Custom AI Endpoint Deployment Platform Comparison: Baseten vs Modal vs RunPod vs GMI Cloud

An H100 on Baseten costs $6.50 per hour. The same GPU tier on RunPod runs approximately $2.69 per hour on demand. On GMI Cloud bare metal, it is $2.00 per hour. All three are called H100 instances. The hardware difference between them is real but smaller than the price difference suggests, and the billing model, cold start behavior, and container abstraction layer each platform wraps around that GPU determine whether the price gap is a good deal or a false comparison.Choosing a custom AI endpoint deployment platform on GPU rate alone misses the variables that most often cause teams to either overpay or underprovision.This piece compares Baseten, Modal, and RunPod across cold start latency, container customization, autoscaling behavior, and per-GPU cost, then shows where bare metal GPU access changes the math.

The Four Variables That Matter in Custom Endpoint Deployment

Teams deploying custom AI endpoints to production care about four things that go beyond the hourly GPU rate:

• Cold start latency: How long does a new container take to accept traffic after a scale-from-zero event? For user-facing APIs with latency SLAs, a 60-second cold start is a production incident. For batch workloads, it is irrelevant.
• Container customization: Can the team bring arbitrary container images, or does the platform impose a specific abstraction layer? Truss on Baseten, decorator functions on Modal, and BYOC on RunPod create different switching costs.
• Autoscaling model: Does the platform scale based on request queue depth, GPU utilization, or a custom metric? Scale-to-zero saves cost on idle workloads. Scale-from-zero creates cold starts.
• Per-GPU cost and billing granularity: Hourly rates, per-second rates, per-minute rates, and always-on replica pricing produce very different bills on the same workload.

These four variables interact. A platform with sub-second cold starts can run at lower replica counts, reducing idle cost. A platform with a rigid container abstraction creates switching costs that make the hourly rate less meaningful over time.

Platform-by-Platform Breakdown

Baseten: enterprise-grade serving, premium pricing

Baseten is built for production ML serving with managed infrastructure. The Truss framework packages models into deployable APIs via a PythonModelclass and aconfig.yamlfile. Baseten handles containerization, TensorRT-LLM compilation, GPU scheduling, and autoscaling.

GPU pricing as of April 2026: T4 at $0.63/hr, A10G at $1.21/hr, A100 (80GB) at $4.00/hr, H100 at $6.50/hr, B200 at $9.98/hr. Billing is per minute.

Cold start behavior is the most frequently cited limitation. Large model containers take 30-90 seconds to become ready on the first request after a scale-down event. Teams handling this in one of two ways: accepting the cold start variance for non-latency-critical workloads, or settingmin_replicasto at least one to keep a warm instance running continuously. The second option eliminates cold starts and restores continuous per-minute billing regardless of traffic volume.

Container customization is possible but constrained by Truss. The framework is well-documented and reduces initial deployment time significantly. It also creates vendor lock-in: migrating off Baseten requires rewriting model wrappers from Truss'sload()/predict()pattern to vLLM, SGLang, or a standard container configuration. Teams with libraries of Truss-packaged models underestimate this switching cost.

Baseten's genuine differentiation is compliance and enterprise support. SOC 2 Type II, HIPAA, private VPCs, and dedicated account engineering for enterprise customers place it in the category of platforms where the per-GPU premium pays for contractual guarantees rather than raw compute efficiency.

Best for: ML teams with compliance requirements, enterprises that need SLA contracts and dedicated support, and workloads where Truss's opinionated structure reduces deployment complexity enough to justify the cost premium.

Modal: Python-native serverless, strong cold start performance

Modal's architecture differs fundamentally from Baseten's. Deployment is done via Python decorators:@app.function(gpu="H100")attaches GPU resources to a function. The platform handles container creation, scaling, and billing. There is no proprietary framework abstraction that creates switching costs.

GPU pricing: A10G at $1.10/hr, H100 SXM at approximately $3.95/hr under sustained load. Billing is per second of active compute. Idle time costs nothing.

Cold start performance is Modal's most cited technical advantage. The Rust-based container stack achieves sub-second cold starts for CPU-bound functions. For GPU containers, typical cold starts run under 2 seconds. For large LLM workloads (70B+ parameter models), cold starts extend to tens of seconds as model weights load from the cache layer. Keep-warm configuration reduces cold start frequency at the cost of adding idle compute charges.

The per-second billing on active compute only means that Modal's effective cost on bursty workloads is substantially lower than platforms billing per replica-hour. A model that handles 1,000 requests per day spread unevenly will cost very differently on Modal versus Baseten, because Modal charges only for the seconds the container is executing.

The limitation is that Modal requires self-configuration of the inference stack.There is no managed vLLM setup or TensorRT-LLM compilation. Teams bring their own serving code. This adds initial setup time and ongoing maintenance compared to Baseten's managed approach.

Best for: Python-first teams building serverless GPU compute pipelines, workloads with variable or bursty traffic, and developers who want flexibility without a proprietary deployment framework.

