MLOps Pricing for Production Ready AI
Whether you’re deploying your first production model or building infrastructure for a growing AI application, our MLOps plans provide the infrastructure and engineering support you need. Compare our Kickstarter and Growth plans to find the right level of deployment, orchestration, observability, and security.
What does your AI team need right now?
Choose the level of MLOps support that matches your AI workloads, engineering capacity, and production needs.
We need to move our AI
prototype into production.
You have a working AI or ML application but need production-ready infrastructure, deployment pipelines, and reliable operations.
Our AI workloads are growing
and getting harder to manage.
Your team needs scalable infrastructure, automated deployment, better observability, and optimized inference performance as usage grows.
Our production AI needs
dedicated reliability coverage.
You need ongoing model monitoring, infrastructure optimization, cost control, incident response, and deeper operational ownership.
✦ MLOPS SERVICES PRICING
Production-Ready MLOps For AI Applications
Choose the MLOps setup that matches where your AI product is today. From deploying your first production model to supporting custom GPU infrastructure, advanced retrieval, AI agents, and production-scale workloads.
Kickstarter
For AI startups moving a working model, RAG application, or notebook prototype into production.
- Optimized deployment for 1 AI model
- AWS Bedrock, SageMaker, or Vertex AI
- Setup and optimization of 1 vector database
- LangChain or LlamaIndex integration
- Basic token usage and latency tracing
- Secure API gateway and PII masking
Growth
For AI products that need custom GPU infrastructure, advanced workflows, and greater production scale.
- Custom GPU orchestration using EKS or GKE
- Multi-tenant vector database and automated indexing
- Re-ranking and advanced retrieval workflows
- Agentic workflows and multi-model routing
- Advanced AI monitoring and evaluation pipelines
- Cost-per-user tracking and prompt versioning
- Audit logs and private/VPC-only deployments
MLOps built around your AI product goals
Move from working prototypes to reliable AI products with infrastructure designed around production readiness, scalability, security, and operational control.
Move from Prototype to Production
Turn your working model, notebook, or RAG application into a production-ready environment without having to build the entire MLOps foundation internally.
- Establish a reliable production foundation
- Reduce time spent building infrastructure
- Deploy AI workloads with production controls
- Focus engineering time on the product
Support growing inference workloads
Design your AI infrastructure to handle increasing users, inference workloads, model complexity, and production traffic as your product grows.
- Support growing inference workloads
- Scale infrastructure around demand
- Prepare for higher model complexity
- Avoid costly infrastructure rework
Control Operations & Costs
Build an infrastructure foundation that supports growth without allowing cloud complexity and operational costs to grow unchecked.
- Track token and inference usage
- Identify performance bottlenecks
- Improve visibility into AI operating costs
- Monitor production AI workloads
The infrastructure foundation your AI product needs
We take care of the infrastructure complexity behind your AI workloads so your team can stay focused on models, products, customers, and growth.
What Happens After You Sign Up?
We work with your team to understand your AI workload, design the right infrastructure, get your models into production, and build the operational foundation needed to run them reliably.
Assess
We understand your models, AI application, data flows, cloud environment, deployment requirements, and current infrastructure.
Architect
We define the right MLOps architecture across model serving, vector databases, orchestration, security, observability, and cloud infrastructure.
Build
We implement the infrastructure, deployment workflows, model serving, integrations, security controls, and automation your AI product needs.
Validate
We test deployments, monitor model behaviour, validate security and reliability, and optimize performance and infrastructure costs before production rollout.
Operate
Your team gets a production-ready MLOps foundation with the monitoring, documentation, and operational practices needed to keep your AI workloads running.
We stay involved beyond the initial setup, helping your team maintain reliable AI infrastructure as models, workloads, and production requirements evolve.
Frequently Asked Questions
Answers to common questions about deploying, scaling, monitoring, and managing AI and machine learning workloads in production.
What does an MLOps engagement include?
