Novita AI sells Llama 3.3 70B inference for $0.14 per million input tokens, which is arguably the lowest headline price currently available for this model. While competitors like DeepInfra hover around $0.23 and major platforms like Together AI charge upwards of $0.88, Novita is aggressively capturing the bottom of the market. It is a classic "wrapper" provider, aggregating GPU compute to offer bare-bones, OpenAI-compatible access to open-source models.
For a text-heavy application processing 10 million input tokens and generating 2 million output tokens daily using Llama 3.3, the math is stark. On Together AI, this workload costs roughly $19/day. On Novita AI, the same volume runs about $2.20. That is nearly an 88% reduction in operating costs. The trade-off, naturally, is infrastructure maturity. You aren't paying for the robust enterprise SLAs, SOC2 Type II audit trails (though they claim compliance), or the white-glove support teams you get with AWS or Azure. You are paying for raw token processing, and in that specific lane, Novita delivers exceptional value.
Technically, the platform is competent. The OpenAI-compatible API means migration is usually a one-line config change. They support advanced features like function calling and prompt caching, which is critical for reducing costs further in RAG applications. Latency is respectable—DeepSeek V3 responses are snappy enough for chatbots—but don't expect the supernatural speeds of Groq's LPUs. It's fast, but it's "standard GPU cloud" fast.
The service feels like a discount warehouse club: the lighting is harsh, the amenities are sparse, but the unit price on the bulk goods is unbeatable. If you are building a non-critical internal tool, a side project, or a feature where margin is thin, Novita is a no-brainer. However, the aggressive pricing on "experimental" model versions (e.g., significantly cheaper rates for deepseek-v3.2-exp vs stable versions) suggests a platform that moves fast and breaks things.
Skip this if you are a bank or a healthcare provider needing guaranteed provisioned throughput and 99.999% uptime. Use it if you are a startup burning cash on inference and need to extend your runway without downgrading your model intelligence.
Pricing
The pricing is aggressively low. Llama 3.3 70B starts at $0.14/1M input and $0.40/1M output, undercutting almost everyone. DeepSeek V3 pricing is volatile depending on the specific version; standard versions can cost ~$1.10/1M output, while "experimental" versions drop to $0.41/1M. The free tier gives you ~$0.50 in credits. It sounds negligible, but at these rates, that's nearly 3.5 million input tokens for Llama 3.3—plenty for a thorough evaluation. Watch out for the specific model slugs; picking the wrong version of DeepSeek can triple your output costs.
Technical Verdict
A solid OpenAI drop-in. The API is RESTful and standard, with Python and Node SDKs that mirror OpenAI's structure. Prompt caching and function calling work as advertised, which is rare for budget providers. Latency is competitive (often sub-500ms TTFT), but reliability is the main variable. Documentation is functional but sparse compared to industry leaders.
Quick Start
from openai import OpenAI
# Point to Novita's base URL
client = OpenAI(base_url="https://api.novita.ai/v3/openai", api_key="YOUR_NOVITA_KEY")
response = client.chat.completions.create(
model="deepseek/deepseek-v3",
messages=[{"role": "user", "content": "Explain quantum entanglement in one sentence."}],
)
print(response.choices[0].message.content)Watch Out
- DeepSeek output pricing varies drastically (up to 3x) between 'experimental' and 'stable' model versions.
- Free tier credits ($0.50) are small in dollar value, though high in token volume.
- Rate limits on the lowest tiers can be tight for bursty workloads.
- Uptime guarantees are not comparable to AWS or Azure SLAs.