RunPod: lowest GPU rate, BYOC, FlashBoot for cold starts

RunPod competes primarily on price and hardware flexibility. The GPU rate on H100 SXM runs approximately $2.69/hr on demand, with community marketplace options at lower prices. The platform supports both serverless autoscaling (scale-to-zero with per-second billing) and dedicated pods (persistent instances with per-second billing when running).

Cold start behavioris handled by FlashBoot, which claims sub-250ms cold starts for container spin-up. The important distinction: FlashBoot starts the container quickly, but model weight loading still occurs afterward. A 70B model loaded from NVMe storage takes approximately 18 seconds; loaded from a slower persistent volume, it can take 74 seconds or more. The FlashBoot number and the time-to-first-inference number are different numbers, and the gap matters for production SLAs.

Container customization is the most flexible of the three platforms. Bring your own container, any framework, any model serving approach. No Truss, no decorator pattern, no required abstraction layer. For teams with existing containerized inference stacks that simply need GPU compute, RunPod imposes no switching cost.

RunPod lacks the compliance infrastructure that Baseten provides. SOC 2 and HIPAA certifications are not part of the standard offering. For teams in regulated industries, this may be a non-starter independent of the price advantage.

Best for: Cost-sensitive teams with existing container configurations, developers who need maximum flexibility and are comfortable with manual setup, and workloads where compliance requirements are minimal.

Where Bare Metal GPU Access Changes the Cost Equation

The three platforms above all run on managed infrastructure with abstractions over the underlying GPU. A fourth option exists: direct bare metal access with no hypervisor layer and no platform abstraction.

GMI Cloud's bare metal GPU instances are available at $2.00/hr for H100, $2.60/hr for H200, and $4.00/hr for B200. The comparison against the managed platforms:

GPU	Baseten	Modal	RunPod (on-demand)	GMI Cloud bare metal
H100	$6.50/hr	~$3.95/hr	~$2.69/hr	$2.00/hr
H200	N/A listed	N/A listed	N/A listed	$2.60/hr
B200	$9.98/hr	available	available	$4.00/hr

The H100 cost difference between Baseten and GMI Cloud bare metal is $4.50/hr per GPU.Over 730 hours of operation (one month), that is $3,285 per GPU. A two-GPU deployment saves over $6,500 per month before any other cost consideration.

The tradeoff is the management layer that Baseten and Modal provide. Bare metal access requires the team to configure and maintain vLLM or TensorRT-LLM, handle autoscaling separately, and manage the inference stack. GMI Cloud provides CUDA 12.x, TensorRT-LLM, and vLLM pre-configured on H100 and H200 instances, which reduces the initial setup time. The ongoing operational scope is still larger than a managed platform.

The decision point is utilization. At sustained high utilization (above roughly 60-70% average), bare metal's lower hourly rate produces better economics than managed platforms' per-second idle-saving billing model. At variable traffic with significant idle periods, Modal's per-second active compute billing can undercut bare metal's always-on cost.

GPU access and pricing are atconsole.gmicloud.aiandgmicloud.ai/en/pricing.

Choosing by the Variable That Constrains You

Priority	Recommended platform	Key reason
Enterprise compliance (SOC 2, HIPAA)	Baseten	Only platform with full compliance documentation and dedicated support
Lowest cold start on serverless	Modal	Sub-2s GPU cold start; Rust container stack
Lowest GPU hourly rate (managed)	RunPod	H100 ~$2.69/hr with FlashBoot
Lowest GPU hourly rate (bare metal)	GMI Cloud	H100 $2.00/hr, no hypervisor overhead
Maximum container flexibility	RunPod or GMI Cloud	BYOC, no framework lock-in
Python-native serverless deployment	Modal	Decorator-based, no switching cost
H200 or Blackwell access at competitive pricing	GMI Cloud	H200 $2.60/hr, B200 $4.00/hr
Managed inference stack (vLLM, TensorRT)	Baseten	Truss handles compilation and optimization

The Platform Should Serve the Deployment, Not Define It

The managed platform abstractions that Baseten and Modal provide are worth their premium for teams that cannot absorb the engineering overhead of self-managed GPU infrastructure. For teams that can, the rate difference between Baseten's H100 at $6.50/hr and GMI Cloud's H100 at $2.00/hr compounds every hour the GPU runs.

Container lock-in deserves explicit attention. Truss is functional and well-documented. It is also a proprietary abstraction that makes migrating to lower-cost infrastructure harder later. Teams that expect to grow into high GPU utilization benefit from designing inference deployment with framework-agnostic containers from the start, even if they begin on a more expensive managed platform.

Cold start management is ultimately a capacity planning problem. The platforms that solve it cheaply at low volume (Modal's per-second billing) and the platforms that solve it well at high volume (dedicated replicas on Baseten or bare metal on GMI Cloud) are different answers to the same question at different scales.