Feed aggregator

• Lidl confirms cyberattack at third-party IT service provider that exposed customer data including names, phone numbers, emails, dates of birth, and customer numbers
• Passwords, payment details, and addresses were not affected, but the company warns of phishing risks and urges vigilance against identity fraud attempts
• Incident was contained quickly, reported to authorities, and investigated by forensic experts; Lidl operates ~12,900 stores across 32 countries

Lidl is warning its customers of a cyberattack which may have affected some of their personal information stored with the company.

In a data breach notification published on its Netherlands, Belgium, and Germany websites, the German discount supermarket chain said an IT security incident at one of its IT service providers affected some of the data stored by Lidl Online Shop customers.

“We were informed of this incident at the beginning of the week,” a machine-translated notification reads. “Despite high IT security standards, unknown persons briefly gained access to a separately stored file with customer data and part of the data was stolen from it. The system of the online shop itself is not affected.”

Unknown impact

Lidl said that the unnamed miscreants walked away with people’s full names, phone numbers, email addresses, dates of birth, and customer numbers. Passwords, billing and delivery addresses, bank details, and other payment information, was allegedly not stolen. Customer accounts remained unaffected, as well.

However, the company is urging its customers to remain vigilant, since there is a high chance the crooks will use the data to send personalized phishing emails.

“Although we currently have no concrete evidence of misuse of data, we warn you about possible phishing attempts or identity fraud as a precaution,” Lidl said.

The company did not say which IT service provider was targeted, or how many people are affected. It merely stated that the company “responded immediately” and “took necessary steps” to restore the full security of the affected systems. The company also filed a report with the relevant authorities, and called in IT forensic experts to investigate the incident.

Local authorities, such as the Dutch Data Protection Authority, or the Belgian “competent supervisory authority for data protection” were notified, as well.

Lidl operates around 12,900 stores across 32 countries in Europe and the United States.

Via Cybernews

IDC projects that AI infrastructure costs at Global 1000 companies will run 30% higher than current budgets by 2027. That gap shows a mismatch between how AI workloads behave in production and how enterprise IT has historically planned for capacity.

The pattern repeats across industries. A pilot project validates an AI model on a controlled dataset, and budgets are created around those economics. When the system moves into production, the bill often outpaces what anyone originally modeled.

The natural instinct is to blame the size of the model or the cost of using tokens, but that’s not where the money goes. The cost lives in the data layer, driven by how often the system reads, how many services it touches, and how continuously those operations run.

What pilots don’t show you

A pilot runs against a narrow dataset, with a handful of concurrent users, on a request-response cadence familiar to anyone who has shipped a web application. Production looks nothing like that.

Consider a generative AI customer support agent in production. A single user prompt can trigger simultaneous lookups across session activity, CRM records, inventory systems, retrieved manuals, and other sources before the model produces a response. All of this happens under sub-100ms latency budgets, with the slowest lookup gating the rest. The operational problem becomes tail latency across many small parallel lookups.

Now layer agentic workflows on top. A user request decomposes into a plan, then into a series of steps that each issue their own lookups, write intermediate state, and read it back. What starts as one inference expands into tens or hundreds of data accesses, with the system holding session and memory state across the entire arc. The cost profile that emerges is nothing like what the pilot priced.

Where the 30% comes from

The overrun comes from a series of defensive choices made under uncertainty. When teams can’t see how data flows through a single request, they over-provision to absorb spikes. When they can’t predict cache behavior under shifting context, they duplicate data across systems to reduce dependency risk.

When one downstream service slows down, they layer another service on top to insulate against it. Each choice is locally rational. The aggregate is a system that costs 30% more than the workload requires, and that’s before anyone adds a new use case.

The underlying problems are usually the same. Fan-out per request goes unmeasured end-to-end. Context gets fragmented across feature stores, session stores, user profile systems, vector indexes, and third-party APIs. KV cache and prefix reuse get left on the table because the inference layer can’t hold or share state across calls.

Replication and tiering decisions get made per system rather than per access pattern. None of these show up in a pilot. All of them show up in the production bill.

What the AI data tier has to deliver

AI in production is a continuous, distributed system whose hot path is context assembly — many small reads per request under tight latency budgets — combined with writes that must keep multiple representations of the same entity consistent.

These systems need two things at the same time: predictable low-latency reads under high concurrency and consistent writes across the data path. The infrastructure underneath has to be sized and shaped accordingly.

