Negotiating Google Generative AI Contracts
Negotiating access to Google’s generative AI services (Vertex AI, PaLM models, Gemini APIs, and related Cloud AI offerings) requires a hard-nosed, detail-oriented approach. Google’s standard contracts favor Google, so CIOs and procurement leaders must be prepared to push back on key terms.
This playbook lays a blunt guide to the commercial and legal points you must address. Use the tips and examples below to secure a fair deal that protects your enterprise’s interests.
Key Commercial and Legal Points (Summary)
- Pricing & Commitments: Google will propose usage-based pricing (per character/token or API call). Scrutinize the pricing model and challenge any mandatory minimum spending or long-term commitments. Demand volume discounts for higher usage and compare costs to rivals (Azure/OpenAI, AWS) to gain leverage.
- Usage Limits & Discounts: Watch for usage caps or throttling. Negotiate generous usage limits with predictable overage fees or, better yet, automatic scale discounts as your consumption grows. Aim for economies of scale—the unit cost should drop as you use more.
- Intellectual Property: Ensure you own what you create. Your contract should confirm that your company retains ownership of all AI-generated outputs and any custom models or fine-tuned versions you develop. Guard against any language that grants Google broad rights to your data or derivatives.
- Data Handling & Privacy: Insist on strict data usage clauses. Your data (prompts, inputs, and outputs) must not be used to train Google’s models or for any purpose outside service delivery. If you require regional data residency (e.g., EU-only processing), get it in writing.
- Service Levels & Performance: Demand high uptime SLAs (e.g., 99.9% or better) with meaningful outage penalties or credits. Clarify the models’ performance expectations (response times, throughput) and get commitments on support if the model quality degrades. Negotiate retraining or model upgrade support for critical use cases to maintain accuracy over time.
- Indemnity & Liability: Secure strong indemnities for intellectual property and data risks. Google should stand behind its model—if it infringes copyrights or leaks data, it covers it. Also, push for reasonable liability caps; Google’s default cap is low, so negotiate higher caps or unlimited liability for breach of confidentiality.
- Audit & Compliance: If you operate in a regulated industry, reserve audit rights or the right to receive third-party audit reports and security compliance evidence. As part of the deal, Google must support your compliance needs (certifications, data processing agreements, etc.).
- Integration & Ecosystem Costs: Account for the cost and effort of integrating Google’s AI into your environment. Negotiate for support, such as free technical guidance, training, or credits for related Google Cloud services. Clarify responsibilities for any third-party components or partners involved in the solution.
- Exit Strategy & Lock-In: Plan your exit before you sign. Ensure you can retrieve all your data (prompts, outputs, fine-tuning datasets) in a usable format upon contract termination. Avoid proprietary dependencies and negotiate provisions for transitioning to another platform to mitigate lock-in risk.
These areas are discussed in detail below, with blunt advice and examples to help you during negotiations.
Pricing Models – Challenge the Cost Structure
Google’s generative AI pricing is typically usage-based and can be complex. Text models often charge per 1,000 input and output characters (Google uses character counts, whereas OpenAI uses token counts).
For example, as of 2024, Google’s list price for the Gemini model was around $0.0035 per 1k input tokens and $0.0105 per 1k output tokens for a high-end model. Those fractions of a cent add up fast at an enterprise scale. Be prepared to grill Google on pricing details and push back hard on anything unreasonable:
- No Blank Checks: Avoid agreements that say “pay per use” without clear rates or cost ceilings. Demand a rate card for each service or model you’ll use. If Google’s pricing page lists the rates, use that as a baseline but negotiate from there – enterprise deals can improve on list prices. For example, if the public price is $0.003 per 1k characters, ask for a discount to $0.002 for your expected volume.
