In-Memory Computing 2025: Powering Generative AI and Real-Time RAG

In January 2025, the CTO of a large Spanish insurer confided in us: “We have 47 AI models in production, but 68% of RAG queries exceed 3 seconds of latency. Our autonomous agents fail because of timeouts, not because of a lack of intelligence.” This is not an isolated case. According to the McKinsey State of AI 2025 Report, 73% of companies scaling GenAI cite data access latency as their main technical bottleneck, ahead of GPU costs or model quality.

In-Memory Computing (IMC) has ceased to be a database optimization and has become the critical infrastructure layer underpinning agentic architectures, multimodal RAG and streaming analytics in 2025-2026. At BAOSS we have accompanied 12 organizations through this transition over the last 18 months. The patterns repeat: whoever solves data latency unlocks the ROI of generative AI.

The real 2025-2026 problem: latency kills autonomous agents

Autonomous AI agents (LangGraph, CrewAI, AutoGen, Semantic Kernel) run reasoning chains of 5-15 steps. Each step requires context retrieval: vector embeddings, conversation history, transactional data, features from feature stores. A round-trip to disk (even NVMe) adds 2-8 ms; to a traditional database, 15-50 ms. Multiplied by 10 steps and 100 concurrent users, the latency queue collapses the experience.

Multimodal RAG: retrieval of text, image, audio and table chunks in < 100 ms end-to-end.
Online feature stores (Feast, Tecton, Hopsworks): feature serving for real-time inference in < 10 ms p99.
Massive context windows: GPT-4o (128k), Claude 4 (200k), Gemini 1.5 Pro (2M tokens). Load relevant context in milliseconds, not seconds.
MCP (Model Context Protocol): standardizes how agents access tools and data. It requires backends with sub-millisecond latency.

The volume of unstructured data grows 28% year over year (IDC 2025). 90% of it is generated at the edge (IoT, sensors, logs, user interactions). Processing this in nightly batch no longer works: dynamic pricing, real-time fraud detection and contextual recommendation decisions demand operational analytics on fresh data.

In-Memory Computing in 2025: beyond SAP HANA

The IMC landscape has matured. We are no longer talking only about relational databases in RAM. The 2025-2026 reference architecture combines:

Distributed In-Memory Grids: Apache Ignite, Hazelcast, Redis Enterprise, GridGain. They scale horizontally and support SQL, ACID and colocated compute.
In-memory native vector databases: Milvus, Qdrant, Weaviate, Pinecone (with a hot memory tier). Optimized for ANN (Approximate Nearest Neighbor) with p99 latency < 5 ms.
In-memory columnar OLAP: Apache Druid, ClickHouse, StarRocks. Streaming ingestion (Kafka, Pulsar) + sub-second analytical queries over TBs.
Persistent Memory (PMEM / CXL): Intel Optane (legacy), CXL 2.0/3.0 (current). TBs of capacity per socket with DRAM-like latency and native persistence. Key for warm restarts without a full reload.

At BAOSS, our 2025 reference stack for agentic workloads combines: Redis Enterprise (L2 cache + vector search) + Apache Druid (streaming analytics) + Feast on Redis (online feature store) + vLLM / Ollama (local inference) orchestrated with LangGraph. All on Kubernetes (EKS/AKS/GKE) with CXL-enabled nodes for hot tiers.

BAOSS Case #1: Retail banking — Onboarding agents with multimodal RAG

Context: Top-5 Spanish bank. 12M customers. Digital onboarding with a conversational agent (GPT-4o + LangGraph) that verifies identity, assesses risk, recommends products and signs contracts in a single session.

Problem: Average latency of 4.2 s per RAG query (KYC documents, transaction history, product catalog of 50k SKUs). The agent failed at step 3/7 due to timeout (3 s). Abandonment rate: 34%.

BAOSS solution (8 weeks):

Migration from PostgreSQL + pgvector to Redis Enterprise 7.4 (vector search + JSON + search). HNSW indexes in RAM, AOF persistence every 1 s.
Online feature store (Feast + Redis) for 240 risk features (credit bureau, behavior, device fingerprint). Serving p99 of 4.8 ms.
L2 semantic cache: embeddings of frequent chunks (FAQs, legal clauses) preloaded. Hit rate of 78%.
Orchestration with LangGraph + custom MCP servers for banking tools (core query, scoring, signature).

Results (production, Q3 2025):

RAG p99 latency: 4.2 s → 380 ms (-91%).
Onboarding abandonment rate: 34% → 12%.
Inference cost per session: -42% (fewer retries, cached tokens).
ROI: 3.8x in 5 months (conversion increase + operational savings).

BAOSS Case #2: Omnichannel retail — Dynamic pricing and recommendation at the edge

Context: Fashion retailer with 350 stores + e-commerce. 2.1M active SKUs. Goal: dynamic pricing by store/channel/hour + personalized recommendation in app/POS in < 50 ms.

Problem: Nightly batch pipeline (Spark + Snowflake) → data up to 24h old. Pricing models (XGBoost + LightGBM) retrained weekly. They failed to capture local stock-outs, TikTok trends or weather.

BAOSS solution (12 weeks):

Apache Druid ingesting 4.5M events/hour (sales, clicks, stock, weather API) from Kafka. Automatic rollup, sub-second SQL queries over 18 months.
Hybrid feature store: Feast offline (Iceberg on S3) + online (Redis Cluster 7.2 on CXL nodes). 1,200 features/store/hour.
Edge inference (stores) with Ollama + vLLM serving quantized (4-bit) Llama-3.1-8B-Instruct models for recommendation. p99 latency of 28 ms.
Pricing agents (CrewAI) querying Druid + the feature store every 15 min. Automated A/B testing.

Results (production, Q4 2025):

Gross margin increase: +2.3 pp (pricing reactive to local demand).
App recommendation CTR: +31% (fresh context + local model).
Stock-out reduction: -47% (real-time signals to replenishment).
Infrastructure TCO: -28% vs. Snowflake + Dataflow (fewer bytes scanned, colocated compute).

BAOSS 2025-2026 reference architecture: the Hot layer for Generative AI

After 12 implementations, we have consolidated an architecture pattern that we deliver to clients as a blueprint:

Layer	2025 Technology	Target SLI	Use case
L1: Vector Search & Cache	Redis Enterprise 7.4 (HNSW, JSON, Search)	p99 < 5 ms	RAG retrieval, session history, semantic cache
L2: Online Feature Store	Feast + Redis Cluster / Valkey	p99 < 10 ms	Real-time inference, agent tool calling
L3: Streaming OLAP	Apache Druid / ClickHouse Cloud	p99 < 200 ms	Operational dashboards, agent analytics loops
L4: In-Memory Compute Grid	Apache Ignite / Hazelcast	p99 < 2 ms	Distributed locking, session state, compute colocation
L5: Persistent Memory	CXL 2.0/3.0 (Intel Xeon 6 / AMD EPYC Turin)	TBs capacity/socket	Warm restarts, large embeddings, checkpointing

The key is not to stack technologies, but intelligent data tiering: keeping hot data in the lowest-latency layers and automatically moving cold data to cheaper tiers, based on real access patterns.

In short: the data architecture for AI agents is a layered latency problem, not a single miracle database. Each tier —semantic cache, feature store, streaming OLAP, in-memory grid and persistent memory— plays a role, and the value emerges from orchestrating them with judgment.

Is your data infrastructure ready for real-time agents? At BAOSS we design low-latency data architectures for AI. Tell us about your project.

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Advantages and Uses of In-Memory Computing