Khursheed Hassan

Founder @Cloudidr — AI FinOps for LLM teams. Ex-AWS EC2. Building the intelligence layer between your apps and LLM providers.

[Khursheed Hassan @khassan9]

Hundreds and growing number of models from frontier labs like OpenAI, Anthropic, Google, Mistral and others.

Pricing spread: 450x between cheapest and most expensive.

A rogue agent or unaware team routing to the wrong model can cost tens of thousands extra.

AI FinOps fixes this automatically 👇
https://t.co/GUrdy6TvkN

[Khursheed Hassan @khassan9]

https://t.co/HkuKYBuWkK

[Khursheed Hassan @khassan9]

Prototyping AI is easy. Production is a security and billing nightmare.

You need an Enterprise AI Gateway.

Cloudidr delivers AI FinOps, data sovereignty, and intelligent routing to cut LLM spend by up to 90%.

Integrates in 60s. https://t.co/2sRKjX53zT

[Khursheed Hassan @khassan9]

Gemma 4 31B managed inference is live on Cloudidr.

⚡️ 507 peak TPS | 272 avg
💰 $0.50 in / $1.00 out per 1M
📉 Up to 5x cheaper than Haiku 4.5 & GPT-5.4-mini

Auto-route agents from GPT/Claude to Gemma with budget guardrails & zero ops.

https://t.co/2sRKjX53zT

[Khursheed Hassan @khassan9]

OpenAI costs are exploding. Compute-heavy models will wreck your 2026 budget.

The biggest mistake? Defaulting to premium models for basic tasks.

Cloudidr routes LLM requests to the cheapest capable model in real time. Cut AI spend up to 90% with 2 lines of code.

[Khursheed Hassan @khassan9]

Seeing your LLM bill isn't the same as controlling it.

Cloudidr is the developer-lite AI FinOps platform that enforces budget guardrails and cuts LLM costs by up to 90% via intelligent routing.

2 lines of code. 60-second setup.

https://t.co/H8Yxm5fkUf

[Khursheed Hassan @khassan9]

Teams default to expensive LLMs because testing cheaper ones takes an engineering sprint.

Braintrust & Portkey require SDKs and code. Cloudidr makes evals visual and instant, then routes prompts to cut costs by up to 90%.

Stop guessing. Start routing 👇

https://t.co/5fD0F7HCoL

[Khursheed Hassan @khassan9]

read more at https://t.co/uRWqPlPbUQ

[Khursheed Hassan @khassan9]

Bureaucracy is the ultimate test of vitality.

Want to spot a dying institution? Look at the ratio of doers to coordinators.

If you aren't actively cutting the excess, you are implicitly approving its growth.

[Khursheed Hassan @khassan9]

Agent tracing shouldn't be an integration nightmare.

While LangSmith and Braintrust offer deep debugging, they require heavy code instrumentation.

Cloudidr provides instant cost visibility and budget guardrails via a 2-line proxy.

Trace, optimize, and cut costs in 60s.

[Khursheed Hassan @khassan9]

Model evaluation shouldn't be complicated.

LangChain, Braintrust, and Helicone are for debugging. Cloudidr is for your bottom line.

Skip the heavy setup. Cut LLM bills by up to 90% with automated routing and hard budget caps in just 2 lines of code.

https://t.co/2sRKjX53zT

[Khursheed Hassan @khassan9]

Relying on one LLM provider is a structural risk. When they go offline, your app breaks.

True resilience requires an agnostic gateway. Cloudidr acts as your automatic failover—routing prompts around outages instantly so you stay online.

Zero downtime. 2 lines of code.

[Khursheed Hassan @khassan9]

Tracing AI agents shouldn’t mean rewriting your code.

LangSmith & Braintrust force heavy manual instrumentation. Cloudidr traces multi-step agents directly at the proxy layer.

See exact costs, enforce budgets, and cut LLM bills by 90% with 2 lines of code.

[Khursheed Hassan @khassan9]

Agent tracing shouldn't be an integration nightmare.

While LangSmith and Braintrust offer deep debugging, they require heavy code instrumentation.

Cloudidr provides instant cost visibility and budget guardrails via a 2-line proxy.

Trace, optimize, and cut costs in 60s.

[Khursheed Hassan @khassan9]

GPT-5.4 vs Gemma 3 27B (open-source, self-hosted)
I tested both on everyday prompts. The results will make you rethink your LLM stack.

One model beat GPT-5.4… while costing 89% less.

Here’s the breakdown:

Prompt 1: “Draft a polite email declining a meeting”
- GPT-5.4 → Clean but generic (7.0/10)
- Gemma 3 27B → Better: suggested alternative times (7.8/10)

Winner: Open source Cost difference: -89%

Prompt 2: “Explain the key differences between REST and GraphQL”
-GPT-5.4 → Thorough 5-point breakdown (8.0/10)
- Gemma 3 27B → Solid but less complete (7.3/10)

Winner: GPT-5.4 (by just 0.7 points) Cost difference: -95% for open source

Key Lesson:
80% of real-world LLM usage is simple tasks (drafting, summarizing, classifying, responses).

→ A good open-source model can match or beat frontier models on these at 1/10th the cost.

Save the expensive models for deep reasoning, complex analysis, or high-stakes work.

Most teams pick one model early and never revisit it. That single decision can cost (or save) hundreds of thousands.

Moral of the story: Evaluate before you commit.

We built the LLM Evaluation Playground exactly for this — run side-by-side tests with scoring in minutes.

You can try dozens of experiments for free on our platform.

What % of your LLM calls are “simple everyday tasks”?
Drop your answer below 👇

[Khursheed Hassan @khassan9]

Full methodology + datasets 👇https://t.co/pgsgq27l7P

[Khursheed Hassan @khassan9]

We benchmarked intelligent model routing on 4 clinical datasets — summarization + ED triage.

Results: ▪ 77–99% cost savings per task ▪ ~$83K saved per $100K spend ▪ Quality verified on every task

Routing reserves premium models for complex clinical work. Simple prompts go cheaper — automatically.

[Khursheed Hassan @khassan9]

I just published AI FinOps: The New Discipline Every AI-First Company Needs https://t.co/alLzqyfUlp

[Khursheed Hassan @khassan9]

Most teams running AWS Bedrock have zero visibility into what each call cost or which model handled it.
Cloudidr fixes that.

15 providers · 62 models · intelligent routing · budget controls

Now fully supported on Bedrock.

[Khursheed Hassan @khassan9]

Blended average: 60% saved Per $100K spend → keep $60K

The insight: most financial AI prompts don't need your most expensive model.

Intra-provider routing stays within Anthropic/OpenAI/Google — zero code change.

Flexible routing goes further — medium prompts hit our self-hosted Qwen/Gemma fleet at $0.65/1M.

Complex prompts always stay protected on Opus.

37–89% savings. Real prompts. Real costs.
Full breakdown → https://t.co/sFaQVvbigG

[Khursheed Hassan @khassan9]

We ran thousands of financial AI prompts through 3 routing configurations.

Results:
→ FiQA Sentiment: 78% / 89% savings
→ Financial Headlines: 57% / 71% savings
→ FPB Sentiment: 37% / 45% savings
→ ConvFinQA: 58% / 40% savings