A few architectural decisions end up driving most of the outcome:

Match the data tier to the access pattern Session state, agent memory, feature lookups, retrieved context, and KV cache reuse all have different read patterns, freshness requirements, and durability needs.

Treating them as different data tiers — or laying them on whatever database happens to be in the stack — is the most common source of overrun. The session store and the system of record have different access pattern demands from the same data tier.

Engineer for fan-out and predictable tail latency Throughput is the wrong primary metric for an AI data tier. The right one is the predictability of many small reads triggered by one request. A batch of parallel lookups is only as fast as its slowest member, and a single slow lookup stalls the entire context-assembly step.

Storage systems optimized for write throughput pay a read amplification penalty under this access pattern. Systems that keep the primary index in memory and resolve point lookups in a single I/O behave differently at the tail.

Treat write consistency as a correctness requirement When updates across user profiles, embeddings, feature vectors, and session state aren’t synchronized, downstream context assembly reads a mix of versions and the model produces confident output grounded in contradictory data.

These are hallucinations that have nothing to do with sampling or model probability, and they don’t yield to better prompts or bigger models.

Treat inference-time data reuse as infrastructure. KV cache reuse, prefix sharing, and agent memory persistence are first-class infrastructure concerns. Teams that figure this out early run the same workloads at lower GPU utilization than teams that haven’t. This is the largest leverage point that doesn’t appear in most AI cost models.

Where to start

The most useful first step is to trace a single production request end-to-end — counting lookups, logging sources, and measuring tail latencies. That exercise reveals more than any architectural review. Once teams can see how data moves through one interaction, they can categorize data accesses by tier and verify that each is running on infrastructure suited to its pattern.

From there, the next question is what’s being recomputed that could be reused, particularly across inference calls and agentic steps. Fan-out per interaction should become a metric teams watch as closely as p99 latency — because at scale, it drives cost just as directly.

AI cost in production is a design discipline. Teams that address it early have far more control over performance and spend than teams that wait until the bill forces the issue. In many cases, the 30% gap is the cost of learning these lessons too late.

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Eyebot wants to make getting a prescription for glasses as fast as ordering a coffee.

These snakes can go for months without eating, grow and shrink the size of their hearts and jump start their metabolism on a dime.

(Image credit: Ari Daniel for NPR)

DeepSeek landed like a thunderclap in January 2025, when its R1 reasoning model briefly dethroned ChatGPT as the most downloaded free app on the iOS App Store in the United States. Built by a Hangzhou-based AI lab backed by Chinese hedge fund High-Flyer, it claimed to match frontier AI performance at a fraction of the development cost. That claim sent Nvidia's stock tumbling 17% in a single session and sparked a global conversation about who was actually winning the AI race.

Since then, DeepSeek has grown to roughly 97 million monthly active users and released multiple model generations, most recently the V4 family in April 2026. Its API pricing stands out: the V4 Flash model starts at $0.14 per million input tokens, cheaper than most "lite" tier models from OpenAI and Google yet competitive on coding, math, and reasoning benchmarks. The open-weight licensing under MIT also means teams can self-host the models and sidestep per-token costs entirely at scale.

We've been reviewing B2B software at TechRadar Pro since 2012, with AI platforms among our most active coverage areas in recent years. Our AI tools roundup, vibe coding guide for 2026, and explainers on OpenClaw and Moltbook give you a sense of the tools we track. DeepSeek is one of the more polarizing platforms we've tested: impressive in many ways, but not without significant red flags.

What is DeepSeek?

DeepSeek is an AI chat platform and API service developed by Hangzhou DeepSeek Artificial Intelligence Co., Ltd., a Chinese company founded in 2023 and funded by the quant hedge fund High-Flyer. It offers a free web and mobile chat interface at chat.deepseek.com alongside a paid developer API, both powered by the same underlying model family.

The platform runs on DeepSeek's own large language models, specifically V4 Flash and V4 Pro, both using a Mixture-of-Experts (MoE) architecture. Only a subset of each model's parameters activates per token, which keeps inference costs low without shrinking the model's overall knowledge base.

V4 Pro has 1.6 trillion total parameters but only 49 billion active at inference. V4 Flash runs 284 billion total with 13 billion active, making it significantly faster and cheaper without sacrificing much on everyday tasks.

Developers, researchers, and cost-conscious businesses are the natural audience. The free chat tier suits individuals and small teams exploring the tool, while the API's aggressive pricing makes it attractive for anyone building AI-powered applications at scale.

