Big Data Architecture 2025: Generative AI, Autonomous Agents and Hybrid Cloud

In 2016 we published the first version of this article. Back then, the debate was on-premise vs. cloud and the challenge was storing petabytes. In 2025, the problem has mutated. Companies no longer just need to “store data”. They also need infrastructure ready for real-time inference, enterprise RAG and agentic workflows. The bottleneck is no longer storage. It is GPU latency, vector governance and the orchestration of autonomous agents that reason over that data.

At BAOSS we have accompanied twelve organisations (banking, logistics, retail, the public sector) throughout 2024-2025. All of them were moving from passive data lakes to active intelligence platforms. Moreover, the failure patterns — and the success ones — repeat themselves. This article synthesises what we have learned deploying architectures. Today they serve GPT-4o, Claude 4 Sonnet, Llama 3.1 405B and local models via Ollama/vLLM in production.

The real 2025 problem: your infrastructure is not ready for agents

78% of the CIOs surveyed by Gartner at the close of 2024 acknowledge it. Their data stack “does not support agentic workloads”. The reason is, ultimately, structural. Traditional architectures — even the “modern” ones from 2021-2022 — separate storage, compute and serving into silos. Those silos add unacceptable latency when a LangGraph or CrewAI agent needs to act. It must query a vector index, invoke an MCP tool and generate a response in <200 ms.

Metadata fragmentation: Catalogs (Unity, DataHub, Amundsen) live disconnected from the model registry (MLflow, Weights & Biases). Also from the inference gateway.
GPU starvation: Shared Kubernetes clusters where batch training workloads compete with low-latency inference. All of it — without priority classes or GPU time-slicing configured.
Vector DB sprawl: Pinecone, Weaviate, Qdrant, Milvus, PGVector coexisting without a tenant isolation strategy or a unified hybrid search.
Blind observability: Infrastructure logs and metrics, but no distributed traces. That is why nobody can follow a request from the user’s prompt to the chunk retrieved in RAG.

A client from the logistics sector (anonymised: “Client LOG-2024”) came to us with 3.2 PB in S3 and an underused Databricks cluster. They also brought 14 GenAI PoCs stuck at “it works in my notebook”. The diagnosis was twofold: absence of a unified orchestration layer and inference costs 4.7x above target. The cause: using managed endpoints without batching or speculative decoding.

The BAOSS 2025 reference architecture: four planes, one governance

Our proposal is not a PowerPoint diagram. It is the blueprint we deploy in production (AWS/GCP/Azure + on-prem GPU). It relies on versioned Terraform modules, GitOps via ArgoCD and policy-as-code (OPA/Kyverno). These are four decoupled but end-to-end observable planes:

1. Unified Data Plane

Objective: Eliminate fragile ETLs. We apply zero-copy on open formats (Iceberg, Delta Lake, Hudi) with a single catalog (Polaris/Unity/OSS). At Client LOG-2024 we migrated 1.8 TB/day of Kafka ingestion → Iceberg on S3 Tables + Polaris. This reduced the freshness latency from 4 h to 3 min. In addition, storage costs dropped by -32% (automatic compaction + real partition pruning).

Ingestion: First, Kafka Connect / Redpanda + Debezium CDC → Iceberg streaming writes.
Catalog: Then, Apache Polaris (incubating) with table/column-level RBAC — integrated with Ranger/Entra ID.
Feature Store: Also, Feast on Iceberg (online: Redis Cluster; offline: parquet partitioned by entity/time).
Vector Index: Finally, Qdrant Cluster (managed/cloud) or Milvus Distributed on-prem with hybrid search (BM25 + dense) and multi-tenancy by namespace.

2. Model Serving Plane

Objective: Serve any model (proprietary or open-weight) with predictable latency and cost SLAs. We deploy vLLM + Kubernetes (KServe/Knative). The configuration includes prefix caching, chunked prefill and speculative decoding (draft model Llama 3.1 8B → target 70B/405B). For proprietary models we use a unified gateway (Portkey / LiteLLM / Kong AI Gateway). It adds fallback routing, budget enforcement and pre-inference PII redaction.

