Latency & Throughput Proofs
"p95/p99 latency, token/sec, and cost ceilings are validated on your workloads before production rollout."
Agents are latency-sensitive. A 500ms overhead on a tool call kills the user experience. In our Performance Lab, we benchmark your specific agentic workloads against our control plane.
01. The Challenge
A real-time market intelligence platform needed to analyze thousands of news signals per second to update trader dashboards. Standard LLM API latency was unacceptable for their "near real-time" SLA. Additionally, the sheer volume of redundant queries was driving inference costs to unsustainable levels. They needed a solution that offered predictable low latency without sacrificing response quality, with monthly API costs exceeding budget thresholds and growing rapidly.
02. The Solution
The Performance Lab engagement can focus on two key optimizations: Semantic Caching and Dedicated Throughput tuning. We deploy the AKIOS Gateway at the edge, close to their ingestion services. By implementing a semantic cache, we can serve 40% of queries instantly from memory without hitting the LLM provider. For the remaining cache misses, we route traffic through a provisioned throughput tier that targets p99 latency stability, minimizing the "cold start" and noisy neighbor penalties of public APIs.
The implementation can take 6 weeks, including a 2-week performance benchmarking phase where we stress-test their actual workloads in a staging environment. This ensures the optimizations would hold under peak trading hours when news volume spikes 300% above average.
- Cache Hit Rate
- ~40%
- Policy Overhead
- < 2ms
- Throughput
- High-performance
- Latency p99
- < 50ms
- Monthly Savings
- Substantial
- Uptime Target
- 99.99%
03Technical Implementation
Core Architecture
- AKIOS Performance Gateway deployed at the network edge for minimal latency
- Real-time traffic routing with intelligent load balancing across model providers
- Shadow Mode deployment for performance validation before production rollout
- Multi-region architecture with automatic failover and geo-routing
Performance Optimization
- Semantic caching with vector-based similarity search (Cosine Similarity > 0.95)
- Protocol optimization migrating from JSON/REST to gRPC/Protobuf
- Speculative decoding for non-critical workloads increasing token generation speed
- Request deduplication and batching to reduce API call overhead
Cost Management
- Dynamic model routing based on cost-performance trade-offs
- Automated scaling with provisioned throughput tiers
- Usage analytics and cost prediction with budget enforcement
- 99.7% uptime SLA with cross-region redundancy
04Implementation Roadmap
Phase 1: Assessment (Weeks 1-2)
- Deploy performance monitoring agents across your infrastructure
- Establish baseline latency and throughput measurements
- Analyze current API usage patterns and cost drivers
Phase 2: Optimization (Weeks 3-4)
- Implement semantic caching with vector similarity search
- Deploy protocol optimizations (gRPC/Protobuf migration)
- Configure dynamic model routing and throughput tiers
Phase 3: Validation (Weeks 5-6)
- Execute comprehensive performance testing under peak loads
- Validate cost savings and latency improvements
- Conduct production simulation with real traffic patterns
Phase 4: Production (Week 6)
- Graduated rollout with performance monitoring active
- Establish automated scaling and cost optimization policies
- Enable continuous performance monitoring and alerting
Ready to build?
Start your own Performance Lab today and see the difference.