DeepSeek connector

Use your DeepSeek data for reporting, automation and AI.

Data Panda brings your DeepSeek API usage data together with the data from the rest of your business. From one place, we turn it into dashboards, automations, AI workflows and custom apps your team uses every day.

About DeepSeek

Where your cheap-tier AI bill really comes from.

DeepSeek was founded on 17 July 2023 in Hangzhou by Liang Wenfeng, who also founded the High-Flyer hedge fund that backs the lab. The company released DeepSeek-V2 in May 2024 with 236 billion parameters of which 21 billion are active per token, then DeepSeek-V3 in December 2024 with 671 billion parameters and 37 billion active, and DeepSeek-R1 on 20 January 2025. R1 is the reasoning-focused model that triggered a major US tech selloff the week after its release, on the basis that a Chinese lab had trained a frontier-class reasoner for a reported six million dollars in compute against the hundred-million-dollar figure attached to GPT-4. The model weights are published under the MIT License; the training data is not.

For a warehouse the API is what matters. The line-up exposed at api.deepseek.com is deepseek-chat for the general workhorse and deepseek-reasoner for the chain-of-thought tier, both routed under the v4-flash family with v4-pro as the higher-performance variant. Pricing is roughly an order of magnitude below the comparable OpenAI or Anthropic tiers, with cache-hit input tokens charged at a small fraction of the cache-miss rate, an off-peak discount window, and a context-caching feature that matters for any workload that reuses the same system prompt. Reasoner output includes the chain-of-thought tokens in the bill, so a single hard question on deepseek-reasoner can spend more output tokens than ten chat-mode answers. The endpoints to pull are the chat-completions usage records, the model list, the balance and billing endpoints and the cache-hit accounting, so finance and product can split spend per API key, per model, per cache state and per peak window.

What your DeepSeek data is for

What you get once DeepSeek is connected.

AI spend attributed to features and customers

Token spend, model mix and cache-hit share per API key and per model on one timeline.

Spend per API key joined to the product feature behind it
deepseek-chat versus deepseek-reasoner mix per week
Cache-hit input tokens against cache-miss input tokens, with the off-peak discount share on top

Cost-control automation

Push usage signals back into the tools where decisions about DeepSeek really get made.

Slack alert when daily reasoner spend on one feature crosses a budget
API key paused when a workspace burns its prepaid balance ahead of schedule
CRM contact tagged when a customer's AI feature runs above its contracted token allowance

AI workflows on AI usage

Use DeepSeek usage history to feed the next round of model and routing decisions.

Routing scoring that picks deepseek-chat or deepseek-reasoner per request based on past quality and reasoning-token cost
Prompt-template ranking on output tokens per task, including chain-of-thought tokens for reasoner runs
Drift detection on system-prompt length per template to catch prompts that lost the cache

Custom apps on your data

Internal tools on DeepSeek usage data for teams that do not log into the DeepSeek console.

AI cost dashboard per product feature, per customer segment, per week
Off-peak scheduling assistant that shows which batch workloads still run inside the discount window
Per-customer AI-usage view next to MRR for finance and customer success

Use cases

Use cases we deliver with DeepSeek data.

A list of concrete reports, automations and AI features we have built on DeepSeek data. Pick the one that matches your situation.

Token spend per featureInput, output and cached-input tokens per API key, joined to the product feature behind the key.

Chat-versus-reasoner mixShare of calls and spend across deepseek-chat and deepseek-reasoner per workspace, per week.

Cache-hit ratio per templateCache-hit input tokens divided by cache-miss input tokens, per template and per workspace.

Off-peak discount adoptionShare of calls landing inside the off-peak window, with the saving against the on-peak rate.

Reasoning-token explosionChain-of-thought output tokens per reasoner request, ranked to surface the prompts that produce the longest thinking traces.

Per-customer AI usageToken spend joined to CRM customer, contract tier and MRR.

