Migrate from AWS Bedrock to Dedicated GPU: Real-Time Inference Guide

When 200ms of Latency Costs You a Customer

A fintech startup learned this through their A/B tests. Their AI-powered trading assistant needed to respond within 300ms to feel “instant” to users. On AWS Bedrock, Claude 3 Haiku averaged 180ms time-to-first-token — but with a p99 of 850ms. One in a hundred requests felt sluggish. Those slow responses correlated directly with user abandonment: customers who experienced a slow response were 40% less likely to complete their next action. The engineering team tried Provisioned Throughput to stabilise latency, but at $24,000 per month per model unit, the cost made the feature economically unviable for their Series A budget.

Real-time inference demands predictable, low latency — something managed API services fundamentally struggle to guarantee. A dedicated GPU with direct hardware access delivers consistent sub-100ms first-token latency without sharing compute with other tenants. Here’s the migration path from Bedrock.

Why Bedrock Latency Is Unpredictable

Bedrock’s latency variability stems from its multi-tenant architecture. Your request shares infrastructure with thousands of other customers. During peak hours, queuing adds 50-400ms before your request even reaches the model. Provisioned Throughput reduces but doesn’t eliminate this — you’re still on shared infrastructure, just with a reserved lane.

The p99 gap is the killer metric. For real-time applications, your worst-case latency defines user experience, not your average.

Migration Steps for Real-Time Workloads

Step 1: Profile your latency requirements. Define your SLA: what’s the maximum acceptable TTFT? For voice agents, it’s 200ms. For trading assistants, 300ms. For interactive search, 500ms. This drives your GPU and model selection.

Step 2: Select optimised hardware. For the lowest latency, choose an RTX 6000 Pro from GigaGPU — RTX 6000 Pros deliver 2-3x faster inference than RTX 6000 Pros for the same model. If budget is constrained, an RTX 6000 Pro 96 GB still beats Bedrock’s latency handily.

Step 3: Deploy with latency-optimised settings. vLLM has several knobs for minimising latency:

python -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --max-model-len 4096 \
  --enforce-eager \
  --disable-log-requests \
  --port 8000

For real-time workloads, smaller models win. Llama 3.1 8B on an RTX 6000 Pro achieves 20-30ms TTFT — faster than most API providers can even route your request. If you need higher quality, Llama 3.1 70B on an RTX 6000 Pro delivers 50-80ms TTFT.

Step 4: Replace the Bedrock calls. Swap each invoke_model call with an OpenAI-compatible HTTP request. For streaming responses (critical in real-time apps), vLLM’s SSE implementation is production-grade.

Step 5: Load test at peak traffic. Simulate your traffic spikes and measure latency percentiles. On dedicated hardware, latency remains stable under load because you’re not competing for compute — the GPU is yours.

Streaming Optimisation

Real-time applications almost always use streaming. The perceived latency improvement is dramatic: instead of waiting for the full response, users see the first token in under 100ms and watch the response materialise. vLLM’s streaming implementation matches the OpenAI/Bedrock SSE format exactly, so your frontend code doesn’t change.

For applications where every millisecond counts, consider running Ollama as your serving layer — it has extremely low overhead for single-user or low-concurrency real-time use cases.

Cost Comparison

Even the RTX 6000 Pro option beats Bedrock Provisioned on both cost and latency. Compare configurations for your workload at GPU vs API cost comparison.

Latency as a Competitive Advantage

In real-time AI applications, latency isn’t a technical metric — it’s a product differentiator. Your competitors on managed APIs are stuck with 200ms+ TTFT. On dedicated hardware, you can deliver 20-50ms responses that make your AI feel genuinely instant. That responsiveness drives engagement, retention, and conversion.

For broader AWS migration context, see the enterprise chatbot migration and multi-model pipeline guide. The TCO comparison covers infrastructure economics, and our self-hosting guide details the full setup process. Explore open-source model hosting for model selection, and use the LLM cost calculator for precise budgeting. More real-time focused guides are in our tutorials section.

Filed under: Tutorials