DeepSeek: At a glance

Attribute

Notes

Underlying model(s)

DeepSeek V4 Flash (284B total / 13B active params) and V4 Pro (1.6T total / 49B active params), both MoE-based

Best for

Coding assistance, mathematical reasoning, document analysis, budget API use

Distinguishing functions

1M token context, thinking/non-thinking modes, prompt caching, open weights (MIT)

UI features

Web chat and iOS/Android apps with web search toggle, file upload (PDF, DOCX, TXT), Expert Mode and Instant Mode

Subscription costs

Free (chat app, unlimited queries); no paid chat subscription tiers

API pricing

Pay-per-token; new accounts receive 5M free tokens valid 30 days; V4 Flash at $0.14 / $0.28 per 1M tokens (input/output); V4 Pro at $1.74 / $3.48 standard, with promotional discounts available

Buy it if…

• You need a cheap, capable coding or reasoning API. V4 Flash at $0.14 per million input tokens is among the most affordable frontier-adjacent APIs available, and its benchmark results on coding and math hold up well against more expensive competitors.
• You want open weights for self-hosted deployment. Both V4 models are released under MIT license on Hugging Face, giving teams the option to run the model on their own infrastructure and eliminate per-token costs entirely for high-volume workloads.
• You're doing non-sensitive exploratory work. For individual researchers, students, or developers prototyping non-confidential projects, the free chat app offers web search, file uploads, and a 1M token context window at zero cost.

Don't buy it if…

• Your work involves confidential or regulated data. All user data is stored on servers in mainland China, subject to Chinese law, which permits government access without user consent. Multiple governments have banned DeepSeek from official devices for exactly this reason.
• You need consistent responses on sensitive topics. DeepSeek avoids certain politically sensitive subjects, particularly around Chinese domestic affairs. That content filtering can produce evasive or incomplete outputs on topics that other platforms handle straightforwardly.
• You're in a GDPR-regulated region. Italy's data protection authority blocked DeepSeek outright in January 2025 after the company provided what regulators called a "completely insufficient" response to data practice inquiries.

My time with DeepSeek

I tested DeepSeek's chat app and API across a range of tasks: code generation, document summarization, long-form reasoning, and general Q&A. On raw capability, the V4 models impressed me. Code outputs were clean and well-structured, long document summaries were accurate, and the one-million-token context window handled full-length PDF ingestion without complaint.

The thinking mode, accessible via Expert Mode in the chat UI, added visible chain-of-thought reasoning that proved useful for multi-step problems rather than theatrical.

What gave me pause was everything outside the model itself. Certain politically sensitive prompts returned conspicuously vague or deflective answers — the kind of behavior that wouldn't be acceptable in a professional context where consistent and complete information matters. I also found that the chat interface lacks the memory and personalization features you'd find in ChatGPT or Claude.

Value for money on the API side is difficult to argue with. A production app with well-structured prompts benefits substantially from the caching discount: cached input tokens cost just $0.014 per million for V4 Flash, a 90% reduction. For high-volume, low-sensitivity workloads, that arithmetic is compelling.

DeepSeek: Features

DeepSeek's core chat feature set covers the bases you'd expect: text generation, code writing and debugging, document summarization, mathematical reasoning, and web search. The web search integration is a manual toggle rather than always-on, which keeps responses faster by default but requires you to switch it on when real-time information matters. File uploads support PDF, DOCX, and TXT formats, with the model able to summarize and answer questions based on the uploaded content.

The standout capability is the 1M token context window introduced with V4, up from 128K in the previous generation. That's a meaningful jump for anyone analyzing long contracts, codebases, or research documents in a single session. Most competitors at comparable price points max out at 128K to 200K tokens.

V4 Flash covers both thinking and non-thinking modes, so you don't need to switch between separate models depending on task complexity. Non-thinking handles fast general responses; thinking adds structured multi-step reasoning for harder problems. That flexibility matters more than it sounds when you're toggling between casual tasks and complex analysis in the same workflow.

Where DeepSeek falls short is multimodal support. The platform does not currently support image generation or image understanding in the web app, putting it behind ChatGPT, Claude, and Gemini on that front. Agentic capabilities are available in the V4 Preview but remain early-stage compared to dedicated agentic platforms.

DeepSeek: User experience

The chat interface at chat.deepseek.com is straightforward and fast to get started with. Signing up requires only an email address from a global provider like Gmail or Yahoo, and the default experience drops you straight into a conversation window. The distinction between Expert Mode (thinking-enabled, slower) and Instant Mode (faster, non-thinking) is surfaced clearly at the top of the interface, and mobile apps on iOS and Android mirror the web experience with file upload and web search included.

