Managed Inference Services
Multi-provider routing, dedicated capacity, serverless and batch inference — operated for your engineering team. Most cut inference spend 30-50% in the first 90 days, without managing GPUs, vendor contracts, or 3am pages.
What We Run
We operate the routing, dedicated, serverless, and batch inference your engineering team would otherwise build, maintain, and be on-call for.
Route every inference call by cost, latency, region, or model availability across Anthropic, OpenAI, Google, AWS Bedrock, Together, Fireworks, and self-hosted endpoints. Automatic failover. One API for your team.
Reserved capacity for predictable workloads. Scale-to-zero serverless for spiky ones. Async batch for offline scoring, evals, and bulk generation at up to 70% lower cost. We pick the right shape per workload.
Token spend by feature, customer, and model. Latency and quality monitoring across providers. Drift detection. The visibility your CFO and your platform team both want, in one layer.
Model Coverage
Frontier models from Anthropic, OpenAI, and Google. Open-weight models like Llama, DeepSeek, Qwen, and Mistral — hosted or self-deployed. Your fine-tuned models, wherever you keep the weights. We treat them all as endpoints behind one routing layer.
How We Work
Three discrete engagements. Each builds on the last. No long-term commitment to move forward.
We map your current inference architecture, benchmark across models and providers, and deliver a written report: projected savings, target architecture, and a 90-day implementation plan. Fixed price.
We deploy the new stack — routing, dedicated, serverless, batch — with zero-downtime cutover patterns. Your team is brought along so the new layer is understood, not just inherited.
We operate the layer. Monitor cost, latency, model performance. Re-optimize as providers ship new models and pricing changes. On-call coverage for inference incidents. Monthly review with your team.
Providers ship new models every quarter; pricing changes monthly; older models deprecate without warning. Keeping up is a full-time job no one on your team signed up for.
Single-provider is single-point-of-failure. Real production stacks span 3-5 providers across model types, regions, and price tiers. Someone has to own that complexity.
Batching, caching, model selection, and dedicated capacity each shave 10-30%. Layered together they routinely cut spend in half. Most teams know this; few have time to do it.
Every hour your platform team spends tuning inference is an hour not spent on the application that uses it. The math gets worse, not better, at scale.
New · Premium Engagement
Every inference cost recommendation eventually hits the same question: will quality drop? We bolt a real eval harness onto the snapshot so the savings number you take to your board is one you can defend.
If measured quality on your golden set drops more than 5% from baseline at the end of 90 days, we extend monitoring and iterate at no additional cost until it doesn't.
We only recommend changes the evals can measure — and the standard cost levers (model right-sizing, prompt caching, batch migration, multi-provider routing) clear that floor in nearly every engagement.
· 30 days of API logs or request samples (≥1,000 examples)
· One subject-matter expert for ~4 hours to validate golden examples
· Engineering access to ship one approved change inside week 4
· A single Slack channel for alerts and async updates
If you can't commit to all four, the guarantee can't hold and we won't sell it. We'll point you to the standard $7,500 Audit instead.
$24,000 flat · 13 weeks · 50% kickoff, 50% week 4
Engagement fee credits 100% toward your first month of managed services.
Pricing
Audit fees credit toward your first month of managed services. Tiers re-evaluated annually based on your actual inference spend.
Audit
flat · 2-week engagement
For teams evaluating their inference architecture. Best entry point.
Managed — Growth
for $25-100k/mo inference spend
Most startups and scaleups land here. Real cost wins, simple operations.
Managed — Scale
for $100-500k/mo inference spend
For teams with material spend and tighter SLAs. Weekly cadence.
Enterprise
for $500k+/mo inference spend
For organizations with hard compliance, sovereignty, or scale requirements.
All managed tiers are month-to-month with 30-day notice. Annual contracts available with 15% discount after first quarter.
What To Expect
30-50%
Typical Inference Cost Reduction
12+
Model Families Supported
< 1s
Routing Decision Latency
Multi-region
Failover By Default
Who We Are
We started AI Sprint because the layer between your application and the model providers is the layer nobody wants to be on-call for. Multiple vendors. Models deprecating mid-quarter. Token bills that surprise the CFO. Routing logic that started as a Python script and never quite stopped being one.
That layer needs to exist. It rarely needs to live inside your company. We operate it for production AI teams across the spectrum — Series A startups running their first model in production, scaleups burning into a procurement review, mid-market organizations getting serious about cost discipline.
We're not a tool or a platform. We're the team that runs the tools, picks the platforms, and owns the outcome.
Get Started
Two weeks. $7,500 flat. You leave with a written audit showing exactly what to change — and credit toward your first month if you move forward with managed services.