5 Criteria Most Teams Skip When Evaluating AI Inference Platforms

When teams evaluate AI inference platforms, they typically focus on two factors: GPU price and model library size. Yet most discover months after deployment that the real cost drivers weren't on that initial checklist. Platforms like GMI Cloud design around these hidden criteria by pre-configuring optimization stacks and publishing benchmark access, but most providers don't. Missing even one of the five criteria below can turn a cost-effective decision into a platform swap. This article covers: cold start latency, auto-scaling mechanics, optimization stack, benchmarking access, and vendor lock-in.

The Hidden Costs of Platform Evaluation

Teams sign contracts believing they've done due diligence, only to realize their inference layer isn't meeting SLAs or consuming 3x the budget they projected. The gap between evaluation and production usually isn't about GPU specs. It's about whether the platform architecture aligns with your traffic patterns, whether your team can actually optimize models on it, and whether you'll be able to move if requirements change.

A first-cut evaluation that misses these five dimensions often leads to costly migrations within the first year.

Criterion 1: Cold Start & Warm-Up Latency

Cold start latency is the time from first request to first token output when no instance is running. On serverless platforms, this commonly ranges from 10 to 30 seconds. For real-time applications, a 20-second cold start is a non-starter, but many evaluation documents gloss over this entirely.

The actual cost of a cold start isn't just latency. A common approach is to maintain at least one warm replica (min-replica=1) so users rarely experience that delay. This removes cold starts but adds idle compute cost. Most teams find that running warm-up scripts post-deployment, loading the model into GPU memory without serving traffic, catches most warm-up overhead upfront rather than distributing it across early requests.

One option is to compare the monthly cost of keeping one H100 idle against the business impact of a SLA violation. If your warm instance costs $1,512/month and a 5-minute downtime window costs you $5,000 in user churn or trust, the math is straightforward. If cold starts only matter during traffic spikes that happen twice a year, that calculation changes entirely. The key is to measure both the technical overhead and its business consequence before committing to a platform's cold-start model.

Criterion 2: Auto-Scaling Granularity

Coarse-grained auto-scaling can waste thousands of dollars each month. Many platforms scale at the node level, meaning you add or remove 8 GPUs at a time. If your workload has two requests per second on an H100 cluster and you need to scale up, you're adding capacity for seven more parallel requests you don't have.

Most teams find better efficiency by verifying that the platform supports per-replica scaling (adding one GPU-backed instance at a time, not one entire node). Equally important is the scaling trigger. GPU utilization-based metrics like "scale up at 70% utilization, scale down at 30%" work well for steady workloads, but requests-per-second or queue-depth triggers often align better with user experience during traffic spikes.

A realistic test here is to run your actual traffic pattern during evaluation and observe how many times the platform would have scaled. If you see unnecessary up-down cycling, that's a sign the platform's scaling algorithm won't match your workload efficiently. Confirming the platform offers both fine-grained scaling and appropriate metrics prevents expensive over-provisioning.

Criterion 3: Runtime & Optimization Stack

An inference platform that doesn't include TensorRT-LLM, vLLM, or comparable optimization software forces your team to compile and optimize models from scratch. This isn't a minor detail. Building a production-grade optimization pipeline typically requires 2 to 4 weeks of engineering work per model family.

It's worth considering whether the platform ships with these precompiled. Specifically, check if TensorRT-LLM is pre-compiled for your target GPUs, if vLLM supports PagedAttention v2 to reduce memory overhead, and if continuous batching is enabled by default. These features directly translate to 20 to 40% better throughput on the same hardware.

If self-optimization is required, factor in that cost during evaluation. A platform that costs 20% more per GPU-hour but includes optimized TensorRT-LLM often proves cheaper than one that forces you to build the optimization layer yourself. Many teams underestimate this hidden labor cost until they're deep into deployment.

Criterion 4: Benchmarking Access

Public benchmarks are useful for initial screening, but real-instance testing is usually needed to validate latency, throughput, and cost under your own workload. Most platforms offer 24 to 48 hours of trial GPU access, enough to run meaningful tests. A good approach is to request a trial node matching your production specs and run a locust simulation with your actual prompt distribution.

Three metrics tend to be the most revealing during trials: tokens per second at batch=1 (single-user experience), p95 latency at peak load, and cold-start-to-first-token time under realistic load conditions. These numbers differ dramatically between platforms, and you'll only see them if you test on actual hardware.

One option is to open a spreadsheet comparing results across two or three shortlisted platforms. If Platform A delivers 80 tokens/sec but Platform B delivers 110 on the same GPU type, that 37% difference will compound across your monthly bill. Skipping this step often leads to underestimating costs in the final decision.

Criterion 5: Portability & Lock-In

Vendor lock-in creates invisible switching costs. After signing, you discover that exporting your model requires a custom script, API-compatible alternatives charge 3x more for egress data, or you've written so much platform-specific code that migration would take months.

Most teams find durability by verifying three portability signals: the platform exposes an OpenAI-compatible API endpoint (/v1/chat/completions standard), model artifacts export in standard formats like SafeTensors or GGUF, and egress pricing is public (anything above $0.05/GB is worth questioning). Additionally, consider writing your own provider-agnostic abstraction layer in your application code, so swapping backends requires only changing a configuration file.

Testing portability during evaluation is straightforward: export a model in an open format, document the API calls your application makes, and confirm both will work with a secondary platform. That confirmation is worth the time when it saves you from being stuck with an underperforming vendor.

Evaluation Checklist

Criterion	What to Test	Red Flags
Cold Start	Request warm-up SLAs and measure 10 cold starts	>30s cold start or no option to pre-warm
Auto-Scaling	Verify per-replica scaling and metric types	Node-level scaling only, no usage-based triggers
Optimization	Confirm TensorRT-LLM, vLLM, PagedAttention v2	"You optimize" or "we'll help you"
Benchmarking	Request 24-48h trial, run locust at batch=1, p95	No trial access or results under NDA
Portability	Export model, confirm OpenAI API, check egress rates	Proprietary export, custom APIs, >$0.10/GB egress

When evaluating platforms, the difference between a thorough assessment and a quick GPU-price comparison often compounds into thousands of dollars of wasted spend. Many teams find that walking through these five criteria before signing contracts transforms inference economics from a cost center into a genuinely optimized operation.

GMI Cloud is worth evaluating against these five criteria. The platform ships with pre-configured TensorRT-LLM, vLLM, and Triton, which addresses the optimization stack criterion. Teams should verify auto-scaling granularity, cold-start behavior, and benchmarking access against their own models during a trial. For portability, it's worth confirming egress pricing and API compatibility directly via gmicloud.ai/pricing, as these details affect long-term cost and lock-in risk.