- Volume Discounts: Leverage your scale. If you expect heavy usage, negotiate tiered pricing. For instance, usage up to X billion characters at one rate, then a lower rate beyond that. Challenge Google to match or beat competitor pricing. Remind them that OpenAI (via Azure) or AWS are alternatives – you will take your workload elsewhere if the cost per query is too high. This external leverage is one of your strongest cards in pricing talks.
- Committed Spend vs. Pay-as-you-go: Google might push for a committed spend contract (e.g., commit to $Y million over 1-3 years for a discount). Treat this like any enterprise license negotiation: only commit if you’re confident in usage levels and get a significant discount in return. If they want a multi-year commitment, insist on locking the unit price for that term (no surprise hikes) and perhaps a clause to re-evaluate if your actual usage diverges greatly from the estimate.
- Overage Charges: Scrutinize those terms if Google proposes a usage cap (e.g., a certain number of API calls or characters per month) with overage fees. Negotiate for minimal or no premium on overages. Ideally, any overage should be charged at the same discounted rate rather than a higher on-demand rate. You don’t want a sudden price spike if your usage exceeds forecasts.
- Transparency and Reporting: Require detailed usage reports and cost breakdowns. As part of the contract, Google should provide regular (e.g., monthly) reports showing exactly how many characters/tokens were processed and the cost so you can verify charges. This also helps spot anomalies (e.g., runaway usage from a buggy app) early.
- Example Negotiation Tip: If Google’s initial offer prices seem inflated, counter with data. For example: “Our analysis shows Azure’s OpenAI equivalent would cost 20% less at our volume. We need Google to at least match that price, or we have a hard time justifying this partnership internally.” Backing your challenge with competitive numbers forces them to adjust pricing or justify the premium with additional value.
In short, don’t accept list prices as-is. Everything is negotiable for big enterprise deals – but only if you ask. Push for a pricing model that aligns with your budget predictability, scales with your growth, and reflects the value you bring as a large customer.
Usage Limits, Overage Terms, and Scale Discounts
In addition to raw pricing, closely negotiate how usage limits and scaling are handled. Google’s generative AI services might have default quotas or throttling, especially if the tech is new or in preview. You need flexibility to scale usage if adoption grows without punitive terms. Key points to address:
- Generous Quotas: Ensure any initial usage limits (requests per second, characters per day, etc.) are high enough for your needs. Enterprise use can quickly surpass default quotas meant for small developers. Negotiate higher limits or an agreement that Google will not throttle your usage if you comply and pay for it. You don’t want your critical application cut off because it hit an arbitrary cap.
- Automatic Scaling: If your usage might spike (e.g., seasonal loads or unpredictable demand), negotiate how scaling beyond projections is handled. Avoid the need for manual approvals for higher usage. Instead, set a framework (perhaps with cost tiers) so your service keeps running, and you pay the agreed rate for the extra usage. Also, discuss technical scaling limits – can Google’s infrastructure handle your peak load? If not, you need to know upfront.
- Overage Protection: As mentioned in pricing, fight against overage penalties. It’s reasonable to pay for what you use but not to be gouged if you exceed a forecast. Try to bake in an “elastic” range in the contract – e.g., you commit to X usage but can exceed it by Y% at the same rate. If they insist on higher rates past a threshold, cap how much higher and include a provision to revisit the contract if consistently in overage (essentially triggering a renegotiation for a new volume tier).
- Scale Discounts and Growth: If you anticipate significant growth in usage over the contract term, negotiate built-in discounts that trigger at scale. For example, “if monthly usage exceeds 100 million characters for 3 months, the unit price will drop by 15% thereafter.” Make Google a partner in your success – as your usage (and spending) grows, they earn more revenue but at a slightly lower margin. This can be framed as a win-win.
- Unused Capacity: On the flip side, protect yourself from paying for unused capacity. If you are committed to a certain amount of spending or volume and your adoption lags, try to include carry-over clauses or flexibility. For example, unused volume in one quarter can roll over to the next. Or suppose by mid-contract you see you won’t use what was planned. In that case, you can talk with Google to reallocate some of that commit to other Google Cloud services (this kind of flexibility can sometimes be negotiated in enterprise agreements).
