AI Supply Chain Resilience
Your AI stack will change.
You won't choose when.
Some changes are forced — a price triples, your access is cut, and you scramble.
Others are your call — a cheaper model, or a skill document you changed.
Forced or chosen, the one that bites is the switch you never measured.
We keep your AI supply chain measured and ready to swap.
You control what, and when.
The drop-in replacement myth
Selecting, serving, and scaling a model is hard. Proving it works is harder.
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Hundreds of models, each with its own precision, serving, and routing choices. The combinations outrun any team's ability to try them all, so the real risk is picking by guesswork.
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Models improve fastest where correctness is cheap to test at scale, like coding and math. Subjective judgment, slow-to-verify work, and work that shifts over time all come with slower feedback. What worked for coding might not work well for your tasks.
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Public benchmarks measure a broad swathe of tasks, not yours. They can be, and often are, tuned to look frontier-grade. The hard task is where frontier models keep their lead. You can win the average and lose the tail.
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One task runs many iterations, calls tools, and adds latency, burning the tokens of a thousand chat conversations. A cheaper per-token model that makes more mistakes and needs more turns will cost more per task, not less.
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Say a company-wide rollout. A managed endpoint hits its rate limits. Or your own cloud has no on-demand compute, so you over-provision upfront and pay for capacity you rarely use.
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Closed labs co-design the model with its harness and tools, so the internals and behavior differ even on the same prompts. A real swap also has to be one you're allowed to run, and sit on the same cloud as the tools and data you depend on.
Traditional software costs almost nothing to serve one more user.
AI is the opposite: every token is manufactured by someone else, using physical inputs you don't own.
You carry supply risk.
Resilience means staying in control as models, prices, and load shift.
Resilience suite
Make your AI supply chain hard to break.
Three links. One coupled chain. We qualify a swap, then serve and scale it as one.
Swappable by design
Measured, not assumed.
A model switch can degrade quality you never measured, and a cheaper-per-token model can raise your total cost. It is not just the model. Update your harness or skills, and quality can drift. We treat swappability as a graded, tested property of your whole system.
A swap is a switch you measured.
- Proactive shortlisting. We scan the model landscape and identify viable substitutes, cutting hundreds to the few that fit your job, your stack, and the rules. We score those and surface any that come back cheaper or better. The same applies to the harness, tools, and agents the model depends on.
- Evidence-based selection. We make the choice measured on your own usage, with a 12-metric schema and a design-of-experiments method. We grade each swap against the model you run today, in cost, quality, and reliability.
- Continuous re-qualification. We shadow-test a candidate against live production, so a swap is proven before it is served. We keep measuring after a swap, not just before it, so drift shows up as evidence, not a production surprise.
- Three axes, all checked. Functional fit is only the first axis. The non-functional axis is wider than it looks: jurisdiction, data handling, safety, and the model license. The attachment surface the model is bundled with also needs qualification.
An eval scores a model once, in isolation. We qualify a swap on your own tasks, and keep proving it as things change.
Agentic by design
Purpose-built agentic serving, not chat endpoints.
How your agents are served is a choice, not a default. Sometimes the right mode is a frontier API, but not its fastest tier. Sometimes it's an open model behind a managed endpoint. Sometimes a long-running workflow on an open model needs the control that self-hosting gives. Whatever the mode, you stay free to move as cost, quality, or supply shift.
- Workflow-matched serving. Evals, agentic RAG, and coding agents are different workflows, and each needs different serving. We tune the stack to the workflow you run and the model you chose, so a chat endpoint is never the default. The stack is defined as code, so it re-fits automatically on a swap, and a model change stays a model change.
- Endurance across long runs. A stateless chat endpoint forgets your context the moment an agent pauses on a tool, then rebuilds it cold on the next call. We hold that state, so long tasks run to completion instead of breaking partway.
- Bounded cost per task. KV caching reuses context already computed, so a task's cost stays predictable as it grows, not just lower. We cache aggressively where it pays and skip it where it doesn't.
- Effort under your control. A chat endpoint thinks the same on every request. Because you own the serving layer, you set how hard the model works per task, so a hard one gets more reasoning and an easy one does not.
Others sell one serving stack for every workflow. We match the mode to the work, and right-size what you run.
Elastic by design
Capacity that follows load, not reserved and idling.