The learning curve is shallow for casual use. Switching between thinking and non-thinking modes takes one click, and the file upload workflow is drag-and-drop simple.

The API experience is less forgiving for first-time integrators. Unlike the chat app, the API is stateless, meaning every call must include the full conversation history in the messages array. DeepSeek's documentation covers this clearly, but it catches developers accustomed to managed conversation state elsewhere off guard.

DeepSeek: Customer support

Support options for free chat users are limited to a Discord community server and an email channel for API service inquiries (api-service@deepseek.com). Community responses on Discord can be prompt, but they depend on other users rather than official staff. There is no live chat or phone support.

API customers have slightly more recourse through direct email support, though response times vary. The official documentation at api-docs.deepseek.com is thorough and well-organized, covering model details, pricing, rate limits, and code examples in both Python and curl. For developers comfortable with self-service documentation, it's adequate.

(Image credit: DeepSeek)DeepSeek: Pricing

• Chat app is free with no query limits. The web and mobile apps give you unlimited access to V4 Flash and V4 Pro at no cost, including web search, file uploads, and the full context window.
• New API accounts receive 5M free tokens, valid for 30 days, giving developers a zero-cost window to prototype and test.
• API billing is pay-as-you-go. V4 Flash costs $0.14 per million input tokens and $0.28 per million output tokens. V4 Pro runs $1.74 and $3.48 respectively at standard rates, with promotional discounts periodically dropping those to $0.435 and $0.87. Cached input tokens cost one-tenth of the standard input rate on both models.

The free chat tier is generous by any measure. Unlimited queries with a 1M context window puts it ahead of most free-tier competitors in raw access terms, and there's no paid chat subscription to worry about. Power users who need more control either stick with the free app or pay per token via the API.

On the API side, DeepSeek makes a strong case for developers managing costs at scale. Off-peak pricing discounts of up to 75% are available during 16:30–00:30 UTC, giving teams with flexible scheduling another cost lever. For production apps with well-structured prompts sharing a common system context, effective input costs can drop well below $0.02 per million tokens with caching applied.

DeepSeek: alternatives you should consider

• ChatGPT (OpenAI): The most capable all-around AI platform, with image understanding, voice, and memory. API pricing is higher, but US-based data residency and enterprise data agreements make it a safer choice for sensitive business workloads.
• Claude (Anthropic): Particularly strong for long-form writing and document analysis, with comparable context windows and clearer data handling policies. Claude Pro starts at $20/month for individual users.
• Gemini (Google): Tightly integrated with Google Workspace and built for multimodal work across text, image, and video. Gemini 2.5 Flash offers competitive API pricing with no Chinese data jurisdiction concerns.

How I tested DeepSeek

• Used the free web app for code generation, document summarization, mathematical reasoning, multi-turn Q&A, and file analysis, covering both Instant Mode and Expert Mode across each task type.
• Ran API calls across varying prompt sizes to verify context window behavior, caching discounts, and response consistency, following DeepSeek's own temperature guidance for different task types.
• Cross-referenced DeepSeek's official privacy policy, third-party security research from NowSecure and SecurityScorecard, and regulatory actions from Italy, Australia, South Korea, and US government bodies to build a complete picture of the data risk profile.

Beyond hands-on testing, I reviewed DeepSeek's official API documentation, the V4 technical report published on Hugging Face, and benchmark data from the April 2026 release. Pricing figures were sourced directly from the official DeepSeek API documentation and corroborated against third-party tracking services.

DeepSeek landed like a thunderclap in January 2025, when its R1 reasoning model briefly dethroned ChatGPT as the most downloaded free app on the iOS App Store in the United States. Built by a Hangzhou-based AI lab backed by Chinese hedge fund High-Flyer, it claimed to match frontier AI performance at a fraction of the development cost. That claim sent Nvidia's stock tumbling 17% in a single session and sparked a global conversation about who was actually winning the AI race.

Since then, DeepSeek has grown to roughly 97 million monthly active users and released multiple model generations, most recently the V4 family in April 2026. Its API pricing stands out: the V4 Flash model starts at $0.14 per million input tokens, cheaper than most "lite" tier models from OpenAI and Google yet competitive on coding, math, and reasoning benchmarks. The open-weight licensing under MIT also means teams can self-host the models and sidestep per-token costs entirely at scale.

We've been reviewing B2B software at TechRadar Pro since 2012, with AI platforms among our most active coverage areas in recent years. Our AI tools roundup, vibe coding guide for 2026, and explainers on OpenClaw and Moltbook give you a sense of the tools we track. DeepSeek is one of the more polarizing platforms we've tested: impressive in many ways, but not without significant red flags.