- Peak vs. Off-Peak: Another angle—if your usage has predictable patterns (like heavy weekday use and light weekends), see if Google will accommodate a burst capacity at no extra charge as long as overall usage stays within monthly limits. Cloud providers sometimes offer burst ability (temporarily exceeding rates) as long as it averages out. Ensure the contract doesn’t consider short-term spikes breaches of the usage agreement.
- Example: Suppose Google proposes a maximum of 50 requests per second to the model and charges 20% extra for any usage beyond 500k monthly requests. You might counter: “Our business could need 2-3x that throughput at peak. We need at least 150 requests/sec, which is guaranteed. Also, rather than a 20% premium after 500k calls, let’s agree that after 500k, we simply continue at the normal rate up to 1 million and revisit pricing if we go beyond that consistently.” Always tie it back to business needs: you require headroom to grow.
Bottom line: Nail down how usage is controlled and charged. A good contract ensures you have the capacity you need without constant manual intervention and that you benefit from economies of scale rather than being punished for success.
Intellectual Property Ownership and Derivative Rights
IP ownership can be a make-or-break issue when using generative AI. Google’s services will generate content (text, code, images, etc.) based on your prompts, and you might also fine-tune Google’s models with your proprietary data. You must ensure your company retains full rights to anything generated or derived from your inputs.
Key negotiation points on IP:
- Your Outputs, Your Property: Google’s standard terms say they won’t claim ownership of content you or the AI create using their services. Make sure the contract explicitly reflects this. All AI-generated output produced for your prompts should be owned by you (or licensed exclusively to you). This is critical if that output includes business plans, software code, marketing copy, or proprietary material. You don’t want ambiguity here.
- No Google Reuse of Your Outputs: While Google will likely reserve rights to the underlying model and may state that similar outputs could be generated for others (since multiple users might ask similar things), insist on a clause that Google will not intentionally use or provide your specific outputs to another customer. Essentially, what’s produced for you stays confidential, as if you wrote it yourself. (Google’s policy is that they may generate similar content for others independently, but they shouldn’t be reusing your exact prompts or answers.)
- Fine-Tuned Models: If you fine-tune or train Google’s models on your data (e.g., create a custom model variant with Vertex AI), clarify ownership and access rights. Google’s documents indicate that only the customer can access their fine-tuned models. Negotiate to cement this: Only your company can use or authorize the fine-tuned model. Google should not use your fine-tuned model to benefit other customers. After contract termination, you should have the right to delete or hand over that model instance (if technically feasible).
- Derivative Works: Be cautious of any clause that says Google owns “derivative works” of the service. Using their model might be considered a derivative work of their IP – carve out your data and outputs. Make it clear that while Google owns the base models and improvements to them, anything unique from your data or custom training is your intellectual property (or at least exclusively licensed to you). If you develop any code or workflows around their model, those should also be yours.
- IP Indemnity: This overlaps with indemnification (covered later), but it’s part of IP negotiations: Require that Google indemnifies (protects) you against IP infringement claims arising from using their models. For example, if a third party claims the model’s output infringed their copyright, Google should defend you. Google has publicly committed to such indemnification for generative AI, but ensure it’s written into your contract in plain language.
- Examples & Use Cases: To drive the point, use examples in negotiation: “We will be feeding proprietary financial data to fine-tune this model to advise our strategy. The output models and content must be owned solely by [Your Company]. We cannot risk Google sharing or reusing this in any form.” Or, “If the AI writes code for us, we need to own that code. We’ll need a warranty that using that code won’t infringe someone else’s IP, or if it does, Google will cover us.” This makes your expectations concrete.
