GPT-5.6 Luna
$0.44API token cost
Scoped tasks with clear acceptance checks
$0.20 input / $1.20 output per 1M
Official model pricing LLM Inference OptimizationMore intelligence.
More intelligence.
For every dollar.
Reducing cost of AI
Give your engineers more room to build and your customers more room to explore. TensorOps brings models, infrastructure and operations together to make every inference budget go further.
YOUR LLM FACTORY
01
Work worth doingEngineers · Agents · AI products
02
One intelligent gatewayQuality · Routing · Budgets
03
The right inference mixManaged APIs + self-hosted models
Built around your organizationWe build optimized
We build optimized
LLM factories.
A shared inference platform gives every team access to the models they need. We design the gateway, connect managed and self-hosted LLMs, and tune the whole system around quality, latency, data policies and cost.
REFERENCE ARCHITECTURE / AWS
Inside your token factory.
A shared gateway. Dedicated GPU capacity. Managed models when the task calls for them.
Organization VPC Your network & access policies
Apps & agentsService accounts · scoped API credentials
DevelopersOpenCode · Cursor · approved coding clients
Authenticated requests
Identity & ingressHTTPS ALB · OIDC / Cognito for browser sign-in · validated tokens for API clients
Identity, tenant and budget context
AMAZON EKS / GATEWAY & ROUTER
Langfuse & FinOpsTraces · quality · cost attribution
Secrets ManagerProvider keys & rotation
Policies & budgetsGuardrails · DLP · per-tenant quotas
Prompt / response cachePolicy-approved reuse, scoped by tenant
LLM GatewayMulti-AZ replicas · model aliases
KV-aware routing · rate limits · approved failover
Self-hosted inferenceManaged inference
EKS GPU NODE GROUP
Open-weight & fine-tuned modelsEvaluated model / precision combinations
vLLM / SGLang serving podsContinuous batching · paged attention · prefix reuse
GPU KV cacheActive context for concurrent requests
EC2 GPU capacityP5 / P5e examples · H100 / H200 · EFA for multi-node
Karpenter · dedicated node poolsCapacity reservations / Capacity Blocks
Tiered KV reuseLMCache connects compatible serving instances
GPU↔CPU RAM↔NVMe
Reuse matching prefixes within model and tenant boundaries. Tune cache movement against latency and memory budgets.
Model weights & loadingS3 → FSx for Lustre / instance NVMe → serving pods
Managed-provider accessBedrock private endpoint where supported; controlled egress to approved external APIs.
Approved managed destinations · outside your VPC
FRONTIER APISOpenAI & other labsAstra and approved model tiers
AWS MANAGEDAmazon BedrockModels available in your region
ALTERNATIVE NEOCLOUDSToken-as-a-serviceFor example, Nebius Token Factory
Match the model to the workDifferent tasks.
Different tasks.
Different cost profiles.
Efficient models suit well-defined work. Frontier models bring deeper capability to complex tasks. Open-weight models add managed or self-hosted options. We evaluate the mix on your actual workflows, including retries and human review.
ONE EXAMPLE WORKLOAD1M input + 200K billable output tokens50 requests · 20K input + 4K output each
GPT-5.6 Terra
$4.40API token cost
Work that balances capability and cost
$2.00 input / $12.00 output per 1M
Official model pricing GPT-6 Astra
$20.00API token cost
Complex reasoning, coding and investigation
$10.00 input / $50.00 output per 1M
Official model pricing Published USD rates checked September 7, 2026. Standard processing, uncached input; excludes tools, cache writes, platform costs, discounts and taxes. Reasoning tokens count as output. Equal token counts compare prices; task quality and token usage vary by model.
Make quality part of the cost equation. Compare cost per accepted change or resolved customer request. For self-hosting, include GPU utilization, serving operations and capacity headroom in the calculation.
GPU inference optimizationThirty seconds of flexibility.
Thirty seconds of flexibility.
A new cost equation.
Interactive coding benefits from immediate feedback. Independent background tasks can allow a 30-second batching window. Explore prefill, decode and the throughput improvement that would deliver up to 75% lower GPU inference cost in this illustrative scenario.
THE SAME MODEL. A DIFFERENT PACE.
Illustrative serving modelGive the GPU more work per pass.
01 / REQUESTS
Developer+0s
QA+10s
Operations+20s
Support+30s
Independent requests for the same model.
02 / SCHEDULER0sCollect → dispatch
Up to 30s of queueing flexibility.
GPU inferenceSame hardware budget
01PrefillCompute intensive
Read the prompt.
Process its tokens in parallel.
Per-request KV cache
KV
02DecodeOften memory bound
Model weights in GPU memory
Amortize weight reads across 4 sequences
R1
R2
R3
R4
Generate one next token per active sequence, per step.
Gathering requestsAnimation time is compressed
GPU COST FOR THE SAME TOKEN VOLUMENormalized cost units · same GPU hourly cost
75%lower GPU inference cost4.0× assumed total token throughput
1× · same throughput4× · up to 75% lower cost
A little scheduling flexibility can unlock a much busier GPU. Larger batches share model-weight reads across more sequences. TensorOps tunes the scheduler, memory capacity and serving stack to turn that opportunity into measured savings.
The 75% example assumes 4× end-to-end throughput at the same GPU hourly cost: 1 − 1/4 = 75%. A 30s wait alone does not establish that gain. Actual savings depend on traffic, model, context, KV-cache capacity and latency targets. This illustrates scheduling delay, not extra reasoning tokens or a provider’s Batch API price.
More ways to serve intelligenceBuild the right mix.
Build the right mix.
Keep your options growing.
Special pricing for your workload.
We help source and negotiate special pricing with alternative neoclouds, matching GPU capacity or managed inference to your volume, region and service requirements. Compare commercial offers alongside a measured serving benchmark.
Provider terms and capacity determine the offer.
Explore your capacity optionsGive complex work room to think.
GPT-6 Astra brings frontier capability to reasoning, coding, research and tool use. TensorOps helps evaluate where that capability earns its place, then integrates approved OpenAI models into your platform with budgets and measurable outcomes.
Model access and deployment options are confirmed during setup.
Our OpenAI partnershipPut efficient inference to workMore capacity.
More capacity.
More possibilities.
Coding organizations
Give your R&D team affordable tokens and capacity to run more coding, testing and review work in parallel. Combine responsive engineer sessions with background agents, then measure accepted changes and delivery throughput.
Build a team of long-horizon agentsCustomer-facing AI products
Offer your users more conversations, richer workflows and higher usage limits within a sustainable budget. Tune model routing, context and caching against product evaluations to preserve the quality customers expect.
Scale your product’s inferenceFrom setup to continuous improvementA platform that runs.
A platform that runs.
A team that stays with you.
Environment setup
Build the gateway, connect approved providers and deploy your serving stack. Configure identity, network boundaries, model aliases and workload evaluations for a confident launch.
A working inference platform, ready for your teams.AI-Ops support
Keep models, endpoints and agent integrations running smoothly. Manage capacity, latency, incidents and evaluated model upgrades as your usage grows.
Reliable operations and a clear path to new models.FinOps for LLMs
Actively monitor tokens, GPU utilization and cost per successful task. Attribute spend to teams and products, set budgets, investigate anomalies and tune the routing mix.
Continuous visibility. Measurable improvements.Let’s build
your LLM factory.
Bring your workloads, quality targets and growth plans. We’ll map the model mix, infrastructure and operating approach that help you get there.
Plan your inference platform