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Kubernetes & vLLM

Kubernetes & vLLM Serving

FISTA Solutions builds production inference serving on Kubernetes: vLLM or equivalent runtimes configured for your traffic, GPU scheduling and autoscaling that respects cold-start reality, continuous batching tuned for throughput, observability, and model rollouts that do not drop requests.

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150+
projects delivered
50+
companies served
99.9%
verified uptime
47%
efficiency gains
12+
countries reached

What we build

What does Kubernetes LLM serving include?

Kubernetes serving work covers cluster and GPU node configuration, serving runtime tuning, autoscaling and capacity strategy, gateway and routing, observability including GPU utilization, and rollout procedures for model and configuration changes.

  1. 01

    Cluster and GPU configuration

    Node pools, device plugins, scheduling, and resource requests tuned for inference rather than training.

    Cluster
  2. 02

    Serving runtime tuning

    Batching, concurrency, memory, and context settings tuned to your prompt and output distribution.

    Serving
  3. 03

    Capacity and autoscaling

    Scaling strategy that accounts for GPU cold starts and cost, with warm capacity where latency requires it.

    Scale
  4. 04

    Gateway and routing

    Request routing, queueing, and back-pressure so overload degrades gracefully rather than timing out.

    Routing
  5. 05

    Rollouts and observability

    Zero-downtime model rollouts plus utilization, latency, and error dashboards with alerting.

    Operations

Requirements

Which requirements shape Kubernetes LLM serving?

GPU serving on Kubernetes is dominated by cold starts, memory limits, and tail latency. Requirements cover capacity strategy over reactive autoscaling, batching tuned to real traffic, graceful overload behavior, and rollouts that never drop in-flight requests.

Kubernetes & vLLM: requirements and how FISTA Solutions builds to them
RequirementWhy it mattersHow FISTA implements it
Cold start realityGPU nodes take minutes to become ready.Warm capacity floors and predictive scaling rather than purely reactive autoscaling on request metrics.
Batching tuningDefault settings waste GPU capacity.Continuous batching, concurrency, and memory settings tuned against your real prompt and output lengths.
Tail latencyAverages hide the experience users get.Latency measured at high percentiles with queueing designed so long requests do not block short ones.
Graceful overloadOverload should degrade, not collapse.Queue limits, back-pressure, and shed-load behavior so callers get clear errors rather than timeouts.
Safe rolloutsModel swaps can drop requests.Rolling updates with readiness gating and connection draining so in-flight requests complete.

Where AI fits

How should you sequence Kubernetes LLM serving?

Build serving capacity before applications depend on it: configure and tune the runtime, establish capacity strategy from real traffic, add gateway and observability, then run rollouts as routine rather than as events.

  1. 01

    1. Tune the runtime

    Batching and memory settings tuned to your traffic before scaling decisions are made.

  2. 02

    2. Establish capacity strategy

    Warm floors and scaling behavior set from measured traffic, accounting for GPU cold starts.

  3. 03

    3. Add the gateway

    Routing, queueing, and back-pressure so overload is graceful and clients see clear errors.

  4. 04

    4. Instrument deeply

    GPU utilization, batch sizes, queue depth, and tail latency, not just request counts.

  5. 05

    5. Make rollouts routine

    Zero-downtime model updates practiced regularly rather than performed nervously.

Cost and timeline

What does Kubernetes LLM serving cost, and how long does it take?

Cost is driven by GPU utilization more than by request volume; timeline by cluster access and capacity availability. FISTA does not quote blind: the scoping call returns a serving architecture and a utilization-based cost model.

Utilization is the whole economic story in GPU serving. Idle GPUs cost the same as busy ones, so batching configuration and capacity strategy determine cost per request far more than raw traffic does.

Warm capacity is a deliberate expense bought to avoid cold-start latency. FISTA sizes that floor against your latency requirements so the premium is a decision rather than a default.

Send the scope you have, even if it is a paragraph. You get a written brief, an architecture sketch, and a phased estimate before any commitment.

Get a scoped quote

Delivery

How does FISTA deliver an AI deployment?

