NEWVectors or files. Pick a path.Start →

    Mixpeek for DevOps Engineers

    Deploy and operate a multimodal AI platform without managing GPU clusters

    DevOps engineers tasked with running multimodal AI workloads face GPU scheduling, model artifact management, and inference scaling challenges that do not map cleanly to traditional container orchestration. Mixpeek provides a managed platform with clear deployment, monitoring, and scaling boundaries.

    Get Started as a DevOps EngineerRead the Docs

    What's Broken Today

    1GPU scheduling and resource contention

    Kubernetes GPU scheduling is complex. Shared GPU workloads compete for VRAM, and autoscaling decisions are slower than CPU-based services.

    2Model artifact management

    Tracking which model version is deployed, managing multi-gigabyte model files, and coordinating model updates across workers requires tooling beyond standard CI/CD.

    3Heterogeneous service dependencies

    A multimodal pipeline depends on vector databases, object storage, task queues, ML inference servers, and monitoring systems that all need to be configured, connected, and health-checked.

    4Cost attribution across workloads

    Understanding whether GPU spend is going to video processing, embedding generation, or real-time inference is difficult without purpose-built metering.

    5Incident response for ML services

    Standard runbooks do not cover debugging embedding quality degradation, stalled Ray jobs, or vector index corruption. ML-specific failure modes require specialized playbooks.

    How Mixpeek Helps

    Managed Ray infrastructure

    Mixpeek runs Ray clusters for inference and batch processing. Scaling, health checks, and worker replacement are handled by the platform, not your ops team.

    Single deployment target

    Instead of deploying and configuring six services, you deploy to Mixpeek's API and engine. The platform manages the Mixpeek Vector Store, Redis, and Ray internally.

    Built-in observability

    Batch processing status, retriever execution latency, and stalled job detection are built into the platform. Integrate with your existing monitoring via API.

    Stalled job recovery

    The stalled job monitor detects orphaned Ray jobs and Celery tasks, performs early exit for terminal states, and alerts without manual intervention.

    How It Works for DevOps Engineers

    1

    Deploy the API and Celery services

    The API and Celery workers deploy automatically to GKE once a commit passes the staging end-to-end gate. Configure environment variables for MongoDB, the Mixpeek Vector Store, and Redis connections.

    2

    Deploy the engine to Anyscale

    Run the deploy script to build a Docker image, push to the artifact registry, and deploy the Ray Serve engine. The script handles blue-green rollout and health verification.

    3

    Configure monitoring and alerts

    Set up health check polling for the API and engine endpoints. Monitor batch processing throughput and stalled job counts through the status API.

    4

    Establish runbooks for ML-specific failures

    Document procedures for common failure modes: stalled batches, vector index issues, Ray worker OOMs, and embedding quality degradation. Mixpeek's API provides the diagnostics needed.

    Relevant Features

    • Managed Ray cluster
    • Batch monitoring
    • Health check endpoints
    • Stalled job monitor
    • Deployment scripts

    Integrations

    • Anyscale
    • Render
    • Docker
    • GitHub Actions
    • Datadog
    "Managing GPU clusters for ML inference was consuming 40% of our ops team's time. Moving to Mixpeek's managed engine let us redeploy that effort to application-level reliability work."

    Alex Rivera

    Platform Engineer, Infra.sh

    Frequently Asked Questions

    How does Mixpeek handle zero-downtime deployments for the engine?
    The Anyscale deploy script uses blue-green deployment. A new version is brought up alongside the existing one, health checks are verified, and traffic is switched atomically. If the new version fails health checks, the rollout is aborted and the existing version continues serving.
    What monitoring endpoints does Mixpeek expose?
    The API exposes /health for load balancer health checks, bypassing middleware to prevent timeouts. Batch status endpoints provide processing metrics. The engine exposes Ray Serve metrics for throughput and latency monitoring.
    Can Mixpeek run in our private cloud or VPC?
    Mixpeek supports deployment to your own infrastructure. The API and Celery services run as standard containers. The Ray engine can be deployed to any Ray cluster, including Anyscale private deployments or self-managed Ray on Kubernetes.
    How does the stalled job monitor work?
    The stalled job monitor runs as a periodic Celery beat task. It checks for Ray jobs and Celery tasks that have been in a running state beyond a configured threshold. Orphaned jobs are terminated, terminal-state jobs are cleaned up, and alerts are emitted through the configured notification channel.

    Related Resources

    Get Started as a DevOps Engineer

    See how Mixpeek can help devops engineers build multimodal AI capabilities without the infrastructure overhead.

    Schedule a DemoRead the Docs