Pre-Launch · LLM Inference Optimization

Cut AI inference cost.
Attribute and prove every token.

Production gateway live · talking to design partners · pre-revenue. InferLayer is the AI inference gateway that attributes every token to a business outcome. Semantic cache, multi-provider model routing across OpenAI, Anthropic, and Amazon Bedrock, and a tamper-evident cryptographic audit chain for independently verifiable cost attribution. Substrate-agnostic — runs in your own cloud, no GPU required on the default tier, and architected to scale to multi-node, Kubernetes-orchestrated inference.

Estimate your savings →
Works with OpenAI Anthropic Amazon Bedrock Self-hosted open-source

Not a deck. A running system.

100 users
Concurrent, load-tested · zero failures
~50%
Measured cost reduction · faster, not slower · no GPU required
100%
Audit chain integrity · tamper-evident · independently verifiable

Four production capabilities.
OpenAI-compatible drop-in.

InferLayer sits between your application and your inference provider as a substrate-agnostic gateway. Your requests pass through cache, classification, governance, and cryptographically-verifiable logging before reaching the model — whether that's OpenAI, Anthropic, Amazon Bedrock, or a self-hosted open-source backend. Integration is a one-line code change.

01
Semantic Cache
A meaning-aware cache layer that recognizes when a new request is equivalent to one already answered — even when worded differently — and serves the prior response instantly. Tuned per deployment to your workload's similarity profile. Zero tokens consumed on repeat queries.
Eliminates repeated cost
02
Intelligent Model Routing
Not every task needs a flagship model. InferLayer classifies each request and routes it to the right tier — lightweight models for classification, extraction, and short Q&A; flagship reasoning models for complex tasks. Routes across OpenAI, Anthropic, Amazon Bedrock, and a tunable open-source backend. Per-tier model selection is configurable per tenant.
Right model for each task
03
Tamper-Evident Audit Chain
Every request is signed into a cryptographic hash chain. Modifying or removing any row breaks every subsequent row — the chain detects it. Clients or their auditors can independently recompute integrity without trusting InferLayer. This is what makes savings-share pricing cryptographically enforceable and what satisfies finance and procurement on cost-of-record verifiability.
Cryptographically verifiable
04
Per-Workflow Cost Attribution
Tag each request with a workflow identifier and InferLayer surfaces cost-per-resolution per workflow — the unit economic CFOs actually buy on. Per-tenant isolation, agent-level spend governance, and comprehensive latency observability feed a single dashboard that maps inference spend to business outcomes.
Maps tokens to outcomes
Request flow
01
Your App
Sends request to InferLayer endpoint
02
Authenticate
API key, tenant scope, rate limit
03
Cache Check
Return instantly if matched
04
Classify & Route
Pick the right model per request
05
Inference
OpenAI · Anthropic · Bedrock · open-source
06
Audit & Attribute
Hash-chained log, workflow tag, cost dashboard

Built for teams
spending on tokens.

I
Enterprise AI Teams
Spending five figures monthly on OpenAI, Anthropic, or Bedrock. Your CFO wants to know what each workflow costs. InferLayer maps tokens to workflows to outcomes, with a tamper-evident audit chain that satisfies procurement and finance.
II
AI Ops & FinOps
Drowning in usage exports across multiple providers with no unified view. InferLayer's per-tenant dashboard unifies cost, latency, and cache observability across all your inference traffic — replacing manual spreadsheet reconciliation.
III
SaaS Companies with AI Features
Building user-facing AI products and watching margins erode as usage grows. InferLayer's semantic cache and intelligent routing cut cost ~50% (measured) without changing your application code — drop-in OpenAI-compatible.
IV
Regulated Industries
Healthcare, financial services, legal — anywhere LLM use requires verifiable audit trails. Deploy InferLayer in your own VPC. Pair the cryptographic audit chain with per-tenant key isolation for verifiable right-to-be-forgotten.

Why we're
building this.

Architecture
Layer 3
InferLayer Gateway — OpenAI-compatible API surface, semantic cache, intelligent model routing, per-tenant isolation and spend governance, tamper-evident audit chain, workflow attribution.
Layer 2
Substrate-agnostic backend — provider-neutral by design. Runs in your own cloud with no GPU required on the default tier; Amazon Bedrock for AWS-native deployments; architected to scale to multi-node, Kubernetes-orchestrated deployment.
Models
Flagship: OpenAI and Anthropic frontier classes. Cloud-managed: Amazon Bedrock model family. Self-hosted: tunable open-source backend selected per deployment.

Token costs are blocking AI adoption — but the harder problem is that no one knows where the money went. Modern enterprises run AI workflows across multiple providers, with no way to attribute spend to a workflow, to a business unit, or to a measurable outcome. The CFO sees a bill. The engineering team sees usage logs. Nobody sees both at once, mapped to what the AI actually produced.

