Nov 1, 20265 minS0
<p>Most on-prem AI security discussions end at the network perimeter — firewalls, SSO, prompt logging, segmented VLANs. But a pair of high-profile research disclosures in late 2024 revealed a class of attack that...
About this Podcast:
Private and custom large language models — the build, the boundaries and the bill. Fine-tuning versus retrieval, running models in your own environment, evaluation you can actually trust, data governance, and the questions to ask before a vendor answers them for you. Each episode takes one decision a team is facing — whether your problem needs a custom model at all, how to evaluate output without fooling yourself, what "private" has to mean contractually — and works it through concretely. Written for engineering and data leaders putting a model into production. Five or six minutes, one idea, no demos. Topics include fine-tuning versus retrieval, self-hosted and private deployment, evaluation you can trust, prompt and context design, data governance and retention, cost and latency tradeoffs, and what "private" has to mean contractually. Produced by LLM.co, private and custom large language models. Full details, services and further reading at https://llm.co
Pitch Analysis
Required Pod Score for this podcast. PitchCentric checks your profile against host openness, topical fit, and audience signals before you generate a pitch.
Contact path
Public web form
Booking probability
36%
Guest openness
Selective
80/100
Required Score
Sign up to generate a grounded pitch for LLM.co.
Signup to Generate a PitchQuestions about LLM.co
What is LLM.co about?
Private and custom large language models, the build, the boundaries and the bill. Fine-tuning versus retrieval, running models in your own environment, evaluation you can actually trust, data governance, and the questions to ask before a vendor answers them for you.
What has LLM.co covered recently?
Recent episodes include "Why Your AI Server's Firmware Is the New Security Frontier" (1 November 2026, 5 minutes), "GPU Server Configs for Open-Weight LLMs: Match the Build to the Model" (25 October 2026, 6 minutes), and "Private LLM vs ChatGPT Enterprise: Cost, Security, and Performance" (21 October 2026, 5 minutes).
How often does LLM.co publish?
LLM.co publishes roughly a few times a week, with episodes averaging 5 minutes.
How hard is it to get booked on LLM.co?
Booking probability for LLM.co is 36%, and the host is rated Selective on guests. LLM.co requires a Pod Score of 80 or higher. LLM.co's contact route on record: public web form.
Who should pitch LLM.co?
The strongest fits for LLM.co are IT Services & Software Development. LLM.co has a business audience.
About the host
LLM.co is a business podcast.
Host email not verified for this podcastSign in to unlock →
Recent episodes
Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.
- Oct 25, 20266 minS0
<p>Choosing the right GPU server for an open-weight LLM isn't as simple as headcount or budget — it comes down to model size, interconnect bandwidth, KV cache math, and concurrency requirements that only become visible...
- Oct 21, 20265 minS0
<p>Choosing between a private LLM deployment and ChatGPT Enterprise is one of the most consequential AI infrastructure decisions a company can make right now — and most teams approach it with the answer already baked...
- Oct 18, 20265 minS0
<p>Ordering a GPU chassis before anyone has mapped the actual workload is one of the most expensive mistakes in enterprise AI infrastructure — and it happens constantly. This episode of LLM.co breaks down the correct...
- Oct 14, 20265 minS0
<p>With healthcare data breaches averaging $7.42 million in 2025 and regulators tightening the screws on electronic PHI, deploying an LLM in a clinical environment is no longer a product decision — it's a compliance...
View More
- Oct 11, 20265 minS0
<p>GPU sizing for on-premises AI rarely starts with the right question. Vendors lead with reference architectures built for worst-case scenarios, and organizations end up over-provisioned before a single workload...
- Oct 7, 20265 minS0
<p>Switching from a public LLM API to a private deployment sounds straightforward — until Monday morning standup reveals broken outputs and a hasty rollback. This episode of LLM.co breaks down why most first attempts...
- Oct 4, 20265 minS0
<p>Most budget conversations about private AI infrastructure start and end with GPU sticker prices — and that's exactly where the planning goes wrong. This episode of LLM.co delivers a rigorous, component-by-component...
- Sep 30, 20265 minS0
<p>Signing a private LLM vendor agreement without the right RFP language is how companies end up locked into data topologies, logging schemas, and termination terms they never agreed to — they simply never required...
- Sep 27, 20265 minS0
<p>With AI pilot failure rates hovering near eighty percent and tens of billions in enterprise spending producing little measurable return, the contract model behind a private LLM deployment deserves far more scrutiny...
- Sep 23, 20265 minS0
<p>The EU AI Act's December 2027 deadline for high-risk AI systems looks distant on a roadmap — but with notified body queues already growing and harmonised standards still being finalised, the organisations that will...
- Sep 20, 20265 minS0
<p>DeepSeek has earned real attention for its speed and accessibility, but its privacy policy contains a detail that enterprise teams cannot afford to overlook. This episode of LLM.co examines the data storage risks...
- Sep 16, 20265 minS0
<p>Global fraud losses are on track to surpass half a trillion dollars, and the rigid, rules-based detection engines that financial institutions have trusted for years simply can't keep up. This episode of LLM.co...
- Sep 15, 20265 minS0
<p>Hallucinations — confident, fluent, and factually wrong AI outputs — are among the most damaging trust failures in production systems. This episode of LLM.co draws on the full guide to debugging hallucinations in...
- Sep 11, 20265 minS0
<p>Technical performance is rarely what kills an AI initiative. This episode of LLM.co examines the organizational and structural breakdowns that derail AI projects long before a model ever has a chance to prove itself...
- Sep 9, 20265 minS0
<p>When an AI project moves from demo to daily infrastructure, the conversation shifts fast — from model benchmarks to balance sheets. This episode of LLM.co tackles one of the most consequential decisions in any open...
Best industries to pitch LLM.co for
PitchCentric classifies LLM.co under these industries. Guests from them are the closest fit, and each links to the best podcasts in that industry.
Similar Podcasts
Shows with the most semantically similar episode content. Pitch one, pitch all; producers cluster.
Podcasts like LLM.co
Is this podcast yours and you'd like to remove or correct details? Request removal or email privacy@pitchcentric.com.
Questions about the free trial? See the pricing FAQ