Real Time vs Batch Inference Architectures describe how machine learning predictions are delivered either instantly during a request or on a schedule as large jobs. Real time paths emphasize low latency, fresh features, and elastic autoscaling. Batch paths prioritize throughput, cost efficiency, and stable reproducibility for analytics or downstream systems. Teams often combine both to meet different service level goals. This guide explains the Top 10 Real Time vs Batch Inference Architectures with clear tradeoffs, data flows, and control planes. You will learn when to choose request response services, streaming pipelines, microbatching, or offline scoring tables, and how to operate them safely.
#1 Request response microservice with online feature store
An online microservice exposes a synchronous API that performs inference during the request. Features are retrieved from an online store such as Redis or a managed key value service that mirrors an offline warehouse. A feature service computes last mile transformations to reduce skew. The model is loaded into memory and kept warm through autoscaling based on concurrency. This pattern suits recommendations, ranking, and fraud checks that cannot wait. It requires strict latency budgets, circuit breakers, and degraded modes. Batch complements it by backfilling features and generating offline labels to keep training and monitoring aligned.
#2 Event driven serverless inference with queues
Event driven inference triggers models asynchronously when new events arrive. Producers write messages to a queue or log, and serverless workers pull batches for efficient parallel scoring. Payloads may include references to objects in storage to keep messages small. This architecture handles spiky workloads without pre provisioning capacity, and it isolates failures through retry policies and dead letter streams. Latency is higher than request response but predictable at minute scale. It pairs well with batch windows that compact topics and build feature snapshots, while the real time stage publishes outputs to sinks for downstream consumers.
#3 Streaming microbatching with sliding windows
Streaming microbatching processes events in small windows to balance throughput and freshness. Frameworks like Spark Structured Streaming or Flink aggregate features over tumbling or sliding intervals, then invoke the model in vectorized batches for better hardware utilization. End to end delay depends on window size and watermarking configuration that controls out of order handling. This pattern is ideal when features depend on counts, rates, or recency metrics. It lowers serving churn compared to per record scoring while avoiding long batch delays. Operators monitor lag, checkpoint health, and skew, and coordinate with offline rebuilds for historical consistency.
#4 Lambda architecture hybrid serving layer
Lambda architecture combines a speed layer for real time approximations with a batch layer that computes authoritative results. The serving layer merges both, preferring the batch view when it arrives. For inference, the speed layer scores new events quickly using streaming features, while overnight jobs recompute predictions over the full dataset to correct drift or late data. Consumers read from a unified table that swaps entries when batch outputs land. This approach ensures low latency and accuracy but adds complexity in reconciliation and cost. Governance requires idempotent writes, versioned models, and clear retention policies for both paths.
#5 Kappa streaming with materialized views
Kappa architecture relies on a single streaming pipeline where all computations, including corrections, are expressed as stream processing jobs. For inference, models subscribe to the log, compute features in motion, and emit predictions to materialized views that are rebuilt by replaying the log when code changes. There is no separate batch layer, which simplifies duplication concerns and keeps results consistent. Latency matches stream processing guarantees, often seconds. Storage costs can rise because raw events are retained for long periods to enable reprocessing. Operators design schemas, compaction, and checkpoints carefully, and expose stable read views for consumers.
#6 Offline batch scoring with prediction table and join at read time
Offline batch scoring computes predictions for many entities on a schedule and writes them to a prediction table. Applications query this table and join by entity key at read time, often via a feature store or data warehouse with serving APIs. This is efficient for personalization, churn risk, or propensity scores that do not change minute by minute. It simplifies service design because no online model hosting is required. Latency equals query time, not model runtime, but freshness is limited by the job cadence. Teams add incremental pipelines and backfills, and track model version columns for lineage.
#7 Offline heavy preprocessing with online lightweight scoring
Some systems precompute heavy features and candidate sets offline, then perform a lightweight online scoring step to personalize the final choice. The offline stage runs nightly to build embeddings, clusters, or top candidates per user, stored in an online cache. The real time service retrieves candidates and applies a small model, calibration, or business rules to adapt to context. This yields low latency without requiring expensive online feature joins. Accuracy depends on the freshness of offline artifacts, so teams schedule partial refreshes and track staleness. This pattern is common in search, feed ranking, and ad retrieval.
#8 Edge inference with periodic batch synchronization
Edge inference places the model near the data on devices or gateways to meet strict latency and privacy needs. Devices pull model bundles and configuration from a control plane on a schedule, which acts as the batch coordination channel. Predictions happen locally, and summaries stream back for telemetry and evaluation. This reduces central compute but requires careful rollout and rollback of versions across fleets. Data drift is mitigated by periodic batch training that produces new artifacts for download. The architecture suits vision, speech, and industrial monitoring. Connectivity loss is handled with local queues and eventual synchronization.
#9 Canary, shadow, and batch replay for safe rollout
Deployment safety spans both serving styles. Canary releases route a small fraction of online traffic to the new model and compare metrics before scaling up. Shadow mode scores the same requests without affecting user outcomes, storing results for offline analysis. Batch backfills replay historical data through candidate models to estimate uplift and risks before any exposure. A policy engine coordinates these routes, and a registry tracks versions, schemas, and thresholds. The architecture reduces regressions, enables rapid iteration, and unifies governance across pipelines. It also highlights feature skew and label leakage early, improving trust in updates.
#10 Tiered SLA hot path and cold path with cost controls
A tiered SLA design separates a hot path for interactive latency from a cold path optimized for cost and completeness. The hot path uses small models, feature caches, and strict timeouts to guarantee response limits. The cold path runs richer models as batch or microbatch jobs that overwrite or annotate hot predictions for analytics and downstream actions. A control plane enforces budgets, rate limits, and autoscaling policies per tier. Teams monitor load, freshness, and accuracy per segment, and dynamically switch between tiers during incidents. This architecture aligns service quality with business value and infrastructure spend.