HL-CORE-2026-001
PRIMORDIAL EDITION
Your company already uses AI.
The real question: is it accelerating results — or accelerating chaos?
Most teams end up hostage to a single LLM provider — and now to the governance platforms that just got bought by big vendors, too. We build the infrastructure layer that separates your operation from all of them: no lock-in, real cost control, and governance with a roadmap of its own.
AI that senses before the gesture — and that you can actually measure.
AI without process doesn't accelerate the business. It accelerates the chaos.
We build the infrastructure layer that separates your operation from the LLM providers — gateway, multitenant, and governance. Switch models, optimize cost, and never get held hostage.
The field opened up.
Portkey
Portkey was acquired by Palo Alto Networks in May 2026. Folded into Prisma AIRS. Its roadmap is now enterprise security.
Helicone
Helicone was acquired by Mintlify in March 2026. In maintenance mode — no new features. Security patches only.
Horse Labs
Our own roadmap. Self-hosted. No big vendor deciding what ships and what doesn't. We're the alternative for teams that don't want governance-platform lock-in.
It's not a lack of technology.
It's a lack of an exit.
Claude Code, Codex, Copilot. Any company can sign up in five minutes. The trouble shows up later: your team adapts to the tool's workflow, your processes get built around it — and before you notice, your operation belongs to the provider.
AI without governance doesn't scale the business — it scales the risk.
The provider deprecates the cheap model. You move to the expensive one with no say. No warning. No negotiation.
Your people burn the company's tokens on tasks that have nothing to do with the business. You find out on the invoice.
If the provider goes down, your operation goes down with it. No fallback. No continuity.
Who used it. For what. How much it cost. For which project. Nobody can answer.
Claude Opus running a FAQ chatbot. You're paying 20× more than you need to.
AI is volatile infrastructure — not a business domain.
Our job is to keep that volatility away from your operation. We build the layer that separates the LLM providers from your day-to-day — switch models, optimize cost, and govern usage without rewriting a thing.
AI as InfrastructureFor those who are going to build on AI seriously.
The technical base that decouples your operation from the providers — provisioned in your own house, under your own domain, ready to scale.
- Gateway that abstracts the LLM providers
- Provisioning with Ansible + Terraform
- On-premise or in your own VPC — AWS · Azure · GCP
- Per-tenant isolation and native observability
AI as a ServiceFor those who just want to buy a finished result.
You don't need to understand the technology. We deliver the result up and running — report, automation, or agent — with the metric agreed on from day one.
- A delivered result, not a tool
- Metric defined before we start
- AI cost measured per operation
- Continuous operation tracked by data
Governance-first, not observability-first.
LLM observability tools focus on analyzing what already happened. Horse Labs focuses on controlling what's allowed to happen: budgets per team, permissions per operation, a hard stop before the overrun.
Structure is what separates results from improvisation.
The gateway abstracts away whoever sits behind it. Claude, GPT, Gemini, or a local model — switch without touching the operation.
Every process carries a visible cost, with a budget ceiling per client. No surprise at the end of the month.
One client's data never mixes with another's. Isolation from the very first line.
The same base serves one client or a hundred. Growing is configuration, not reconstruction.
Those who get it, see it and trust it. Those who don't, move on without losing the thread. The diagram above is the real structure — the same one behind every deliverable below.
The LLM Gateway is the product. Everything runs through it.
Opus for the dev. Haiku for the chatbot. Gemini for the documents.
You set which model handles each activity — based on what each does best — and the gateway routes it. Cost stops being premium by default.
Provider down? Another model takes over.
No single point of failure. If one provider goes dark, the gateway reroutes to another and the operation keeps running.
Budget ceiling per tenant, an audit trail per person.
Every cost center has a limit. Every bit of usage has an owner. The intern's side project no longer lands on the company's bill.
Cost Centers per team or client.
Each team, squad, or client runs with its own budget, allowed model, and independent rate limit. A budget overrun blocks automatically. Admin sees everything; dev sees only what's theirs.
The same agents serve three clients. Budgets, models, and cost centers kept separate — according to each one's contract. All controlled by multitenant.
Your entire operation fits in a WhatsApp conversation.
No dashboard nobody opens. You ask in the channel you already use and get, in real time, any report on the health of your own company.
Total · $312.40
Client A · $214.10
Project B · $51.90
Team C · $46.40
Efficiency · 94% on-target
2 flagged uses · off-project
Action · block suggested
Financial report
Revenue, cost, and margin of the operation in real time.
Project status
Where each deliverable stands, with no alignment meeting.
Cost per operation
How much each AI process consumed — by client and project.
Token usage
Volume by person, team, and cost center.
Token efficiency
How much usage turned into results — and how much was waste.
