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Cerebrium Inc Unclaimed

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cerebrium.ai

New York City, NY, United States

Cerebrium is a remote-first AI infrastructure company that enables developers to build and deploy real-time AI workloads at global scale.

Cerebrium is a remote-first AI infrastructure company building the global platform that enables teams to design, deploy, and scale real-time AI workloads. We believe AI will fundamentally change how businesses operate, and we provide the tooling engineers need to build, deploy, and scale AI-powered applications without fighting the underlying plumbing. The team is remote-first, globally distributed, and engineer-led, focused on enabling the next generation of AI-driven products. From real-time voice bots to multimodal inference pipelines and large-scale batch processing, Cerebrium makes it radically easier for teams to deploy, scale, and operate AI workloads without managing a single server. We reimagine infrastructure to abstract away the mess of cold starts, autoscaling, orchestration, observability, and regional deployment—so engineers can focus on building. Whether you’re running LLMs across regions with data residency in mind or fine-tuning models at scale, Cerebrium is optimized for performance, reliability, and speed. The company is committed to helping organizations bring AI-powered solutions to customers quickly, securely, and at scale.

Enabling companies to build AI products people love

What we offer

Cerebrium Platform

Accelerate development of AI applications with seamless deployment, scaling, and performance optimization.

cerebrium.ai/

Market segments

Market size by segment

Growth potential (CAGR)

Serverless AI infrastructure

12.5 Billion USD25% CAGR

Managed, serverless execution environment that abstracts provisioning, autoscaling, orchestration, observability, and cold starts so engineering teams can deploy AI workloads without managing servers.

Real-time inference infrastructure

18 Billion USD30% CAGR

Infrastructure and runtimes that deliver ultra-low latency, fast cold starts, and cost-efficient inference for interactive AI experiences.

Multi-region deployment and data residency

10.9 Billion USD28% CAGR

Cross-region deployment and orchestration to meet data residency, regional latency, and compliance requirements for running LLMs and AI workloads across geographies.

GPU-accelerated cloud compute

8.21 Billion USD26.5% CAGR

Platforms that provide on-demand GPU instances, preconfigured environments, and scalable cloud infrastructure to run training, fine-tuning, and inference workloads.

Developer-first AI operations

2.5 Billion USD33% CAGR

Capabilities that streamline developer workflows—rapid cloud code execution with no provisioning or CI/CD, production testing with real secrets, one-off tasks, and simplified deployment iteration.

More information about our offering

Cerebrium Platform

Cerebrium Platform is a remote‑first, serverless AI infrastructure that enables teams to design, deploy, and scale real‑time AI workloads without managing underlying servers. It abstracts away the mess of cold starts, autoscaling, orchestration, observability, and regional deployment, allowing engineers to focus on building. The platform is optimized for performance, reliability, and speed, and supports cross‑region deployment with data residency considerations to power AI workloads at scale. Cerebrium Run enhances this by enabling rapid cloud code execution without provisioning delays, supporting GPU acceleration, and offering production testing capabilities.

  • Deploy Globally With Low Latency
    Customers can deploy workloads closer to end users, ensuring low latency and compliance with data residency regulations.
  • Focus On Building, Not Infrastructure
    Reduces operational complexity, allowing teams to allocate resources toward innovation rather than infrastructure management.
  • Achieve High Performance
    Ensures fast deployment and scalability to support demanding AI tasks efficiently.
  • Eliminate Server Management
    Removes the need for server management, enabling teams to focus on developing and scaling AI solutions.
  • Execute Code Instantly
    Achieve cloud code execution in seconds, greatly enhancing development speed.
  • Handle Diverse Workloads
    Supports a wide array of AI applications, making it versatile for different use cases.
  • Leverage GPU Power
    Utilize GPU capabilities for efficient processing of demanding tasks.
  • Deploy Voice Solutions Quickly
    Supports speech-to-text and text-to-speech applications with minimal latency, enhancing user experience.
  • Optimize Cost Efficiency
    Ensures organizations are charged only for the compute they use, eliminating hidden costs associated with idle resources.
  • Skip CI/CD Overhead
    Focus directly on code execution without the intermediate stop of CI/CD setups.
  • Access Essential Resources
    Utilize secret management, storage, and logs for comprehensive task execution.
  • Speed Up Development Cycle
    Enables faster feedback loops and reduces deployment times significantly, enhancing overall productivity.
  • Run Production Tests Instantly
    Test new features in live environments immediately to enhance reliability.
  • Secure Execution Environment
    Maintain the security of your code and data in an isolated cloud environment.

