Synvolv

AI isn’t expensive. Uncontrolled AI is. No errors. No alerts. No downtime. Just a $47k invoice because: • an agent loop didn’t exit • a feature doubled tokens • one customer went viral Nothing broke. That’s the new failure mode. 98% of FinOps teams now manage AI spend. We’re building Synvolv to stop runaway cost before the meter runs. If AI has ever surprised your bill, what caused it? #AI #FinOps

@Crowdreply_io We’re getting really good at measuring AI behavior, still pretty bad at controlling it.

We just crossed $5M ARR fully bootstrapped Introducing CrowdReply 2.0 The new benchmark of ranking in AI Answers

@OpenAIDevs every time models get cheaper people just find more ways to use them

We’re introducing GPT-5.4 mini and nano, our most capable small models yet. GPT-5.4 mini is more than 2x faster than GPT-5 mini. Optimized for coding, computer use, multimodal understanding, and subagents. For lighter-weight tasks, GPT-5.4 nano is our smallest and cheapest version of GPT-5.4. openai.com/index/introduc…

@GergelyOrosz Agents writing code is wild. But the infra cost of AI-assisted development is still massively under-discussed.

OK, fresh data from Uber, from Feb 2026: 31% of code is AI-authored 11% of PRs opened by agents And Uber is investing heavily in AI So outside Anthropic + AI labs, we are a far way out, probably? Source: newsletter.pragmaticengineer.com/p/how-uber-use…

Exactly one year ago (10 mar 2025), Dario Amodei: "I think we will be there in 3-6 months, where AI is writing 90% of the code. And then, in 12 months, we may be in a world where AI is writing essentially all of the code." This turned out to be... too darn accurate.

@hwchase17 Agent UX is hard because the system isn’t deterministic. Cost behavior isn’t either.

UI/UX matters a ton for agents. And is quite hard to get right we're investing a lot more in frontend hooks to work with LangChain, LangGraph, DeepAgents!

@togethercompute 1M token context unlocks crazy workflows. But it also means one request can burn more compute than an entire prompt pipeline used to.

Highlights: 👉 Multimodal reasoning—unified vision-language model for text, image, and video understanding 👉 Native tool calling—66.1% on BFCL-V4, 79.1% on TAU2-Bench for production-ready agentic workflows 👉 262K native context—extensible to 1M+ tokens for long-horizon tasks 👉 Production-ready on the AI Native Cloud—99.9% SLA, serverless and dedicated infrastructure

Introducing Qwen3.5 9B, Qwen's multimodal foundation model combining text, image, and video understanding with native tool calling and 262K context. Available now on Together AI at $0.10 input / $0.15 output for multimodal agentic workflows.

Most cost discussions focus on model pricing. But behavior multiplies cost faster than pricing ever will.

The hidden cost driver in AI systems isn't the model. It's everything around it. • retries • agent loops • tool calls • longer prompts • bigger context windows One request becomes five. And the bill scales quietly. #FinOps #LLM

Curious how teams handle this: If a single AI request exceeds its cost budget, do you: A) Fail the request B) Downgrade the model C) Truncate the response D) Let it run anyway Most systems don’t enforce anything. They just measure the damage later. #AI #FinOps

Traditional infra has predictable cost curves. AI doesn't. One prompt tweak, one retry policy, one viral customer, and your margins move overnight.

AI has a strange failure mode. Success can make it financially worse. More users → more tokens → more retries → more cost The system improves. Margins shrink.

Topics: #AI #FinOps #LLM #DevTools

A team shipped an AI feature on Friday. Pretty normal setup: • GPT-4 for answers • retries for reliability • an agent that could call tools Everything worked. No errors. No alerts. Latency looked fine. On Monday finance asked a question. “Why did AI spend jump $38k this weekend?” Nothing was hacked. Nothing was broken. A retry loop + longer prompts quietly multiplied tokens. AI failures are strange. The system succeeds technically. And fails financially. #AI #FinOps

Most teams monitor AI spend. But by the time the dashboard updates, the tokens are already spent. That’s the real gap.

The first real AI outage most companies will face won’t be downtime. It’ll be a bill. $30k $80k $200k Because nothing actually broke. • the agent kept retrying • the prompt kept expanding • the model kept answering AI doesn't fail loudly. It fails financially. #AI #FinOps

AI models are getting cheaper. AI bills are getting bigger. Because the real cost driver isn’t price. It’s behavior: • longer outputs • reasoning tokens • retry loops • agent chains Cheaper AI just means people run it more. And cost failures scale silently.

@RoryCrave Optimization saves money. Enforcement prevents loss. Most teams fix routing after the bill spikes. The real shift is constraining model choice + spend at request time.

Just saved a client $2.3M/year on their AI infrastructure. They were doing everything "right." Here's the problem nobody tells you about scaling OpenAI in production 🧵

The next wave of AI infra won’t be observability. It’ll be enforcement.

• A request comes in • Model selection happens • Retries happen • Tool calls happen • Tokens expand • Context grows • All before finance sees anything. AI cost is runtime behavior. If you can’t intervene there, you’re watching, not controlling.

AI doesn’t need better dashboards. It needs budget enforcement at runtime.

@mattturck The uncomfortable shift is that AI vendors now monetize volatility. Usage-based is fine, until customers have no control surface. Enterprises don’t mind paying for value. They mind being unable to predict or enforce it.

One under-discussed reality of AI today is that a lot of Global 2000 enterprises are starting to feel pretty victimized by their vendors right now - hearing things like: * I liked the old product, why am I being forced to upgrade to your new AI product on your timeline * the FDE thing is cool, but does that mean we need to pay you forever or we can't operate our own system? * token-based or usage-based makes sense, but hard to predict and your credit system is complicated * your contract says you do nothing with our data, and that's fine but you run on [multiple AI model/infra vendors], how do I know my data is safe with them If there's one lesson for startups/scaleups: be easy to work with.