Drift observability
Who used AI for what — and what fell out of scope.
Infrastructure is also where AI stays secure.
Treating AI as infrastructure opens up a layer no provider delivers: credentials that never touch the model, access restricted by team and role, and security policies enforced in real time.
Vault + RBAC
Credentials never exposed in the agents. Each team accesses only what it needs — and the reporting agent bridges the two, with explicit permission.
Permission per report
Support can't pull the financial report. Leadership doesn't have to ask — it arrives on its own. Granular control over every piece of data the AI delivers.
SecOps at the gateway
A credential typed into the prompt is blocked before it reaches the model — and the manager gets the alert on WhatsApp instantly. The window to react exists because the infrastructure creates it.
The problem first. The solution after.
Branded reports, right in WhatsApp
Your data becomes a finished report — in your own visual identity — delivered where the client already is. No lost PDF, no dashboard nobody opens.
Automation of operational processes
Repetitive tasks that drain the team move off manual and start running on their own — with every step logged and a human checkpoint where it matters.
Conversational agents for support and sales
Support that qualifies, answers, and sells — 24/7, in your company's voice. Not a decision-tree chatbot: an agent that understands context and acts.
AI infrastructure implementation
We build the full base: isolated, monitored, and ready to scale. You get big-company AI capability — running under your own domain.
If you recognize yourself here, Horse Labs was built for you.
Engineering teams
With 5 to 50 devs actively using AI. You approved the AI budget, but you don't know who spends what. Horse Labs gives visibility and control by team, by project, by operation.
Technical leaders
CTOs and VPs of Engineering who have to answer to finance: how much did we spend on AI this month, and why. Without relying on a spreadsheet or OpenAI's raw billing.
Companies with AI in production
That are past the experiment phase and need real governance: rate limits, audit trail, automatic blocking, self-hosted deploy. No big-vendor platform lock-in.
How the problem becomes a solution, in practice.
Illustrative scenarios — the mechanics of each situation, not real cases. When there's a client result, it goes here with a number and context.
Same agents, three clients
Support without paying premium
Build without getting held hostage
From diagnosis to continuous operation.
Diagnosis
We map the current operation: where time leaks, what can be measured, and where AI creates real impact.
Proposal
Scope and metrics defined before we start. You know what will be delivered and how it will be measured.
Implementation
We build and integrate on top of the isolated, monitored, ready-to-scale infrastructure.
Continuous operation
We don't vanish after delivery. We operate on data, adjust, and keep the result alive.
What people ask before getting started.
What is Horse Labs' LLM Gateway?
It's the infrastructure layer between your operation and the LLM providers. It routes each task to the best model — Claude, GPT, Gemini, or a local model — applies a budget ceiling per client, and records the cost of every operation.
How does Horse Labs avoid AI provider lock-in?
Your operation talks to the gateway, not directly to the provider. Switching models becomes configuration, not migration — if one provider raises its price or goes down, another takes over without rewriting anything.
Does the infrastructure run on my own company?
Yes. We provision on-premise or in your own VPC — AWS, Azure, or GCP — with Ansible and Terraform, isolated per tenant and under your own domain.
How does per-client cost control work?
The model is multitenant: each client or cost center has its own budget, model, and usage trail. You track cost per operation — including over WhatsApp.
Do I need to understand the technology to hire you?
No. In the AI as a Service model we deliver the finished result — report, automation, or agent — with the metric agreed before we start. You hire the result, not the tool.
What's the difference between Horse Labs and Portkey or Helicone?
Portkey was acquired by Palo Alto Networks in May 2026 and folded into Prisma AIRS — an enterprise security product, with a roadmap outside the original team's control. Helicone was acquired by Mintlify and has been in maintenance mode since March 2026. Horse Labs is independent, with its own roadmap and a focus on organizational governance — not just observability. On top of that: unlimited logging with no charge per record volume, and self-hosted as a first-class citizen, not an enterprise afterthought.
Is Horse Labs a platform or a consultancy?
Both, in stages: we deliver the infrastructure implementation (a service) and operate the platform that governs it continuously (a product). The client buys the implementation once and uses the platform indefinitely. For those who already have a technical team: you can implement it in-house using our docs and use just the governance layer as SaaS.
The operational layer of intelligence.
Why the company that wins won't be the one that owns the best model — but the one that can switch models without stopping the operation. Three stages of maturity, from no governance to governed infrastructure.
- The eight symptoms of an exposed AI operation.
- Why centralizing on a single vendor is false governance.
- The target architecture: the LLM Gateway as the single operational layer.
No long presentation.
A 30-minute conversation.
Enough to tell whether it makes sense — and if it doesn't, you walk away clear on what you actually need.
Chat on WhatsApp · +55 11 92452-1813