References

Methodology and sourcing behind the figures shown above.

Serverless AI infrastructure

Estimation based on intersecting published AI infrastructure and serverless computing market figures in the search results. AI infrastructure reports put the overall market in the low-hundreds of billions (USD 135–394B reference points for 2024–2030) while serverless computing reports show a 2024 market in the mid-teens to mid-twenties of billions (USD 17.2B–25.5B) with high growth rates (≈14–25%+). I estimated Serverless AI infrastructure as the subset of AI infrastructure delivered via serverless/cloud models: assuming a material cloud share of AI infrastructure and that a modest fraction (roughly mid-single to low-double-digit percent) of cloud AI consumption runs on serverless-style platforms yields an estimated current market around USD 12.5B. Growth potential (CAGR ≈25%) uses recent serverless market CAGRs (~25%) and faster AI-infrastructure growth as a reference, implying serverless AI could expand at a serverless-plus-AI pace in the mid-20% range.

Real-time inference infrastructure

No explicit market figures were present in the supplied search results. Estimated market size reflects the portion of global AI infrastructure spending attributable to real-time inference (cloud inference services, inference-optimized hardware, edge runtimes, and inference runtimes/serving software). I derived a mid-range 2024 market size (~$15–25B) and selected $18B as a conservative central estimate based on known data‑center GPU and AI cloud service spend trends and the rapid adoption of LLM-driven interactive applications. Growth potential (≈30% CAGR) reflects observed rapid investment in inference capacity, expansion of real-time/interactive AI use cases, and strong vendor guidance and chip/cloud spend trajectories (typical industry estimates for inference/AI infra growth fall in the mid‑20s to mid‑30s percent range).

Multi-region deployment and data residency

Primary sources show the broader data-residency / data-sovereignty tools market is large and fast-growing (Mordor: ~USD 72.4B in 2025, 25.8% CAGR; SNSInsider: USD 27.35B in 2025, 18.4% CAGR). Multi-region deployment and orchestration for LLMs/AI is a specialized subset of that market (controls, residency, regional orchestration, sovereign-cloud integrations). I estimated the multi-region/AI-focused subsegment at roughly 15% of the 2025 data-residency market (0.15 * USD 72.37B ≈ USD 10.9B) because AI/LLM workloads generate disproportionate demand for regionalization, GPU/edge orchestration, and sovereign-cloud features. Growth potential (CAGR) is set above the overall market rate (estimated 28.0%) to reflect accelerated adoption driven by AI-model localization, hyperscaler sovereign-cloud investments, and increasing regulatory pressure cited in the reports.

GPU-accelerated cloud compute

Estimate based on published GPU-as-a-Service / GPU cloud market reports in the search results. MarketsandMarkets reports a GPU-as-a-Service market value of USD 8.21B in 2025 with a 26.5% CAGR to 2030; Mordor Intelligence and GMI Insights provide similar current-size estimates (~USD 6–7.4B in 2023–2026) and higher CAGRs (28–30%). Persistence Market Research shows a broader data-center GPU market (USD 22.7B in 2026, 32.1% CAGR) that confirms strong upside for GPU-accelerated cloud compute. I therefore use MarketsandMarkets' 2025 GPUaaS figure (USD 8.21B) as the primary market-size estimate and its 26.5% CAGR as the growth-potential estimate, noting other sources indicate a 27–32% CAGR range.

Developer-first AI operations

Estimate derived by situating ‘developer-first AI operations’ between narrow generative-AI-in-SDLC tools (Precedence: USD 0.64B in 2025) and broader AI platform / development-to-operations markets (MarketsandMarkets: USD 18.22B in 2025; LinkedIn dev-to-ops: ~USD 18.5B in 2026). Developer-first AI ops addresses developer tooling, rapid cloud code execution, and platform engineering—a meaningful subset of AI platforms and Dev-to-Ops. Using 2025 sector figures as bounds and assuming the subsegment represents roughly 10–15% of AI platform/development-operations spending in early adoption, I estimate a current market size ≈ USD 2.5B and a high-growth CAGR (driven by generative AI adoption and platformization) of ~33%.

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