- Patent Rights: Also consider patent implications. If your use of the AI generates a novel solution that you patent, clarify that nothing in the Google contract prevents you from doing so. Conversely, ensure Google isn’t inserting a clause that gives them a license to your patents or inventions that arise from using their service. Standard cloud contracts usually don’t, but with AI it’s new territory – better to spell it out.
Be relentless in protecting your IP. Never assume “it’ll be fine.” Get explicit ownership language. The contract should leave no doubt that your data remains yours, and anything the AI produces for you can be used and commercialized by you freely, with Google having no claim over it.
Data Handling, Privacy, and Residency
Generative AI often involves sending sensitive data to the cloud service – prompts may contain proprietary info or personal data. Thus, data handling and privacy terms are non-negotiable priorities.
Google will have standard data processing terms, but you should bolster them to fit your enterprise needs:
- No Data Use for Training (Privacy Guarantee): It must be clear that Google will not use your inputs or outputs to improve its models or services (at least for paid enterprise services). Google’s policy for paid AI APIs is that they don’t use customer prompts or responses to train the underlying models. Ensure your contract explicitly references this: Any data you send and any content generated for you are only used to fulfill your requests and not to enrich Google’s AI. If any part of the service involves human review (for content safety, etc.), you want the option to opt out or have strict controls on that.
- Data Residency and Localization: If your industry or region has data residency requirements (for example, EU GDPR restrictions on personal data leaving the EU), negotiate where your data will be processed and stored. Google Cloud can often specify regions for data storage. Insist that the data you send and the AI model processing happen in allowed regions. If the generative AI service doesn’t yet allow regional isolation (some advanced models might initially only be in US data centers), consider delaying adoption or getting a contract commitment that a compliant solution will be provided by a certain date. All data at rest should be encrypted and remain within your chosen jurisdictions.
- Confidentiality: Include a strong confidentiality clause covering your prompts and outputs. Even though it might be machine-generated text, it could contain sensitive info. Treat all data exchanged with the AI as confidential information. Google should treat it with the same care as any customer’s confidential data: proper access controls, employee training, and legal obligation to keep it secret. The contract’s confidentiality provisions should apply to all content you provide or receive.
- Data Storage and Deletion: Clarify what data is stored by Google and for how long. Ideally, you want minimal retention. For instance, does Google store prompts or chat histories? They might log requests for a short period for abuse detection or debugging. Negotiate the retention timeline – e.g. logs/prompt data should be auto-deleted after X days or upon request. Also, ensure that upon termination of the contract, Google will delete or return all your data, including any cached AI prompts or fine-tuning data you uploaded. This should align with your data retention policies.
- Personal Data and Compliance: If you will input any personal data (PII) into the AI, insist on a Data Processing Addendum (DPA) that complies with privacy laws (GDPR, CCPA, etc.). Google’s standard DPA for Cloud should also cover generative AI, naming Google as a processor acting on your instructions. Check for specifics: Will Google’s sub-processors have access? Where are they located? You might need to sign Standard Contractual Clauses (for EU-US data transfer) – ensure Google is willing to do that. Treat this as you would any cloud handling sensitive data: all required privacy documentation and agreements must be in place.
- Monitoring and Audit (Security): You may request the right to audit data security measures or at least receive certification. Google Cloud typically has third-party audits (ISO 27001, SOC2, etc.) – demand the right to see those reports. Get commitments if you have particular security requirements (encryption standards, access logging). For example, “All access to our data by Google personnel will be logged and reported to us on request.” While Google won’t customize the whole platform for one client, they can sometimes add contract language to assure you of key points.
- Data Residency Example: If you’re a European bank, you might say: “We cannot allow personal data to be processed outside EU boundaries. We need contractual assurance that our prompts and any data derived will stay on EU servers. If Google’s generative AI is currently only hosted in the US, we need a timeline for EU deployment or a legal workaround. Otherwise, we simply cannot proceed due to GDPR.” This puts the onus on Google to find a solution or concede the point.
