Operations | Monitoring | ITSM | DevOps | Cloud

Learn how to allocate cloud spend, rightsize Kubernetes safely, stop idle resources, manage commitments, and prevent cost spikes without risking reliability.

September is associated with football, foliage, flannel and, for some, the Financial Plan. As we put pen to paper (or agents to harnesses), there’s a few core elements that have always driven the P&L outlook for the following year: rep productivity and new product releases driving new sales, expansion and contraction against the install base, employee roster changes, and discretionary spend.

Somewhere in your company, a team is three weeks into the AI project that’s going to matter. Somewhere else, a support pilot from the spring is still summarizing every ticket with a frontier model, and nobody has looked at it since it started working. On the invoice they’re identical, and the company has two moves: leave everything open, which funds the waste, or cap everyone, which kills the bet.

15 AWS cost optimization tactics across compute, storage, networking, and monitoring plus a quick-reference table by effort and savings potential.

Learn why cloud cost optimization for engineers fails and how to fix it with developer-centric FinOps practices. See how Harness helps. Engineers often ignore cloud costs due to a lack of visibility, context, and ownership in their daily workflows. By shifting cost governance left and integrating real-time cost insights into CI/CD pipelines, teams build lasting cost accountability.

An LLM inference provider hosts open-weight models like Llama, DeepSeek, and Qwen behind a pay-per-token API, handling GPUs, scaling, and serving for you. The same Llama 3.3 70B model ranges from $0.10 to $1.04 per million input tokens depending on who serves it, so provider choice is a pricing decision. Top picks as of September 2026: Groq and Cerebras for speed, DeepInfra for price, Together and Fireworks for breadth, Baseten for custom models.

Build when the problem is narrow (one provider, one team, simple attribution) and the tooling is strategically yours to own. Buy when AI spend spans providers, arrives untagged, and needs unit costs finance will trust, because that build is a multi-quarter platform project with a permanent maintenance tail. Price both paths in engineer-years before deciding. CloudZero sells the “buy” side.

Perplexity pricing runs $0 for Free, $20 a month for Pro, and $200 a month for Max, with enterprise seats listed from $40 per user. Pro fits most people who search for work daily. Max exists for heavy automation. The prices are verified against Perplexity's live plans page as of September 2026. When Perplexity published a customer quote on its own pricing page, it chose an unusual one.

Kling AI pricing runs from a free tier of 66 daily credits to a reported $180 per month, with annual billing about 34% cheaper. Kling 3.0 bills per second: 6 to 12 credits for standard resolutions and 30 for native 4K. The API sells separate prepaid packages from $9.80 to $7,560.

ElevenLabs pricing runs from a free plan to $990 per month across six published tiers, with custom Enterprise above that. Plans meter usage in credits, where one credit roughly equals one character of speech. Voice agents cost $0.08 per minute on every tier, plus separate LLM and telephony charges. The AI agency PxlPeak published its own ElevenLabs invoice: $303 for January 2026, covering voice content for six clients, IVR systems for two, and live voice agents for three.