What is DeepSeek?

DeepSeek is an AI chat platform and API service developed by Hangzhou DeepSeek Artificial Intelligence Co., Ltd., a Chinese company founded in 2023 and funded by the quant hedge fund High-Flyer. It offers a free web and mobile chat interface at chat.deepseek.com alongside a paid developer API, both powered by the same underlying model family.

The platform runs on DeepSeek's own large language models, specifically V4 Flash and V4 Pro, both using a Mixture-of-Experts (MoE) architecture. Only a subset of each model's parameters activates per token, which keeps inference costs low without shrinking the model's overall knowledge base.

V4 Pro has 1.6 trillion total parameters but only 49 billion active at inference. V4 Flash runs 284 billion total with 13 billion active, making it significantly faster and cheaper without sacrificing much on everyday tasks.

Developers, researchers, and cost-conscious businesses are the natural audience. The free chat tier suits individuals and small teams exploring the tool, while the API's aggressive pricing makes it attractive for anyone building AI-powered applications at scale.

DeepSeek: At a glance

Attribute

Notes

Underlying model(s)

DeepSeek V4 Flash (284B total / 13B active params) and V4 Pro (1.6T total / 49B active params), both MoE-based

Best for

Coding assistance, mathematical reasoning, document analysis, budget API use

Distinguishing functions

1M token context, thinking/non-thinking modes, prompt caching, open weights (MIT)

UI features

Web chat and iOS/Android apps with web search toggle, file upload (PDF, DOCX, TXT), Expert Mode and Instant Mode

Subscription costs

Free (chat app, unlimited queries); no paid chat subscription tiers

API pricing

Pay-per-token; new accounts receive 5M free tokens valid 30 days; V4 Flash at $0.14 / $0.28 per 1M tokens (input/output); V4 Pro at $1.74 / $3.48 standard, with promotional discounts available

Buy it if…

• You need a cheap, capable coding or reasoning API. V4 Flash at $0.14 per million input tokens is among the most affordable frontier-adjacent APIs available, and its benchmark results on coding and math hold up well against more expensive competitors.
• You want open weights for self-hosted deployment. Both V4 models are released under MIT license on Hugging Face, giving teams the option to run the model on their own infrastructure and eliminate per-token costs entirely for high-volume workloads.
• You're doing non-sensitive exploratory work. For individual researchers, students, or developers prototyping non-confidential projects, the free chat app offers web search, file uploads, and a 1M token context window at zero cost.

Don't buy it if…

• Your work involves confidential or regulated data. All user data is stored on servers in mainland China, subject to Chinese law, which permits government access without user consent. Multiple governments have banned DeepSeek from official devices for exactly this reason.
• You need consistent responses on sensitive topics. DeepSeek avoids certain politically sensitive subjects, particularly around Chinese domestic affairs. That content filtering can produce evasive or incomplete outputs on topics that other platforms handle straightforwardly.
• You're in a GDPR-regulated region. Italy's data protection authority blocked DeepSeek outright in January 2025 after the company provided what regulators called a "completely insufficient" response to data practice inquiries.

My time with DeepSeek

I tested DeepSeek's chat app and API across a range of tasks: code generation, document summarization, long-form reasoning, and general Q&A. On raw capability, the V4 models impressed me. Code outputs were clean and well-structured, long document summaries were accurate, and the one-million-token context window handled full-length PDF ingestion without complaint.

The thinking mode, accessible via Expert Mode in the chat UI, added visible chain-of-thought reasoning that proved useful for multi-step problems rather than theatrical.

What gave me pause was everything outside the model itself. Certain politically sensitive prompts returned conspicuously vague or deflective answers — the kind of behavior that wouldn't be acceptable in a professional context where consistent and complete information matters. I also found that the chat interface lacks the memory and personalization features you'd find in ChatGPT or Claude.

Value for money on the API side is difficult to argue with. A production app with well-structured prompts benefits substantially from the caching discount: cached input tokens cost just $0.014 per million for V4 Flash, a 90% reduction. For high-volume, low-sensitivity workloads, that arithmetic is compelling.

DeepSeek: Features

DeepSeek's core chat feature set covers the bases you'd expect: text generation, code writing and debugging, document summarization, mathematical reasoning, and web search. The web search integration is a manual toggle rather than always-on, which keeps responses faster by default but requires you to switch it on when real-time information matters. File uploads support PDF, DOCX, and TXT formats, with the model able to summarize and answer questions based on the uploaded content.

