Baseten vs DeepInfra: Managed Premium Ops vs Cheap Serverless-First Inference

Who Wins Per Workload

The decision is per workload, not per platform. DeepInfra wins on price and convenience; Baseten wins wherever control, custom serving, or compliance is load-bearing.

Quick Comparison

Baseten is the control-and-performance platform; DeepInfra is the price-and-convenience platform.

Billing Models: Replica-Minute vs Per-Token

This is the core difference, and it decides which platform is cheaper for you.

Baseten dedicated deployments bill per replica-minute. Every running replica costs the underlying GPU rate continuously, whether it serves one request or a thousand. That is great at high, steady utilization where you pack a GPU with batched traffic, and expensive when traffic is low or spiky, because idle warm replicas still bill. Baseten also offers Model APIs for pre-optimized open models priced per token, so you can mix both.

DeepInfra serverless is the inverse: you pay per input and output token on shared infrastructure, and pay nothing when you send nothing. There is no replica to keep warm and no idle cost. For dedicated needs, DeepInfra rents whole GPUs by the hour at rates well below Baseten's.

Pricing: DeepInfra Is Cheaper Per Unit

On raw price, DeepInfra undercuts Baseten on both dedicated GPUs and per-token calls.

Baseten's posted GPU rates are roughly 3x to 4x DeepInfra's on comparable hardware. The gap reflects what you get: Baseten's rate bundles its inference stack, autoscaling, multi-cloud scheduling, and managed ops. DeepInfra rents closer to raw capacity.

Both providers price cached input separately, which matters for agentic workloads that resend a large system prompt each turn. DeepInfra also covers embeddings (BGE-M3), image generation (Flux at ~$0.07/image), and speech under the same API, so a multi-modal pipeline stays on one bill.

Cost on a Real Workload

Performance: Cold Starts vs Warm Serverless

DeepInfra's popular models are always warm; Baseten's scale-to-zero trades idle cost for a first-request delay.

DeepInfra reports sub-half-second time-to-first-token on its shared endpoints, with output speeds like 403 tok/s on a small Qwen3.5 model, because high-traffic models never scale down. You can hit 429 errors when a model is saturated, after which autoscaling adds capacity.

Baseten's cold start depends on how you configure autoscaling. Scaled to zero, the first request waits for a replica: about 9 seconds for some models using parallelized byte-range downloads and Firecracker-style snapshots, but 30 to 90 seconds for large containers. To eliminate that, you set a minimum replica count above zero and pay for the warm idle capacity. That is the control-versus-cost tradeoff in one knob.

Neither is built for the coding-agent apply loop; if applying model-generated code edits is the bottleneck, that is a different tool (Morph Fast Apply, ~10,500 tok/s, with published benchmarks).

The Inference Stack: Baseten Goes Deeper

Baseten invests in serving performance as a product; DeepInfra optimizes quietly behind a fixed menu.

Baseten selects between TensorRT-LLM and SGLang per model, applies custom kernels, and supports speculative decoding through draft-target models, Medusa heads, and Eagle self-speculation. Its Chains framework runs each step of a compound pipeline on its own hardware with its own autoscaling, which Baseten credits for 6x better GPU usage on multi-step workloads. Multi-cloud Capacity Management schedules across 15+ cloud providers, so capacity is sourced wherever GPUs are available.

DeepInfra runs its own inference-optimized hardware in US data centers and exposes the result as a flat per-token menu. You do not choose the engine or tune speculative decoding; you get a fast, cheap endpoint and the optimization is DeepInfra's problem. That is a feature if you want simplicity and a limit if you need to squeeze a specific model.

Features & Compatibility

Both speak the OpenAI API; the difference is breadth versus depth.

DeepInfra's edge is coverage: 83 of 84 models support JSON mode and 79 of 84 support function calling, across text, embeddings, image, and speech, all OpenAI-compatible. Baseten's edge is depth: Truss lets you deploy any model with arbitrary serving logic, and the engine builder exposes the knobs DeepInfra hides.

Compliance & Deployment

Baseten is the safer answer for regulated or self-hosted requirements.