Frontier-versus-DeepSeek routingShare of traffic routed to DeepSeek against the frontier providers in the same warehouse, per workload type.

Prepaid balance burnSpend against the prepaid balance per workspace and projected runway in days.

Output-token drift per templateAverage output tokens per template over time, to catch responses growing silently.

Multi-account consolidationUsage across several DeepSeek workspaces rolled up into one picture.

Real business questions

Answers you will finally get.

Which feature is driving our DeepSeek bill?

Token spend per API key over the last thirty days, joined to the product feature behind each key, with the deepseek-chat versus deepseek-reasoner split on top. Surfaces the one classifier that quietly migrated to deepseek-reasoner after a release and now spends ten times the output tokens it used to, before the next prepaid balance refill arrives as a single line.

Are our reasoner calls really paying for themselves?

Chain-of-thought output tokens per reasoner request ranked by template, with the cache-miss share alongside. Catches the template whose system prompt grew past the cache window after a release, so every call now pays the cache-miss rate on input and the full thinking trace on output, and the user gets the same answer they used to get from deepseek-chat at a fraction of the cost.

Which customers are pulling the heavy reasoner traffic?

Token spend joined to CRM customer, contract tier and MRR, with reasoner-token spend per customer ranked against their tier allowance. Shows the customer on a small plan whose AI assistant routes every question through deepseek-reasoner with thirty-thousand-token chain-of-thought traces, so account management gets a real number to take into the renewal conversation instead of a hunch.

Value for everyone in the organisation

Where each function gets value.

For finance leaders

DeepSeek spend per product feature, per customer segment and per cache state instead of one prepaid-balance line. The cheap tier stops being cheap by accident the day a feature flips to deepseek-reasoner; the curve catches it before the next refill.

For sales leaders

AI usage per customer in the same record reps already open, so a customer running heavy reasoner traffic on a small plan becomes a renewal conversation instead of a surprise on the year-end review.

For operations

Cache-hit ratio, off-peak share, reasoning-token length and template drift over ninety days. The behaviour of the AI features is followed as a curve, not rediscovered the morning a release routed every classifier through the reasoner.

Ideas

What you can automate with DeepSeek.

Pair with Slack

Push DeepSeek spend alerts into Slack

Daily token spend per API key from the DeepSeek billing endpoint lands in Slack as a per-feature line, with the deepseek-reasoner share called out separately. The product team gets a ping the day reasoner traffic on a previously chat-only feature crosses its budget, instead of finding out a week later when the prepaid balance drops faster than planned. Threshold breaches reference the workspace, the model and the API key so the on-call engineer knows where to look first.

Pair with HubSpot

Sync per-customer DeepSeek usage into HubSpot

DeepSeek token spend per API key is mapped to the HubSpot customer it serves and lands on the contact record next to MRR and contract tier, with reasoner-token spend split out from chat-token spend. Account managers see the customer on a small plan whose AI assistant routes every question through deepseek-reasoner before the renewal conversation, and customer success can flag accounts whose reasoner usage is creeping toward what the contract assumed.

Pair with PostHog

Join PostHog product events with DeepSeek token usage

PostHog events for AI features (prompt submitted, agent task started, summary generated) are joined to DeepSeek usage records on workspace and timestamp. Product gets cost per AI action, including the chain-of-thought output tokens of every reasoner call, so a feature that fires one reasoner question per click becomes visible against a feature that fires ten chat-mode classifications. The same join answers which features benefit from the order-of-magnitude cheaper rate and which would have been better off on a frontier model with shorter outputs.

Pair with Fireflies.ai

Map Fireflies meeting summaries to DeepSeek inference cost

Fireflies meeting IDs that triggered a DeepSeek-driven summary or action-item extraction are joined to the usage records on the API key and time window of the run. Revenue operations sees cost per summarised meeting split between chat-token spend and reasoner-token spend, so the choice between summarising every internal call on deepseek-chat and only the closed-won ones on deepseek-reasoner stops being a guess. The same view shows which sales teams pull the most reasoner-token cost per week.

