Production → Archived). Deployment via MLflow Serving or integration with external systems.

Typical initialization in code:

import mlflow

mlflow.set_experiment("fraud-detection-v2")
with mlflow.start_run():
    mlflow.log_params({"learning_rate": 3e-4, "batch_size": 64, "epochs": 10})
    mlflow.log_metric("val_f1", val_f1, step=epoch)
    mlflow.pytorch.log_model(model, "model")

This is the minimum. In production, we add logging of system metrics (GPU utilization, memory), dataset (hash, version), code (git commit hash). Weights & Biases — richer UI, collaboration features, sweep for hyperparameter optimization. MLflow — for on-premise deployment without external dependencies.

DVC (Data Version Control) — versioning of data and models on top of git. Data is stored in S3/GCS/Azure Blob, only metadata (hashes) in git. dvc repro reproduces the entire pipeline from raw data to metrics.

To ensure reproducibility of training, fix random seeds (torch.manual_seed, numpy.random.seed, random.seed) and record them in experiment metadata. Without this, debugging irregular results is painful. Log the dataset version (DVC hash) and git commit — then any experiment can be reproduced down to the byte.

Pipeline Orchestration: Kubeflow, Airflow, Prefect

A pipeline orchestrator becomes necessary when: A 100-line training script in cron is fine for simple tasks. But as soon as you have a multi-step pipeline (data loading → preprocessing → feature engineering → training → validation → deployment if quality above threshold), you need an orchestrator with retry logic, visualization, and alerts.

Kubeflow — Kubernetes-native orchestrator for ML (see Kubeflow). Each step is a Docker container. Supports parallel steps, conditional branches, artifacts between steps. Integrates with Katib (AutoML), KServe (serving), Feast (feature store).

Apache Airflow — more general DAG orchestrator. Wide ecosystem of operators (S3, Spark, DBT, Kubernetes). Easier to deploy if Airflow already exists in the company.

Prefect / Metaflow — less boilerplate. Prefect 2.x with @flow and @task decorators — quick start for small teams.

Typical training pipeline architecture on Kubeflow:

1. Data ingestion component — fetches data from S3/DB, validates schema via Great Expectations
2. Preprocessing component — transformations, normalization, train/val/test split
3. Training component — training on GPU, logging to MLflow
4. Evaluation component — metric calculation, comparison with baseline in Model Registry
5. Conditional deployment — deploy only if new model is better than current by >2% F1

Each component is a separate Docker image. Pipeline is versioned in git. Scheduled run (retraining once a week on new data) or manual.

Model Registry and Lifecycle Management

Model Registry is not just a checkpoint store. It is a centralized system that knows:

• Which model is currently in production (and with what metrics)
• History of all versions with training parameters
• Metadata: dataset, git commit, validation results
• Lifecycle stage: None → Staging → Production → Archived

MLflow Model Registry — standard. For enterprise — Vertex AI Model Registry (GCP), SageMaker Model Registry (AWS), Azure ML Model Registry.

Model promotion through stages: automatically move model to Staging after successful eval, then manual or automatic (during A/B test) promotion to Production. Rollback — switch to previous Production version in seconds.

Serving: From FastAPI to Triton Inference Server

Simple case. FastAPI + PyTorch/ONNX on one server — 80% of production ML deployments are exactly that. Sufficient for most tasks with load up to 100 req/s.

from fastapi import FastAPI
import onnxruntime as ort

app = FastAPI()
session = ort.InferenceSession("model.onnx", providers=["CUDAExecutionProvider"])

@app.post("/predict")
async def predict(request: PredictRequest):
    inputs = preprocess(request.text)
    outputs = session.run(None, {"input_ids": inputs})
    return {"label": postprocess(outputs)}

Triton Inference Server — production standard for high loads (500+ req/s). Dynamic batching, concurrent model execution, model ensemble. Supports TensorRT, ONNX, PyTorch TorchScript, TensorFlow SavedModel.

KServe — Kubernetes-native ML serving with autoscaling, canary deployments, A/B testing out of the box. Scale-to-zero for inactive models — savings on infrastructure up to 40% annually for a project with 10 models.

Monitoring: Data Drift, Model Drift, Infrastructure Metrics

Monitoring — what is usually done last and regretted first. Three levels.

Infrastructure monitoring. Latency (P50/P95/P99), throughput (req/s), error rate (4xx, 5xx), GPU/CPU utilization. Prometheus + Grafana — standard. Alert when P99 latency > threshold or error rate > 1%.

Data drift monitoring. Distribution of input data changes over time. Detect via PSI (Population Stability Index) for numerical features: PSI > 0.2 — strong drift. Chi-squared test for categorical, Kolmogorov-Smirnov test for continuous. Evidently AI — open source library with ready-made drift tests.

Model drift monitoring. If ground truth is delayed (e.g., we know conversion after a week) — monitor real metrics. If not — surrogate metrics: distribution of prediction scores, proportion of confident predictions.

Alerting. Three levels: INFO (minor drift, log it), WARNING (significant, notify team), CRITICAL (quality dropped below threshold — automatic switch to fallback model).

Why is data drift monitoring important?

Without it, you learn about model degradation only from user complaints or ringing SLA. A drift alert allows you to retrain the model in advance, before errors start causing losses. In one of our projects, PSI monitoring detected drift 2 days after a data source change — this saved the campaign.

Feature Store

Feature Store solves the training-serving skew problem. If preprocessing during training and inference is implemented in two different places — divergence is inevitable.

A Feature Store is needed when:

• Several models use the same features
• Features are computed from streaming data (real-time)
• Large team with different people on feature engineering and model training

Feast — open source Feature Store. Offline store (S3 + Parquet) for training, online store (Redis, DynamoDB) for low-latency inference. Feature definitions as code, materialization job syncs offline → online.

Tecton (commercial), Vertex AI Feature Store (GCP), SageMaker Feature Store (AWS) — managed options with less ops overhead.

CI/CD for ML

ML CI/CD is regular CI/CD plus specific ML steps.

ML-specific checks in CI:

• Reproducibility check: run training with a fixed seed, result must match
• Data validation: Great Expectations or Pandera on schema/distribution checks
• Model performance check: automatic eval on holdout, block merge if degradation > threshold
• Latency regression test: inference must meet SLA

GitOps for deployment. Merge to main → CI triggers training → eval → if passes → automatic deployment to Staging → smoke tests → manual promotion to Production or automatic upon successful canary.

Tools: GitHub Actions / GitLab CI for CI, ArgoCD for GitOps deployment on Kubernetes.

What's Included in MLOps Platform Development

We provide a full cycle of work, documentation, and team training.

Total time from audit to full MLOps platform: 3–5 months. Also possible phased launch: basic level (tracking + serving) in 4–6 weeks.

Cost is calculated individually based on data volume, number of models, and infrastructure requirements. Order an MLOps infrastructure audit — get a roadmap in 1–2 weeks. Contact us for a project assessment — we will send a preliminary estimate within 2 business days.

Note: warranty on architectural solutions — 12 months. We provide integration certificates with major cloud providers (AWS, GCP, Azure). During our work, we have not lost a single client after the first implementation — the experience of 50+ successful MLOps projects speaks for itself. Get a consultation on building an MLOps platform today.