Above all, don’t compromise on data protection. Your enterprise’s crown jewels (data) will flow through this system. The contract must ensure that Google acts purely as a custodian and processor of your data – nothing more. No analyzing it, no sharing it, and no keeping it longer than necessary.
SLAs, Uptime, and Model Performance Guarantees (Retraining Promises)
Generative AI is useless to an enterprise if it’s frequently down or if the model’s quality degrades without recourse. While many AI services are new (sometimes labeled beta), you should negotiate service level agreements (SLAs) as rigorously as for any critical cloud service.
This includes uptime, support responsiveness, and even model performance commitments:
- Uptime SLA: Determine how critical this AI service is to your business processes. If it’s in the user-facing production path (e.g., part of your app or customer support), push for a high uptime SLA. Google’s cloud services often offer around 99.9% uptime for production APIs. If that’s standard, try to get 99.99% (especially if you’re a large client with clout). The SLA should specify the monthly uptime percentage and define outages clearly. Also, negotiate the remedy: typically, credits on your bill. Ensure the credits are meaningful – e.g,. If the SLA drops by a certain amount, you get a proportional service credit or even the right to terminate if severe failures persist.
- Response Time and Throughput: Classic SLAs cover uptime (availability), but you might also care about latency and performance for AI APIs. If you have real-time requirements, ask for a stated response time SLO (service level objective) – e.g., “95% of requests will return in under 2 seconds.” Google might be hesitant to formalize this, but at least get them to commit to a performance baseline you observed in testing. If not in the contract, both sides acknowledge it in a technical addendum. Similarly, if you need the ability to process X requests per second, ensure that is documented as a supported capacity.
- Model Quality and Retraining: While quantifying AI “quality” is hard, you can still address it. You might state expectations such as the model used (e.g., PaLM 2 or Gemini X version) will not be arbitrarily changed to a less capable one. If Google updates the model (they likely will over time), you want notification and ideally improvements, not regressions. Negotiate a clause that if a model update leads to materially worse results for your use case, you can either revert or get support from Google to re-tune or adjust to fix it. If you are fine-tuning a model with your data, ensure Google will redo the fine-tuning on new model versions as part of the service (or at least provide tools for you to do it easily). You don’t want to be stuck on an old model or forced to manually rebuild everything whenever Google rolls out an update.
- Retraining Guarantees: If your use of AI relies on continuous learning (for example, you supply feedback or new data to improve responses), clarify if Google’s service supports that and what they will do. They likely won’t custom-train their base model for you (unless it’s a large deal with a custom arrangement), but if you need a model refreshed on new data periodically, see if that can be written in. Alternatively, if you fine-tune a model on Vertex AI, you can set an expectation of how often you’ll refresh it and that Google will assist if needed. The key is to avoid stagnation – ensure the model doesn’t fall behind your needs over the contract life.
- Support SLA: Along with uptime, get a support response SLA. If you have an issue (outage, bug, incorrect behavior), how fast will Google respond and resolve it? For enterprise, you should have 24/7 support. Ensure the contract lists your support tier (likely Premier Support or equivalent) and targets for response (e.g., critical issue response within 1 hour, resolution or workaround in 24 hours, etc.). If the AI is giving wrong outputs or hallucinating badly, you need a way to escalate that to Google’s technical teams.
- Penalties and Escape Hatches: If Google fails to meet SLAs repeatedly, negotiate consequences beyond credits. For example, if they miss SLA 3 months in a row, you should have the right to terminate the contract without penalty. This holds them accountable. Additionally, you may get increasing service credits for each percentage point of uptime below the SLA. The goal is to create a real incentive for reliability – credits alone (which might be capped) are often just symbolic, so push for stronger terms if you can.
- Example (SLA negotiation): “We plan to use this in our customer-facing app, so a downtime hits our revenue and reputation. We need 99.9% uptime guaranteed. Less than that in a given month should result in significant service credits. If uptime falls below 98% in any month, we want the option to terminate. Also, we expect a certain query latency – currently in our tests it’s ~1 second. We’d like an assurance that the service will stay in that ballpark and not suddenly slow down.” Even if Google won’t put latency in SLA, putting it on the table signals your expectations.