Your existing tools

Your data lands in a warehouse. Your BI tools read from it.

You keep the reporting tool you already have. We connect it to the warehouse where your DeepSeek data lives.

Power BI Microsoft

Fabric Microsoft

Snowflake Data warehouse

BigQuery Google

Tableau Visualisation

Excel Sheets & pivots

Three steps

From DeepSeek to answers in three steps.

Connect securely

OAuth authentication. Read-only by default. We sign a DPA and your admin keeps the keys.

Land in your warehouse

Data flows into your warehouse on your schedule. Near real time or nightly, your call. You own the data.

Reporting, automation, AI

We build the first dashboard, workflow or AI feature with you, then hand over the keys. Or we stay on for ongoing delivery.

Two ways to work with us

Pick the track that fits how you work.

Track 01

Self-serve

We set up the foundation. Your team builds on top.

DeepSeek connector configured and running
Warehouse set up in your cloud account
Clean access for your Power BI, Fabric or Tableau team
Documentation on what's in the data model
Sync monitoring so you're warned before reports break

Best fit Teams that already have a BI analyst or data engineer and want to own the build.

Track 02

Done for you

We build the whole thing, end to end.

Everything in Self-serve
Dashboards built to the questions your team actually asks
Automations between your systems
AI workflows scoped to real tasks your team runs
Custom apps where a dashboard does not cut it
Ongoing delivery at a pace that fits your team

Best fit Teams without in-house BI or dev capacity. You tell us what you need and we deliver it.

Before you book

Frequently asked questions.

Who owns the data?

You do. It lands in your warehouse, on your cloud account. We don't resell or aggregate it. If you stop working with us, the warehouse stays yours and keeps running.

How fresh is the data?

Near real time for most operational systems. For heavier sources we schedule hourly or nightly. You pick based on what the reports need.

Do I need a warehouse already?

No. If you don't have one, we help you pick one and set it up as part of the first delivery. Common starting points are Snowflake, Microsoft Fabric, or a small Postgres start.

Which DeepSeek usage data does the connector really pull?

The chat-completions usage records, the model list, the workspace billing and balance endpoints and the cache-hit accounting are the primary sources. Per API key and per model the connector returns input tokens split into cache-hit and cache-miss buckets, output tokens including the chain-of-thought tokens of reasoner runs, request counts, the timestamp of each call (so the off-peak-window share is computable) and the prepaid-balance state. Customer prompts and completions are not pulled, only the metering. Anything else in the warehouse, like which feature owns which API key, has to be joined in from your own systems.

Does the bill for deepseek-reasoner include the chain-of-thought tokens?

Yes. DeepSeek bills reasoner output as a single output-token count that includes the visible answer plus the chain-of-thought trace the model produced before it. A hard question that triggers a forty-thousand-token thinking trace will show up as a forty-thousand-token output line on the usage record, even if the user only sees a two-paragraph answer. The connector keeps the model and the per-call output count separate, so the templates that generate the longest thinking traces are rankable next to the ones that do not.

What about EU data residency and the China-origin question?

DeepSeek is a Chinese company headquartered in Hangzhou and the API is served from infrastructure outside the EU, so any team subject to GDPR data-transfer rules, sector-specific guidance from a national supervisory authority, or an internal restriction on Chinese-origin AI providers has to clear that before pointing customer data at the API. The connector pulls the metering, not the prompts or completions, so the warehouse side is the same as for any other LLM provider; the question of whether your workloads are allowed to call the API in the first place sits with your DPO and your security team, not with the integration.

GDPR-compliant

Data stays in the EU

You own the warehouse

A first deliverable live in four to six weeks.

We review your DeepSeek setup and the systems around it. Together we pick the first thing worth building.

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