The standout capability is the 1M token context window introduced with V4, up from 128K in the previous generation. That's a meaningful jump for anyone analyzing long contracts, codebases, or research documents in a single session. Most competitors at comparable price points max out at 128K to 200K tokens.

V4 Flash covers both thinking and non-thinking modes, so you don't need to switch between separate models depending on task complexity. Non-thinking handles fast general responses; thinking adds structured multi-step reasoning for harder problems. That flexibility matters more than it sounds when you're toggling between casual tasks and complex analysis in the same workflow.

Where DeepSeek falls short is multimodal support. The platform does not currently support image generation or image understanding in the web app, putting it behind ChatGPT, Claude, and Gemini on that front. Agentic capabilities are available in the V4 Preview but remain early-stage compared to dedicated agentic platforms.

DeepSeek: User experience

The chat interface at chat.deepseek.com is straightforward and fast to get started with. Signing up requires only an email address from a global provider like Gmail or Yahoo, and the default experience drops you straight into a conversation window. The distinction between Expert Mode (thinking-enabled, slower) and Instant Mode (faster, non-thinking) is surfaced clearly at the top of the interface, and mobile apps on iOS and Android mirror the web experience with file upload and web search included.

The learning curve is shallow for casual use. Switching between thinking and non-thinking modes takes one click, and the file upload workflow is drag-and-drop simple.

The API experience is less forgiving for first-time integrators. Unlike the chat app, the API is stateless, meaning every call must include the full conversation history in the messages array. DeepSeek's documentation covers this clearly, but it catches developers accustomed to managed conversation state elsewhere off guard.

DeepSeek: Customer support

Support options for free chat users are limited to a Discord community server and an email channel for API service inquiries (api-service@deepseek.com). Community responses on Discord can be prompt, but they depend on other users rather than official staff. There is no live chat or phone support.

API customers have slightly more recourse through direct email support, though response times vary. The official documentation at api-docs.deepseek.com is thorough and well-organized, covering model details, pricing, rate limits, and code examples in both Python and curl. For developers comfortable with self-service documentation, it's adequate.

(Image credit: DeepSeek)DeepSeek: Pricing

• Chat app is free with no query limits. The web and mobile apps give you unlimited access to V4 Flash and V4 Pro at no cost, including web search, file uploads, and the full context window.
• New API accounts receive 5M free tokens, valid for 30 days, giving developers a zero-cost window to prototype and test.
• API billing is pay-as-you-go. V4 Flash costs $0.14 per million input tokens and $0.28 per million output tokens. V4 Pro runs $1.74 and $3.48 respectively at standard rates, with promotional discounts periodically dropping those to $0.435 and $0.87. Cached input tokens cost one-tenth of the standard input rate on both models.

The free chat tier is generous by any measure. Unlimited queries with a 1M context window puts it ahead of most free-tier competitors in raw access terms, and there's no paid chat subscription to worry about. Power users who need more control either stick with the free app or pay per token via the API.

On the API side, DeepSeek makes a strong case for developers managing costs at scale. Off-peak pricing discounts of up to 75% are available during 16:30–00:30 UTC, giving teams with flexible scheduling another cost lever. For production apps with well-structured prompts sharing a common system context, effective input costs can drop well below $0.02 per million tokens with caching applied.

DeepSeek: alternatives you should consider

• ChatGPT (OpenAI): The most capable all-around AI platform, with image understanding, voice, and memory. API pricing is higher, but US-based data residency and enterprise data agreements make it a safer choice for sensitive business workloads.
• Claude (Anthropic): Particularly strong for long-form writing and document analysis, with comparable context windows and clearer data handling policies. Claude Pro starts at $20/month for individual users.
• Gemini (Google): Tightly integrated with Google Workspace and built for multimodal work across text, image, and video. Gemini 2.5 Flash offers competitive API pricing with no Chinese data jurisdiction concerns.

How I tested DeepSeek

• Used the free web app for code generation, document summarization, mathematical reasoning, multi-turn Q&A, and file analysis, covering both Instant Mode and Expert Mode across each task type.
• Ran API calls across varying prompt sizes to verify context window behavior, caching discounts, and response consistency, following DeepSeek's own temperature guidance for different task types.
• Cross-referenced DeepSeek's official privacy policy, third-party security research from NowSecure and SecurityScorecard, and regulatory actions from Italy, Australia, South Korea, and US government bodies to build a complete picture of the data risk profile.

