DeepInfra is a lean, engineering-driven inference platform that has positioned itself as the market's price floor leader. Founded in September 2022 by ex-imo.im engineers with deep distributed systems expertise, the company operates 100+ models with approximately 15 employees on $28M in total funding.
The company's strategic significance lies in its pricing: at $0.03 per million input tokens for Llama 3.1 8B, DeepInfra sets the benchmark that every other inference provider is measured against. Its early access to NVIDIA Blackwell GPUs has enabled a further 4x cost reduction through NVFP4 quantization, pushing the cost floor even lower.1
DeepInfra's extreme cost efficiency creates persistent downward price pressure across the inference market. While the company targets developers and SMBs rather than enterprise customers, its pricing anchors buyer expectations and accelerates the commoditization narrative for inference services.
DeepInfra was founded in Palo Alto, California in September 2022 by three engineers who previously worked together at imo.im, a messaging app serving hundreds of millions of users. Their shared experience building low-latency, high-volume distributed systems directly informed their approach to inference infrastructure.2
| Founder | Role | Background |
|---|---|---|
| Nikola Borisov | CEO | Northwestern University, competitive programming, ex-HalloApp and imo.im |
| Georgios Papoutsis | Co-founder | Physics and engineering background, ex-imo.im |
| Yessenzhar Kanapin | Co-founder | Distributed systems expert, Kazakh-British Technical University, ex-imo.im |
The founders observed massive investment flowing into AI model training while affordable, production-grade inference infrastructure remained scarce. They set out to make open-source model deployment trivially easy via API, emerging from stealth in November 2023 with an $8M seed round.3
| Round | Date | Amount | Lead Investors |
|---|---|---|---|
| Seed | November 2023 | $8M | A.Capital, Felicis Ventures |
| Series A | November 2024 | $18–20.6M | Felicis Ventures, Georges Harik |
| Total | $26–28.6M |
Revenue is estimated at approximately $3.8M annually (third-party estimates from Craft.co/ZoomInfo). DeepInfra has not publicly disclosed revenue figures. The pay-per-token model with no long-term contracts generates purely consumption-based revenue.4
Running 100+ models with ~15 employees and achieving 8,000x volume growth on $28M total funding is exceptional capital efficiency. Revenue per employee (~$250K) is modest, but the infrastructure-heavy nature of the business means margins improve dramatically with scale.
Post-money valuation has not been publicly disclosed. Based on typical Series A multiples for infrastructure companies raising ~$20M, the implied range is $80M–$150M (speculative). Some sources reference $74.6M in total funding, which may include non-dilutive funding or GPU credits.5
OpenAI-compatible API endpoints for 100+ models. Pay-per-token pricing for language models, pay-per-second for image/audio. Auto-scaling with zero idle cost. Zero data retention policy (no logging of prompts). SOC 2 and ISO 27001 certified.6
On-demand dedicated GPU containers (A100, H100, H200, B200) with full SSH access. Deploy custom models from Hugging Face with autoscaling options.
Deploy any Hugging Face model as an API. Configure GPU class, batch size, number of GPUs. Multi-region deployment for reduced latency.
DeepInfra received early Blackwell GPU shipments and is featured alongside Baseten, Fireworks AI, and Together AI in NVIDIA's official Blackwell inference content. NVFP4 quantization on Blackwell enables up to 4x cost reduction on MoE models compared to Hopper generation.7
DeepInfra consistently prices at or near the market floor across all model families. The pricing model is purely consumption-based with no upfront fees and no contracts required.
| Model | Input ($/M tokens) | Output ($/M tokens) |
|---|---|---|
| Llama 3.1 8B Instruct | $0.03 | $0.05 |
| Llama 3.1 70B Instruct | $0.23 | $0.40 |
| Llama 3.1 405B Instruct | $1.79 | $1.79 |
| DeepSeek-V3 | $0.32 | $0.89 |
| DeepSeek-R1-Distill-Llama-70B | $0.05 | $0.08 |
NVIDIA's Blackwell GPUs have enabled a progressive cost reduction on MoE models:
| Generation | Cost per M Tokens (MoE) | Reduction |
|---|---|---|
| Hopper (H100/H200) | $0.20/M | Baseline |
| Blackwell | $0.10/M | 2x |
| Blackwell + NVFP4 | $0.05/M | 4x |
DeepInfra's $0.03/M input price for Llama 3.1 8B undercuts most competitors by 30–60%. At these levels, margins are razor-thin, suggesting either massive GPU utilization efficiency or a willingness to operate at low/negative margins for market share. This creates persistent downward price pressure for the entire inference market.8
| Customer | Use Case | Impact |
|---|---|---|
| Latitude (AI Dungeon) | AI game narratives | 1.5M MAU, 4x cost reduction on MoE models via Blackwell |
| Partner | Integration Type |
|---|---|
| OpenRouter | Backend inference provider |
| LiteLLM | Supported provider |
| LlamaIndex | Official integration |
| Vapi | Voice AI model provider |
| MindStudio | Inference backend |
DeepInfra primarily targets developers and SMBs through its API-first model. The company does not publicly list enterprise customers, and its developer-focused strategy means most revenue comes from high-volume consumption rather than large enterprise contracts.9
| Certification | Status |
|---|---|
| SOC 2 | Certified |
| ISO 27001 | Certified |
| Data Retention | Zero retention (no prompt logging) |
| Differentiator | Detail |
|---|---|
| Lowest price positioning | Consistently at or near the price floor for all models |
| Speed to new models | Among first to host new models when released |
| OpenAI API compatibility | Drop-in replacement with minimal code changes |
| Zero data retention | No prompt logging; SOC 2 + ISO 27001 |
| Capital efficiency | ~15 people, 100+ models, 8,000x volume growth on $28M |
| Full-stack ownership | Hardware through application layer |
| vs. Competitor | DeepInfra Advantage | Competitor Advantage |
|---|---|---|
| Fireworks AI | Lower pricing | Better latency, structured output, agent support |
| Together AI | Lower pricing | Broader open-source ecosystem, fine-tuning |
| Groq | More models, dedicated GPUs | Custom LPU silicon, faster raw inference |
| Cerebras | More models, broader GPU support | Custom wafer-scale chips |
Industry analysts increasingly recommend treating inference providers as commodities, using routing solutions (LiteLLM, OpenRouter) to dynamically switch between them. DeepInfra is the recommended default for cost-sensitive, high-volume background tasks. This trend directly undermines differentiation claims from all inference providers.10
| Dimension | DeepInfra Position | Differentiation Opportunity |
|---|---|---|
| Deployment | US-based, NVIDIA GPUs only | Sovereign-ready, multi-chip (SambaNova, Etched) for regulated industries |
| Latency SLA | No advertised latency guarantees | Sub-120 us/token target is a concrete, measurable differentiator |
| Energy cost | Market-rate GPU leasing | Colocation with energy assets creates structural cost advantage |
| Customer segment | Developer/SMB focused, no enterprise sales | Enterprise design partners with dedicated capacity |
| Hardware flexibility | NVIDIA-only | Multi-chip strategy provides optionality |
If DeepInfra's pricing continues to drop (their Blackwell migration already demonstrated 4x reduction), they could pull the floor price so low that even energy cost advantages cannot deliver a meaningful differential to enterprise buyers. The defense is to compete on reliability, latency SLAs, data sovereignty, and multi-chip flexibility, not just price.