Lambda Labs vs DeepInfra: open-weight inference (2026)

Side-by-side

Lambda Labs vs DeepInfra — at a glance

Dimension	Lambda Labs	DeepInfra
Business model	GPU cloud + LLM API	Pure LLM inference
Llama 3.3 70B price (per 1M tok)	Competitive, mid-market	~$0.20-0.60 (among the cheapest)
Direct GPU rental	Yes — H100, H200 hourly	No
Catalogue	Popular Llama, Mistral, Qwen	100+ open models, very broad
Latency (typical)	Solid on shared LLM API	Very competitive on hot models
Best for	Combining LLM API + raw GPU rental, training + inference under one bill	Lowest per-token price, batch and high-volume open inference

When to choose which

Pick Lambda Labs or DeepInfra?

When to choose Lambda Labs

Choose Lambda Labs when you want a single vendor for both LLM inference and raw GPU rental — for example, training or fine-tuning on rented H100/H200 then serving the result. Lambda has built reputation in the GPU-cloud space first; the inference API is a clean extension. For teams that need raw compute (Lambda hourly H100s) plus a hosted Llama endpoint, the unified bill is operationally simpler.

GPU cloud + LLM API under one vendor
Direct H100 / H200 hourly rental for self-hosting or training
Competitive shared-tier per-token pricing
Operationally simpler for hybrid train + serve setups
Trusted GPU-cloud reputation from the AI research community

When to choose DeepInfra

Choose DeepInfra when the lowest possible per-token price on open-weight LLMs is the priority. DeepInfra has positioned itself around ultra-cheap inference — Llama 3.3 70B is commonly $0.20-0.60 per 1M tokens, materially cheaper than Together, Fireworks, or Lambda. For high-volume batch workloads (classification, summarisation, RAG over large corpora) this can cut the inference bill by 50% or more.

Among the cheapest per-token rates for open-weight LLMs
Llama 3.3 70B typically $0.20-0.60 per 1M tokens
Broad catalogue of open-weight models (100+)
Strong fit for batch and high-volume workloads
OpenAI-compatible API with no markup model

Why not both?

Run Lambda Labs and DeepInfra side-by-side

VerticalAPI lets you switch between Lambda Labs and DeepInfra per-request through a single OpenAI-compatible endpoint. Use Lambda when you also need raw GPU rental or a single vendor for train + serve; use DeepInfra for the cheapest per-token batch inference. Same SDK, same API key, zero markup — you pay Lambda Labs and DeepInfra directly with your own keys (BYOK).

from openai import OpenAI
client = OpenAI(base_url="https://api.verticalapi.com/v1", api_key="vapi_...")

# Lambda Labs — LLM API + GPU rental in one place
resp_x = client.chat.completions.create(
    model="lambdalabs/llama-3.3-70b",
    messages=[{"role": "user", "content": "Serve our fine-tuned Llama on Lambda + use H100 cluster"}],
    extra_headers={"X-Provider-Key": "lmb-..."},
)

# DeepInfra — ultra-cheap per-token inference
resp_y = client.chat.completions.create(
    model="deepinfra/llama-3.3-70b",
    messages=[{"role": "user", "content": "Classify 5M support tickets at the lowest cost"}],
    extra_headers={"X-Provider-Key": "di-..."},
)

Try VerticalAPI free →

VerticalAPI verdict

Use Lambda Labs when you want a single vendor for LLM inference plus raw GPU rental, or when train + serve under one bill matters. Use DeepInfra when raw per-token cost on high-volume open-weight workloads drives the decision. Through VerticalAPI you can route between both with a single OpenAI-compatible endpoint and BYOK — no SDK migration.

Get started — BYOK both providers →

FAQ

Frequently asked questions

Is DeepInfra cheaper than Lambda Labs for Llama 3.3 70B?

Usually yes. DeepInfra commonly prices Llama 3.3 70B in the $0.20-0.60 per 1M tokens range in 2026, while Lambda Labs sits in the more typical $0.60-1 mid-market band. For batch and high-volume workloads (millions of tokens per day), DeepInfra can cut the inference bill by 50% or more. The trade-off is fewer adjacent offerings like raw GPU rental.

Can I rent raw GPUs from either?

Lambda Labs is a GPU cloud first — direct H100 / H200 hourly rental is the core product. DeepInfra is inference-only; there is no raw GPU rental. For teams that need to train or fine-tune on rented GPUs and then serve via API, Lambda is the natural unified-bill choice. DeepInfra is purely an inference provider.

Which has a broader catalogue?

DeepInfra. DeepInfra hosts 100+ open-weight models across Llama, Mistral, Qwen, DeepSeek, and more. Lambda's LLM API catalogue is narrower and focused on the most popular Llama, Mistral, and Qwen variants. For niche open-weight models, DeepInfra is more likely to host them at low cost.

Which is faster?

Both providers are competitive on shared multi-tenant inference for hot models like Llama 3.3 70B. DeepInfra has invested heavily in inference optimisation to support ultra-cheap pricing without latency penalties; Lambda's shared LLM tier is solid but not specifically tuned for the lowest possible TTFT. For latency-critical workloads, benchmark both on your traffic.

Can I call both Lambda Labs and DeepInfra through one endpoint?

Yes. VerticalAPI exposes a single OpenAI-compatible endpoint at https://api.verticalapi.com/v1. You send the same request shape and change the model parameter (for example, lambdalabs/llama-3.3-70b or deepinfra/llama-3.3-70b) and the matching X-Provider-Key header. There is no markup on tokens; you pay Lambda Labs and DeepInfra directly using your own keys (BYOK).

Caveats

Limitations of this comparison

Per-token pricing for open inference changes frequently — DeepInfra's $0.20-0.60 figure is a mid-2026 range, not a guarantee.
Lambda Labs' GPU-cloud and LLM-API businesses have different SLAs and quota structures that can be confusing.
Ultra-low DeepInfra prices may come with stricter rate limits and noisy-neighbour effects at peak times.
Catalogue coverage shifts as new models drop; benchmark availability of specific Llama / Qwen variants before committing.
This page covers two of several open-inference providers. Together AI, Fireworks, Replicate, and Lepton all overlap.

Outlook

What may change in 12-24 months

DeepInfra is likely to keep pushing per-token prices down — $0.15 per 1M tokens for Llama 3.3 70B is plausible by late 2026.
Lambda Labs is expected to expand the LLM API tier and integrate it more tightly with their GPU-cloud reservations.
Catalogue parity between providers will narrow as new open-weight releases ship simultaneously on all major hosts.
Provider lock-in will weaken further as OpenAI-compatible gateways (including VerticalAPI) make swapping open-inference providers a one-line change.

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