Operational Excellence

LLMOps, FinOps &
Observability

We implement robust operational strategies that balance performance, cost, and reliability. From kernel-level inference optimization to real-time forensic monitoring.

Observability

Deep
Insights

Real-time monitoring and distributed tracing for AI systems to detect anomalies and optimize performance.

Operational Dashboard

Real-time CloudWatch Metric Stream
Avg Lat: 142ms
Est. Cost: $0.42/hr
Token Burst Detected
Anomaly Detection

We set static and dynamic thresholds on token usage. The chart highlights a burst at 10:15, which could indicate a DOS attack or a loop in an agentic workflow.

Business Impact

High latency directly correlates to drop-off rates. We track P95 and P99 latency alongside token costs to optimize profitability.

Diagnostics

System
Health

Tools and strategies for debugging AI systems, from prompt drift to context window overflows.

ReactDigi_Diagnostics_CLI
Error: ContextWindowExceededError
Current Usage: 8500Limit: 8192
OVERFLOW DETECTED (+308 tokens)
Root Cause Analysis

Input prompt + RAG chunks exceed model context limit.

Remediation Applied

Applied Dynamic Chunking Strategy: Reduced 'k' retrieval from 10 to 5 and enabled summary-chaining.

Cost Efficiency

Optimize
Costs

Generative AI can be expensive. We implement strategies to maximize value while minimizing token usage and infrastructure costs.

Prompt Compression System

Reduce inference costs by compressing inputs while maintaining semantic meaning.

Original Prompt (32 tokens)

"I am writing to you today to request that you please provide me with a summary of the attached financial document. The document contains data about Q3 revenue."

Compressed Prompt (9 tokens)

"Summarize attached Q3 financial revenue data."

72%
Cost Savings / Request
Strategy: Token Pruning

Removing stop words, redundant adjectives, and politeness markers automatically.

Strategy: Context Window Opt

Summarizing conversation history into a "rolling window" to prevent context overflow.

High Performance

Latency &
Throughput

Strategies for low-latency inference and high-throughput processing to ensure a snappy user experience.

Throughput vs. Latency

Advanced batch inference strategies.

Latency (ms) Throughput (tok/s)
Our Approach

Trade-off Analysis: "Dynamic Batching" increases throughput by 400% (good for cost) but adds 70ms latency. "Speculative Decoding" cuts latency by 60% (good for UX).

Deep Profiling

Trace Profiling: We identify bottlenecks in the pre-fill vs decode phases of generation to optimize kernel execution.

Deployment Strategy

Scaling Architectures

Deploying FMs isn't one-size-fits-all. We architect specific solutions based on your traffic patterns. On-Demand (Serverless) is perfect for spiky, unpredictable workloads, while Provisioned Throughput guarantees latency for mission-critical, sustained applications.

Traffic Simulator

Current Load:High Spike
Scale Behavior: The system automatically spins up new Lambda instances to handle the spike.
Trade-off: You may experience 3-5s "Cold Starts" during the sudden jump at t=10.

GPU Inference Engine

Model: Llama-3-70B

VRAM
80GB
Tokens/Sec
120
Weights
A100 (80GB)
Quantization (AWQ)
FP16
Continuous Batching
Standard Attention
Inference Server (vLLM)
Docker Container
NVIDIA Driver / CUDA
Containerization & Optimization

Engineering for Inference

Deploying LLMs differs from traditional software. They are memory-bound and compute-heavy. We implement specialized container patterns using Quantization to reduce memory footprint by 75% and Continuous Batching to saturate GPU utilization without spiking latency.

Tech Stack

  • TensorRT-LLM / vLLM: High-performance serving engines.
  • AWQ / GPTQ: 4-bit weight compression.
  • FlashAttention-2: Memory-efficient exact attention.
Deployment Optimization

Intelligent Cascading

Why pay for a PhD-level model to answer elementary questions? We implement Model Cascading. A lightweight "Router Model" analyzes query complexity and directs traffic. Simple queries hit cheap/fast models; complex ones hit capability-rich models.
Cost Savings
~60%
Latency Drop
~40%
Incoming Query
"Hello, how are you?"
Router
Nano (7B)
$0.20/M20ms
Ultra (70B+)
$10.00/M800ms
Request Processed by NANO