Baseten is SOC 2 Type II certified and HIPAA compliant, and supports dedicated, self-hosted, and hybrid deployments. Its multi-cloud model means workloads can run across regions and providers rather than a single fixed footprint, which helps with capacity and data-residency constraints. DeepInfra runs on its own hardware in secure US data centers and is a strong fit when US-based serverless inference is enough, but it is built around a hosted serverless model rather than self-hosted or VPC deployment.

When to Use Baseten

• Custom or fine-tuned models in production. Package anything with Truss, pick the GPU, and get full control over autoscaling and serving logic. This is the platform's core.
• You need the last 20% of performance. TensorRT-LLM or SGLang selection, custom kernels, and speculative decoding (Medusa, Eagle) for a specific model that has to be fast.
• Compound pipelines. Chains runs each step on its own hardware with independent autoscaling, which is why Baseten reports 6x better GPU usage on multi-step workloads.
• Compliance and capacity guarantees. SOC 2 Type II, HIPAA, and multi-cloud scheduling across 15+ providers for data residency and burst capacity.
• High, steady utilization. When a dedicated replica stays near full, per-minute billing you control can beat per-token rates because you keep the batching margin.

When to Use DeepInfra

• Lowest cost per token. Llama 3.3 70B Turbo at $0.10/$0.32 per 1M and dedicated H100 at $1.79/GPU-hr undercut Baseten's posted rates by 3x to 4x.
• Zero setup, no idle cost. Call a popular open model behind an OpenAI-compatible URL. Pay only for tokens, with sub-half-second time-to-first-token on warm models.
• Bursty or unpredictable traffic. Serverless absorbs spikes without you paying for warm replicas between them.
• Multi-modal on one bill. Text, embeddings (BGE-M3), image (Flux), and speech under a single API and account.
• LoRA adapters. Deploy adapters on supported base models without managing dedicated GPUs.

Frequently Asked Questions

Is Baseten or DeepInfra cheaper?

For per-token serverless calls, DeepInfra is cheaper. Llama 3.3 70B Turbo runs at $0.10 input / $0.32 output per 1M, and DeepInfra's dedicated H100 is $1.79/GPU-hr versus Baseten's $6.50/hr. Baseten charges per replica-minute and bills whenever a replica runs, so it costs more for low or bursty traffic but can be competitive at sustained high utilization where you control batching.

What is the difference between Baseten and DeepInfra?

Baseten is a dedicated-deployment platform: ship a model with Truss, pick the GPU, pay per replica-minute, and get a custom inference stack (TensorRT-LLM, SGLang, speculative decoding) plus multi-cloud capacity. DeepInfra is serverless-first: call a shared per-token endpoint for popular open models with no infra to manage. Baseten gives control; DeepInfra gives the lowest price per token with zero setup.

Does Baseten have cold starts?

Yes, for scaled-to-zero deployments. The first request after a scale-down waits for a replica to start, which Baseten optimizes to about 9 seconds for some models, though large containers can take 30 to 90 seconds. Keep a minimum replica count above zero to avoid cold starts, at the cost of paying for idle compute. DeepInfra's shared serverless endpoints report sub-half-second time-to-first-token because the popular models stay warm.

Can I run a fine-tuned model on both?

Yes. Baseten deploys any custom model you package with Truss on dedicated GPUs and supports training at the same GPU rates. DeepInfra supports LoRA adapter deployment and custom hosting for text and image models. Baseten suits full custom models with bespoke serving logic; DeepInfra suits LoRA adapters on supported base models.

When is Baseten worth 3x to 4x DeepInfra's price?

Baseten's premium pays off when you run your own or fine-tuned model and need the tuned serving stack (TensorRT-LLM or SGLang selection, custom kernels, speculative decoding), Chains for compound pipelines, multi-cloud capacity across 15+ providers, or VPC and HIPAA deployment. If you only need to call a popular open model behind one API and want the lowest price per token, DeepInfra's serverless rates win and the Baseten controls go unused. The break-even is operational, not just per-token: pay the premium when those production controls are load-bearing for you.