InferLayer is the allocation and attribution layer between your applications and your inference providers. We attribute every token to a workflow, route each request to the right model, cache repeat queries, and sign every audit log row into a cryptographic chain — so the cost numbers are independently verifiable without you having to trust us.

We have a production gateway live in the cloud, a verified audit chain operating in production, and benchmarks showing ~50% measured cost reduction — and lower latency — against an all-GPT-4o baseline on the default CPU/API tier, plus 100% on warm cache. We're talking to our first design partners — teams whose real workloads shape what we harden next. Your inquiry directly shapes the roadmap.

Not hiring yet.
But stay in touch.

We're heads-down with design partners and a small team. No open roles right now. If you're an inference engineer, AI infrastructure researcher, or systems developer who cares about LLM cost attribution and production inference — send us your work. We'll save it for when we're ready.

Questions teams ask
before integrating.

How does InferLayer reduce LLM inference cost? +
Three mechanisms work together. Semantic caching returns cached responses for queries matching prior ones above a tuned similarity threshold — zero tokens for repeat queries. Intelligent model routing classifies each request and sends simple tasks (classification, extraction, short Q&A) to lightweight tiers, reserving flagship reasoning models for complex tasks. Per-workflow attribution surfaces which workflows account for the most spend so engineering can optimize the highest-cost paths first. Benchmarked at ~50% cost reduction — measured live against an all-GPT-4o baseline on the default CPU/API tier, no GPU — and 100% on warm-cache hits. The ~50% blends model-routing and cache hits; your real repeat rate is measured during a diagnostic.
Is InferLayer OpenAI API compatible? +
Yes. InferLayer exposes the OpenAI Chat Completions API surface. Integration is a one-line code change in your application: replace your OpenAI base_url with https://api.inferlayer.ai/v1 and use an il- API key. Streaming, function calling, and tool use work without modification. Same response shape, same SDKs.
How is InferLayer different from LiteLLM, Portkey, or Helicone? +
LiteLLM and Portkey are routing proxies — they direct traffic between providers. Helicone is an observability layer — it shows you what happened. InferLayer is the only inference gateway with a tamper-evident cryptographic audit chain designed for independently verifiable cost attribution and savings-share contract enforcement. Combined with per-workflow attribution and BYOC deployment, this targets a different layer: allocation and attribution, not just routing or visibility. They route requests. We attribute outcomes.
Can InferLayer be deployed in our own AWS, Azure, or GCP environment? +
Yes. InferLayer ships as a Docker Compose deployment that runs in your VPC. Your inference traffic and audit logs never leave your network. For AWS deployments, InferLayer integrates with Amazon Bedrock so all inference can be in-VPC. It is architected to scale to Kubernetes-orchestrated, multi-node deployment (with llm-d compatibility for clusters that adopt it). BYOC onboarding typically takes 5-14 days depending on your environment and security review process.
What models does InferLayer support? +
Frontier models via OpenAI and Anthropic. Cloud-managed via Amazon Bedrock model family. Self-hosted open-source backend, tunable per deployment. Provider-agnostic by design — switch backends via configuration without code changes. Bring your own fine-tuned model and InferLayer routes to it like any other.
What is the tamper-evident audit chain and why does it matter? +
Every inference request is signed into a cryptographic hash chain. Modifying or removing any row breaks every subsequent row — the chain detects it. Clients or their auditors can independently recompute integrity to verify cost numbers without trusting InferLayer. This makes savings-share pricing contractually enforceable, satisfies compliance requirements for verifiable audit trails, and gives finance teams the integrity guarantee they need to bring AI cost into the same reporting framework as cloud and SaaS spend.
How long does it take to integrate InferLayer? +
Hosted integration: typically 2 to 4 hours. Change the base_url in your OpenAI/Anthropic client to https://api.inferlayer.ai/v1 and use an InferLayer API key. Verify the first request appears in your dashboard. Done. BYOC deployment: typically 5 to 14 days depending on your cloud environment and security review process. Includes Docker Compose setup, audit log verification, dashboard configuration, and per-workflow attribution tagging.

Interested?
Say hello.

We're talking to design partners — teams running production LLM workloads who care about cost attribution, not just optimization. If that's you, we'd love to see your setup and run a diagnostic on a sample of your logs.

We'll reply personally. No automated sequences.

01
Shape the roadmap. The gateway is live; we're hardening it around real workloads. The pain points you describe directly influence what we prioritize next.
02
First access. People who reach out now get priority when we launch — ahead of general availability.
03
Pricing input. We haven't set pricing yet. Early conversations directly inform what model makes sense for different usage patterns.
04
Direct line. You're talking to the person building this — not a sales rep.