Beyond hands-on testing, I reviewed DeepSeek's official API documentation, the V4 technical report published on Hugging Face, and benchmark data from the April 2026 release. Pricing figures were sourced directly from the official DeepSeek API documentation and corroborated against third-party tracking services.

• Microsoft produced 20.3 million tons of CO2e emissions last year, up from 16.2 million tons
• Of the 37.5 million MWh of energy used, only 422,000 MWh came from unsustainable sources
• A rise in diesel/crude oil consumption and Scope 2 emissions are key drivers

In its latest sustainability report, Microsoft has admitted its greenhouse gas emissions actually rose 25.1% year-over-year from 16.2 million tons to 20.3 million tons in 2025.

With the company targeting a 2030 carbon negative deadline, rising emissions presents a major challenge that it must overcome, however current trends point to emissions continue to rise even further.

Microsoft said its rapid expansion of AI and cloud data centers as a key driver for rising emissions, and with more projects in the pipeline, this could be an ongoing challenge for years to come.

Microsoft's emissions are moving in the wrong direction

The company also noted its decision to stop buying short-term renewable energy certificates that do not directly support additional clean capacity. While the previous year's 16.2-million-ton figure was lower than last year's, it was largely offset by carbon credits and doesn't accurately represent the true emissions.

More broadly, Scope 2 and Scope 3 emissions are also under pressure from the continued data center expansion, due to electricity purchases, unsustainable construction materials and compute hardware. For example, Scope 2 emissions went from accounting for 1.6% of total emissions in FY24 to a staggering 13.3% in FY25.

As for fossil fuel use, the company saw a 51% rise in diesel and crude oil consumption despite reductions in natural gas (-6.5%), propane/LPG/jet fuel (-10%) and gasoline (-16%) use. Still, of the nearly 37.5 million MWh of energy the company used in FY25, only around 422,000 MWh came from non-renewable sources (per a separate data sheet).

However, despite expansion-related challenges, Microsoft did make significant progress to reducing overall emissions, hitting around 20 million tons last year instead of the 34 million tones it could have hit without work on carbon-free electricity, sustainable fuels, energy efficiency improvements and other supple chain refinements.

Microsoft isn't the only company battling the impacts of AI – Amazon also recently noted a 16% annual increase in emissions, while also blaming AI and data centers. Google also saw a 25% rise in emissions for its most recent full year.

IBM’s launch of its AI coding assistant “Bob” points to a much bigger shift in enterprise modernization. Across the industry, AI tools are being positioned as a way to make legacy systems easier to understand, assess and eventually modernize. And there is real value there.

Some of these tools can read thousands of lines of legacy code, identify deprecated APIs, summarize business logic and surface technical debt in minutes. For organizations carrying decades of operational history, that kind of visibility is a big step forward - but let’s not confuse visibility with modernization.

Understanding how a system works is necessary. It is not sufficient. I have seen teams produce clean dependency maps, detailed code summaries and impressive technical assessments, only to realize the hardest part starts after the AI has finished scanning the code.

Legacy estates rarely sit neatly off to the side. They are woven into the operating model of the business. They reflect years of process decisions, integration choices, compliance requirements, customer-specific exceptions and institutional knowledge that is often scattered, tribal or barely documented. Lovely little treasure hunt, except the treasure is risk

An AI model may identify an ageing integration point or highlight an application that supports a critical business process. That is helpful. But the real challenge begins when teams realize how many other systems, workflows and operational teams are connected to what looked like a straightforward change.

In many large organizations, legacy systems are still in place for a very simple reason: they work. They continue to perform reliably under demanding conditions, even if parts of the surrounding environment have evolved, degraded or become harder to support over time.

That is why modernization is not just a technology exercise. It is a sequencing exercise. It is a risk exercise. And, done properly, it is a business decision.

The multi-layer challenge

Every technical decision inside a legacy estate has consequences somewhere else. A change to one application can affect recovery procedures, audit requirements, licensing agreements, batch schedules, integration layers or support processes that have been stable for years.

This is where many modernization programs stall. Teams underestimate how interconnected these environments have become. AI can accelerate the technical assessment, but its real value comes when those insights are connected to the operational and commercial context around the system.

That distinction matters. Enterprises are moving away from broad “replace everything” strategies and becoming more selective. Not every legacy platform needs to be ripped out. Some systems need restructuring. Some need better interfaces. Some need to be moved. And some, frankly, should be left exactly where they are because they are doing their job reliably at scale.