FISTA deploys AI in four phases: an assessment that inventories workloads, data boundaries, and constraints and produces a target architecture; a platform build with networking, identity, secrets, and observability as code; a migration with evaluation gates and shadow traffic; and a production cutover with dashboards, budgets, runbooks, and rollback.

  1. 1

    Assess and target

    Workload inventory, data classification, latency and volume profile, compliance constraints, and a target architecture with cost model.

    Output

    Target architecture, cost model

  2. 2

    Build the platform

    Networking, identity, key management, model endpoints, gateway, tracing, and evaluation pipeline delivered as infrastructure-as-code.

    Output

    Platform as code, control matrix

  3. 3

    Migrate with gates

    Move applications behind the gateway, run evaluation and shadow traffic, and tune routing, caching, and capacity.

    Output

    Eval reports, shadow results

  4. 4

    Cut over and operate

    Graduated production rollout, dashboards for quality, latency, and cost, runbooks, on-call, and a change process with rollback.

    Output

    Production platform with SLOs

Why FISTA

Why choose FISTA Solutions for Kubernetes LLM serving?

FISTA tunes inference serving against your real traffic, plans capacity around GPU cold-start reality, and makes model rollouts routine. Work is contracted through a US entity with full IP assignment.

Kubernetes & vLLM specifics

  • Batching and memory settings are tuned to your actual prompt and output length distribution, not left at defaults.
  • Capacity strategy accounts for GPU cold starts with warm floors, rather than relying on reactive autoscaling alone.
  • Overload degrades gracefully through queue limits and back-pressure rather than collapsing into timeouts.
  • Model rollouts use readiness gating and connection draining so in-flight requests always complete.

How FISTA engineers

  • Spec-Driven Development: every deliverable starts as a written specification with acceptance criteria, so scope is testable before it is built.
  • AI-native delivery: engineers direct coding agents under review gates and evaluation harnesses, compressing build time without loosening verification.
  • Official Anthropic partner, with production experience across Claude, OpenAI, Google, and open-weight models, chosen per workload rather than by default.
  • One accountable delivery lead, weekly demos on your environment, and code in your repositories from week one.

What you get as a client

  • 150+ projects delivered for 50+ companies across 12+ countries since 2017, with 99.9% verified uptime on systems we operate.
  • A US entity (FISTA Solutions Inc., Wilmington, Delaware) for contracting, invoicing, and IP assignment, with an engineering center in Faisalabad, Pakistan for cost-efficient senior capacity.
  • US business-hours overlap for standups and reviews; written decision logs so nothing depends on a meeting you missed.
  • Flexible engagement: fixed-scope build, embedded forward deployed engineers, or a dedicated team that you can scale month to month.

Clear answers

What platform teams ask before deploying AI.

Straightforward guidance for evaluating scope, fit, and the next step.

01Why is our GPU serving so expensive?

Usually low utilization: idle GPUs cost the same as busy ones. Batching configuration, capacity strategy, and routing typically improve cost per request far more than adding hardware does.

02Can we autoscale GPU inference?

Partially. GPU nodes take minutes to become ready, so purely reactive autoscaling produces latency spikes. FISTA uses warm capacity floors plus predictive scaling against traffic patterns.

03How do we update models without downtime?

Rolling updates with readiness gating and connection draining, so in-flight requests complete on the old version while new traffic moves to the new one.

04What should we monitor?

GPU utilization, batch sizes, queue depth, and tail latency alongside request counts. Request-rate dashboards alone hide the metrics that explain both cost and user experience.

05How long does it take to build?

A production serving stack typically takes weeks, with cluster access and GPU capacity availability as the usual gating items.

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Related capabilities

Open-source LLM deploymentModel selection and evaluation for self-hosted serving.AI cost optimizationReducing inference spend across self-hosted and managed models.LLMOps platform developmentEvaluation, versioning, and release process around serving.

Scoped in writing before you commit

Serve more requests per GPU, and update without downtime.

Bring your traffic profile and cluster. The scoping call returns a serving architecture and a utilization-based cost model.

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