Every AI product has a shape to its demand. It bursts, it idles, and the shape differs by workflow. A cloud's default autoscaling sees only compute, but inference load moves on several axes at once. ScaleFit reads your load shape and provisions to it, so you hold neither idle reservations nor a queue under burst.
- Scale the load shape, not the request count. A burst is not just more requests. It is more compute, more held state, and more concurrent tool calls, and we scale the three together. A single-axis autoscaler overshoots cost and starves capacity at once.
- On-demand by load factor. On-demand instances are the default serving mode where the load factor supports it. We size that choice to your actual duty cycle, not to a worst-case reservation you pay for around the clock.
- CPU lanes for the work that fits. Not every job needs a GPU. Latency-tolerant work runs on cheaper CPU capacity, so you stop paying GPU rates for jobs that can wait.
- Hyperscaler internals, used directly. Right-sizing burst needs the substrate's real primitives, like AWS EFA for fast interconnect. We provision against what the cloud actually exposes, not a generic abstraction over it.
- The optimization frontier keeps moving. New techniques arrive constantly, speculative decoding, continuous batching, and more. If you self-host, you stay free to adopt the ones that fit your load.
Others reserve for the peak and pay for the idle. We scale compute, memory, and concurrency to your load shape.
Benefits
AI supply resilience is not free. We make it affordable.
One discipline, two payoffs: a stack ready for change, and a smaller token bill.
cheaper model selection1
cheaper than a managed endpoint2
cheaper than reserved3
Modeled targets, not guarantees. The assumptions are in the notes below.
1 Run your evals on cached infrastructure, not full-price every turn. Cost to run the model-selection evaluation, not production inference. Directional estimate for an input-heavy agentic eval (~1M input / ~6k output tokens per task at current Sonnet-class rates): about $3.09 per task uncached, ~$0.84 with prefix and KV caching of the stable test harness, where repeated context bills at roughly a tenth of fresh input. Assumes ~80% of input is cacheable; the real fraction is set by your harness, so your number depends on its cache-hit rate. Caching reuses identical computation, so the certified score is unchanged.
2 Self-host your serving plane instead of renting it per token. Self-hosted serving cost versus a per-token managed endpoint, for the same eval workload. Based on GPT-OSS-120B over a 50-task run: ~$4 self-hosted on four L40S with aggressive caching, against ~$8 to $22 on a managed endpoint. The advantage holds at sustained utilization, since a self-hosted instance is billed whole whether busy or idle, and narrows if the endpoint offers comparable caching. Hardware throughput is estimated, not measured; a short calibration on your target model firms it.
3 Pay for evals only when they run, not for a whole idle month. On-demand spend at your actual duty cycle versus a full month of reserved capacity, not a per-hour rate comparison; reserved is cheaper per hour, and the saving comes entirely from not paying for idle time. Modeled on five eval runs per weekday at about 0.7 hr each, roughly 11% of a monthly duty cycle. Against a reserved discount of 40 to 60 percent, on-demand lands about 4 to 5 times cheaper. The multiplier scales with run length, so confirm your real run duration.
Who we are
Senior hands on a hard problem.
DeepStore is built by two practitioners with nearly 30 years each. A. Lakshminarayanan (Lux) works across cybersecurity and AI product, with R&D years at A*STAR Singapore, and co-founded the venture-backed cybersecurity-hardware startup iTwin, which was acquired in 2016. Shyam Maniyedath spent five years at Infosys in India before nearly twenty-five years building software platforms in the Bay Area, at Yahoo, Salesforce, Uber, and AWS. Both are engineers from IIT Madras, with graduate work at the Indian Institute of Science, Bangalore (Lux) and Cornell, New York (Shyam).
Measuring whether a swap really holds up, serving agents instead of chat, and scaling to real load are three hard, separate problems. This year we have been building the measurement and serving methods behind SwapFit, ServeFit, and ScaleFit. The hardest part is making them work as one. That coupling is what keeps a swap working after it ships. Our home markets are Singapore, the US, India, and the EU. We work closely with the teams building on the frontier, on the problems they actually hit.
Every manufacturer works from a bill of materials: each component, and the approved substitutes that keep production running when a part is discontinued or runs short. DeepStore keeps that record for AI — the models you run, and the qualified, measured replacements ready to swap in when one is repriced or cut off. That's the store in DeepStore. Not a shop you buy from, but the proven model materials you keep, so you stay in control when supply changes.
Models change.
Vendors change.
Your control shouldn't.
You can't choose when the next change lands. You can choose to be ready for it.