GPU Pooling: First, NVIDIA GPU Operator + Time-slicing (A100 80GB → 7 slices) + MPS for light workloads. MIG, on the other hand, covers strong isolation.
Autoscaling: Also, a KEDA scaler based on queue_depth + gpu_utilization + cost_per_1k_tokens.
Model Registry: Likewise, MLflow + mandatory model cards (data lineage, eval benchmarks, licence, SLA).
Observability: First, OpenTelemetry traces propagated from gateway → vLLM → vector DB → feature store. Then, Grafana dashboards cost/latency/quality per tenant.

Result at Client LOG-2024: cost/1k tokens -68% (from €0.042 to €0.013 blended) and p95 latency 180 ms (previously 1.2 s). The 4.3x throughput was achieved on the same hardware.

3. Agentic Orchestration Plane

Objective: Turn prompts into auditable, versioned and testable workflows. For example, we do not use the “framework of the moment”. We choose based on state control, native human-in-the-loop and observability:

LangGraph (Python/TS): Persistent state (PostgreSQL/Redis), automatic checkpointing, reusable subgraphs, native streaming. It is ideal for multi-step agentic RAG (retrieve → grade → rewrite → generate → verify).
CrewAI: On the other hand, it applies when the pattern is role-based collaboration (researcher → analyst → writer → reviewer) with dynamic delegation.
AutoGen: Likewise, for multi-agent debate and sandboxed code execution (data analysis, SQL generation).
MCP (Model Context Protocol): A unified tools layer — we no longer write ad-hoc wrappers. We also expose internal MCP servers (read-only SQL, ERP API, filesystem, vector search) via stdio/SSE with OAuth2 mTLS.

Key metric: new agent deployment time: 2 days → 4 hours. It is enabled by the LangGraph template + CI/CD and the automatic eval harness with LangSmith/Langfuse.

4. Governance & FinOps Plane

Objective: Make every euro spent on GPUs and every token generated traceable to a business case. To achieve this, we implement:

Real-time FinOps: Kubecost + OpenCost + team, project, model, environment labels. Slack/Teams alerts if daily_cost > budget * 0.8.
Data Contracts: Schema registry (Confluent/Apicurio) + data quality gates (Great Expectations / Soda Core) in CI/CD. These gates fail the deploy if null_rate > threshold or drift_score > 0.15.
AI Governance: A registry of System Cards (model, training data, bias eval, EU AI Act risk). Also, human review gates in LangGraph for high-risk decisions (credit, compliance, pricing).
Privacy: Finally, PII detection/redaction (Presidio + custom regex) at the pre-inference gateway; differential privacy in aggregated analytics.

Real anonymised cases: the metrics that matter

Client (sector)	2024 challenge	BAOSS solution	Metrics 6 months post-go-live
BAN-2024 (Mid-size bank)	12 GenAI PoCs with no path to prod; regulatory risk	Unified platform vLLM + LangGraph + Polaris; EU AI Act gates	ROI 3.2x (€1.4M/year saved in the contact centre); 0 compliance incidents; 8 agents in prod
RET-2025 (Omnichannel retail)	Real-time personalisation <100 ms; 4M SKU catalogue	Feast/Iceberg Feature Store + Qdrant hybrid + vLLM speculative decoding	CTR +23%; p99 latency 87 ms; cost/inference -61%
LOG-2024 (Logistics)	Last-mile route optimisation with an LLM reasoning over constraints	AutoGen + MCP (ERP, GIS, traffic API) + human-in-the-loop dispatcher	Km/route -14%; CO2 -11%; deployment in 3 weeks (vs 6 months estimated internally)
ADM-2025 (Public administration)	Multi-language citizen assistant; data sovereignty	Ollama + vLLM on-prem (H100 GPU) + Llama 3.1 70B Instruct + legal RAG	95% of queries resolved without a human agent; 0 data leaves the data centre; fixed monthly cost vs variable cloud

Cloud, On-prem, Hybrid: the 2025 decision is not binary

Ultimately, the “cloud-first” dogma has matured into workload-first. This is our decision matrix with clients:

Workload	2025 recommendation	Rationale
Foundational training / massive fine-tuning (&gt

Prefer to talk to us directly? Contact the BAOSS team and tell us about your challenge.

Big Data Systems and Hardware