Workload placement has become much more nuanced. Moving a service to public cloud may improve scalability and speed up software delivery, but it can also introduce data sovereignty concerns, latency issues, cost variability or new support dependencies.

At the same time, keeping workloads on modernized IBM Z or Power environments may provide more predictable performance for applications that already run effectively at scale.

The real question is not, “How do we get everything off legacy platforms?” The better questions are, “Which systems genuinely benefit from relocation, which need to be modernized in place, and which can be extended through modern interfaces?”

Without that context, organizations can spend a lot of money moving systems around without actually fixing the underlying problem. Congratulations, you now have the same complexity in a newer location.

We are already seeing this play out in enterprise environments where legacy platforms still sit at the center of high-volume operations. In one recent assessment, AI coding assistants were used to analyze more than six million lines of RPG code running on IBM Power systems, processing roughly 30 million requests a day.

The work surfaced technical debt and concentrated areas of complexity in weeks, giving the organization a clearer basis for deciding what to modernize, where to start and how to sequence change without disrupting core operations.

That is the practical value of AI in modernization: not magic, but better visibility, faster assessment and smarter prioritization.

Why enterprise AI deployments are becoming more specific

This broader shift is also showing up in how hyperscalers talk about enterprise AI adoption. Microsoft CEO Satya Nadella has described the market as moving from “discovery” into “widespread diffusion.” In plain English, the challenge is no longer just building impressive models.

It is embedding AI into real workflows, real operations and real business systems at scale. That is much closer to how modernization actually works inside large enterprises.

The same shift is happening with AI models themselves. The industry still loves to talk about scale, but most enterprise teams are not sitting around hoping for a trillion-parameter model to save them. They need tools that help engineers solve very specific problems inside environments that are already complicated enough.

In many cases, smaller, specialized models are proving more useful because they can be deployed in controlled ways, focused on specific tasks, and governed more tightly.

That governance point matters. Bringing AI into infrastructure operations raises very practical questions: What data can the model access? What systems can it touch? Can it recommend changes? Can it execute them? Who approves movement toward production?

That is another reason task-specific models are gaining traction. Teams can define exactly what the model is allowed to do, where human approval is required and how changes move through existing controls. In enterprise environments, that kind of control is not bureaucracy. It is how you avoid turning a productivity tool into tomorrow morning’s outage bridge.

Where AI is delivering practical value today

The organizations getting real value from AI are usually not the ones making the loudest claims about it. They are applying AI to engineering and infrastructure work that already consumes huge amounts of time: investigating incidents, mapping dependencies, validating changes, supporting regression testing and understanding how complex systems actually behave.

A lot of that work comes down to giving engineers better visibility and helping them get to root cause faster.

AI models can help connect runtime anomalies to recent code changes. They can reduce the time teams spend manually tracing incidents across hybrid environments. They can support regression testing around older applications and surface integration dependencies that were previously difficult to visualize across multiple infrastructure layers.

That becomes especially important in environments where cloud-native services sit alongside long-established mainframe and midrange systems. In many organizations, the hardest problems show up in the seams between those environments, particularly when different teams manage different parts of the estate with different tools, different metrics and different operating rhythms.

That is why the most useful AI deployments tend to focus on practical engineering work, not grand attempts to automate everything at once.

Organizations are seeing value in areas that are repetitive, complex and difficult to scale manually. Automated test generation can reduce regression risk around legacy applications. AI-supported observability correlation can shorten incident investigation cycles. Dependency analysis can help teams prioritize infrastructure work that removes bottlenecks affecting service delivery.

In most cases, AI is not replacing engineering judgment. It is improving the work engineering and infrastructure teams already understand well. And that is where the expectations need to be clear.

AI can absolutely speed up discovery. Work that once took weeks of manual assessment can now happen much faster. But that is usually the point where the real work starts.

A model can tell you how systems connect. It cannot tell you how much disruption the business is prepared to absorb. It cannot decide which customer commitments matter most. It cannot magically unwind 25 years of operational dependency while everyone politely keeps breathing.

Technology leaders should view AI coding assistants as decision-support tools for broader infrastructure and modernization strategies, not as stand-alone solutions to legacy complexity.

IBM’s Bob announcement shows how quickly these capabilities are advancing, especially when it comes to understanding legacy code and helping teams work through large, complex estates. But visibility only matters if organizations can turn it into practical change without creating instability elsewhere.

AI can help you read the legacy estate. It can help you understand the risk. It can help you move faster. But modernization still requires judgment, sequencing and operational discipline.

That part is still very human.

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