Our MLOps services cover the infrastructure and operational layer required to move AI and machine learning workloads into production. Depending on your requirements, this can include model deployment, CI/CD pipelines, containerization, cloud infrastructure, model monitoring, autoscaling, inference infrastructure, observability, and ongoing optimization.
Can you help move an AI prototype into production?
Yes. We work with teams that already have a working model, RAG application, or AI agent and need a reliable production environment. We assess the existing architecture, package the workload, set up the required cloud infrastructure and deployment pipelines, and establish monitoring and operational controls for production use.
How do you handle growing inference workloads?
We design inference infrastructure around your workload, traffic patterns, latency requirements, and model characteristics. This can include containerized deployments, autoscaling, load balancing, GPU or CPU optimization, model serving infrastructure, and monitoring so capacity can scale as demand changes.
How do you help control the cost of running AI workloads?
We optimize the infrastructure behind your models and inference workloads by identifying over-provisioned resources, inefficient compute usage, unnecessary idle capacity, and opportunities for autoscaling. We also evaluate compute choices and deployment architecture to help keep production AI costs aligned with actual usage.
How do you monitor models after they are deployed?
We set up production monitoring across infrastructure, application, and model performance. Depending on the workload, this can include latency, throughput, resource utilization, error rates, model performance, inference health, and other operational metrics needed to identify issues before they affect users.
Can you set up CI/CD for machine learning and AI applications?
Yes. We can build automated pipelines for testing, packaging, validating, and deploying AI applications and supporting infrastructure. This creates a repeatable path from code or model changes to production while reducing manual deployment steps and improving release consistency.
Will we retain control of our models, code, and cloud infrastructure?
Yes. Your models, source code, cloud accounts, infrastructure, and intellectual property remain yours. We use standard technologies and infrastructure-as-code practices within your environment, with documentation and configurations that your team can continue to manage after the engagement.
Do we need to commit to a long-term MLOps engagement?
No. You can start with a focused MLOps project, such as productionizing an existing AI application, setting up inference infrastructure, or building a deployment pipeline. If you need ongoing infrastructure, monitoring, optimization, or operational support, you can then move to a managed engagement.
How do you ensure our sensitive data and customer prompts remain secure?
We architect your AI infrastructure with strict zero-data-retention and network isolation boundaries. Whether you are using proprietary API endpoints (like OpenAI or Anthropic) or hosting open-weight models (like Llama or Mistral) on private GPU clusters, all data stays strictly within your cloud perimeter. We never expose your prompts, vector embeddings, or fine-tuning datasets to external training loops.
How do you optimize GPU costs and latency for self-hosted models?
We evaluate your target throughput and latency budgets to match the right compute layer. We implement inference engines like vLLM, TensorRT-LLM, or Ollama, leverage model quantization (AWQ/FP8), and set up autoscaling GPU instances (via AWS EKS, GCP Cloud Run, or specialized platforms like RunPod and Modal) so you only pay for active GPU compute hours.
How do you monitor for hallucination, prompt drift, and LLM output quality?
We set up automated evaluation (Evals) and LLM observability pipelines alongside standard system metrics (latency, token-per-second, memory). This tracks prompt/completion costs, token usage per user, retrieval accuracy in RAG pipelines, latency bottlenecks, and guardrails for safety and hallucination before bad outputs reach users.
Do you handle Vector Database setup and optimization for RAG applications?
Yes. We configure, secure, and optimize production vector databases—such as pgvector (PostgreSQL), Qdrant, Pinecone, or Milvus—including index configuration, embedding generation pipelines, automated backups, and cache layers (like Redis) to minimize costly embedding re-computation.
How fast can you take our Jupyter Notebook or Python prototype into a live API?
Most prototype-to-production transformations take 10 to 14 business days. We refactor experimental code into containerized microservices, set up FastAPI/Ray Serve inference endpoints, automate CI/CD pipelines, and expose production-ready REST/gRPC endpoints wrapped in rate-limiting and authentication.
Not Sure Where to Start?
Book a call with our cloud and DevOps team.
We’ll discuss your current setup, challenges, and goals, and help you determine the right next step.
