nowfound

AI · October 22, 2026

Seclai

Production LLM infrastructure: model portability, output validation, eval, observability, RAG, memory, governance, and cost management. 200+ models. Try it free.

Seclai launched on October 22, 2026 with 11 votes, #743 of 4,246 launches that month and more than 90% of that year's launches.

Visit seclai.com ↗Seclai alternativestop 17% of October 2026

What it does

Your LLM app works in development. Then production hits: silent failures, prompt injection, cost spikes, models get deprecated. Seclai is everything around the LLM call. Every agent run is traced step by step. Governance policies are written in plain English and screen every input and output. Evals run identically locally, in CI, and in production. When something breaks, replay the trace to find the cause. 200+ models on one account, switchable per step. SDKs for Python, JavaScript, Go, and C#.

Seclai is the production infrastructure for AI applications. Orchestrate workflows, evaluate outputs, enforce governance, and switch between 200+ models — visually or in code. Every piece of a production LLM system, unified under one runtime. No orchestration stack to assemble. Every step in every run with status, latency, credits, and governance. Replay any trace from your dashboard. Write what you want screened in plain English. Every input and output is graded with flag and block thresholds you set. Define eval sets once. Run locally, in CI, and in production — with identical semantics. What you build and test is what runs in production. Clone any agent and it behaves identically, with…from seclai.com

Does a similar job

all alternatives →
  • Langfuse2023 · langfuse.com · ▲771

    Open source tracing and analytics for LLM applications

  • Forge – Guardrails take an 8B model from 53% to 99% on agentic tasksMay 2026 · github.com · ▲687

    Hi HN, I'm Antoine Zambelli, AI Director at Texas Instruments. I built Forge, an open-source reliability layer for self-hosted LLM tool-calling. What it does: - Adds domain-and-tool-agnostic guardrails (retry nudges, step enforcement, error recovery, VRAM-aware context management) to local models running on consumer hardware - Takes an 8B model from ~53% to ~99% on multi-step agentic workflows without changing the model - just the system around it - Ships with an eval harness and interactive dashboard so you can reproduce every number I wanted to run a handful of always-on agentic systems…

  • AI Observability by OpenObserveAug 2026 · openobserve.ai · ▲317

    OpenTelemetry-native observability for agents and LLMs

  • OpenLLMetry – OpenTelemetry-based observability for LLMs2023 · github.com · ▲154

    Hey HN, Nir, Gal and Tomer here. We’re open-sourcing a set of extensions we’ve built on top of OpenTelemetry that provide visibility into LLM applications - whether it be prompts, vector DBs and more. Here’s the repo: https://github.com/traceloop/openllmetry. There’s already a decent number of tools for LLM observability, some open-source and some not. But what we found was missing for all of them is that they were closed-protocol by design, vendor-locking you to use their observability platform or their proprietary framework for running your LLMs. It’s still early in the…

  • TrueFoundry AI GatewayDec 2025 · truefoundry.com · ▲315

    Many teams connecting LLMs to external tools eventually encounter the same architectural issue: as more tools and agents are added, the integration pattern becomes an N×M mesh of direct connections. Each agent implements its own auth, retries, rate limiting, and logging; each tool needs credentials distributed to multiple places and observability becomes fragmented. We built LLM gateway with this goal to provide a single place to manage authentication, authorization, routing, and observability for MCP servers, with a path toward a more general agent-gateway architecture in the future. The…

  • UD
    Unify – Dynamic LLM Benchmarks and SSO for Multi-Vendor Deployment2024 · ▲91

    Hey HN! I’m the founder of Unify, and we’ve just released our Model Hub, which provides a collection of LLM endpoints with live runtime benchmarks all plotted across time: https://unify.ai/hub A key finding is that static tabular runtime benchmarks for LLMs simply do not work. It’s necessary to take a time-series perspective, and plot the variations through time. We currently have 21 models provided by: Anyscale, Perplexity AI, Replicate, Together AI, OctoAI, Mistral AI and OpenAI, with more on the roadmap. We test across different regions (Asia, US, Europe), with varied…

More ai this month

the category →
  • Spira Maxima▲578

    Video model that turns script into viral social media videos

    AI · 23d ago · spira.ai

  • Rill Browser▲540

    The browser where Claude Code and Codex work beside you

    AI · 23d ago · rill.love

  • Germany's new sovereign AI model Kolibri▲424

    Kolibri is Aleph Alpha

    AI · 25d ago · tej.as

  • Giving Opus 5.5 a simulated paint canvas▲392

    Paintings by AI models: programs run through a simulation of oil paint.

    AI · 26d ago · stillwet.art

  • Beam.win▲378

    Run AI agents on one Mac and use its windows on another

    AI · 9d ago · beam.win

  • FastRouter.ai▲364

    Route requests to the right LLM for cost, latency & quality

    AI · 27d ago · fastrouter.ai

Source: Product Hunt launch ↗

Launched alongside, October 2026

the whole month →