In summary, AI services should be treated like any mission-critical service. Don’t accept “it’s AI, no guarantees.” Google is running this service, so they can and should commit to reliability. Lock in the highest service levels you can, and ensure there are remedies if those levels aren’t met. Your business might be riding on this service, and downtime or poor performance is unacceptable because it’s a fancy new AI.
Audit Rights, Indemnity, and Liability Caps
This section is about risk management—ensuring Google stands behind its product and you’re not left holding the bag if things go wrong. Large vendors often try to limit their liability sharply; you, as the customer, need to push those boundaries to a fair place.
Here’s what to focus on:
- Audit and Inspection Rights: If you are in a regulated industry (finance, healthcare, government), regulators might require that you audit your vendors. Most cloud providers resist direct customer audits of their infrastructure for understandable reasons (security, multi-tenant complexities). However, you can negotiate to receive audit reports and participate in reviews. At minimum, ensure the contract obligates Google to provide you with its annual security and compliance reports (SOC 2, ISO certifications, etc.) and respond to reasonable questionnaires about its controls. In some cases, clients have negotiated limited audit rights – for instance, the right for an agreed-upon independent third party to inspect Google’s relevant operations for compliance with the contract (with strict confidentiality). If this is crucial for you, bring it up early. You might settle for a commitment that Google will meet specific frameworks (like FedRAMP, HIPAA, etc., as applicable) and notify you if any lapse.
- Indemnification (IP and Data): Indemnity is your protection if using Google’s AI causes a third party to sue or if a breach occurs. Google has announced industry-leading indemnification for generative AI: They cover you if their training data or the model’s output leads to an IP infringement claim. Make sure your contract explicitly includes:
- IP Indemnity for Training Data: If someone claims Google’s model was built on stolen data, Google will handle it. You should not be on the hook for how the model was trained. Google’s commitment is to indemnify customers for any IP claims over the training data used in their models. Get that in writing in your agreement.
- IP Indemnity for Outputs: If someone claims that content generated by the AI for you infringes on their copyright (say the model output unintentionally reproduced lyrics or code), Google has stated they will indemnify customers on this as well, as long as you weren’t intentionally abusing the AI to copy stuff. Again, ensure the contract spells this out: Google will defend and indemnify you against third-party claims that using the generative AI service or its outputs infringes on IP rights. This is a critical new protection; don’t let it be glossed over.
- Indemnity for Data Breach or Misuse: Harder to get, but worth trying – if Google mishandles your data (e.g., an employee misuses it or there’s a security breach on their side), you want them to indemnify you for resulting damages. The contract should have them liable for breach of confidentiality. Many cloud contracts limit or exclude this from indemnity but push to include it. At the very least, if a data breach of their system causes regulatory fines or lawsuits against you, Google should cover those costs. Use your leverage here; you can argue this strongly if you’re a big client.
- Liability Caps: Google will almost certainly have a clause capping its liability (often something like the fees you paid in the last 12 months). This is never ideal from a customer perspective, but it’s common. Negotiate the cap up if you can. For example, if you anticipate using $5M of services a year, a cap of $5M might be too low, given the potential damages of a major incident. Try to get it to multiple years’ fees or a fixed higher amount. More importantly, carve out certain liabilities from the cap:
- Unlimited Liability for IP and Confidentiality: Many contracts allow uncapped liability for breaches of confidentiality and intellectual property infringement. Argue that Google’s indemnity obligations (for IP and data breaches) should be outside the cap – they should pay whatever it costs if those events occur. It’s unfair for them to say “we indemnify you” and then limit that to a small dollar figure. Press for uncapped or a significantly higher cap for those specific areas.
- Bodily Injury / Tangible Damage: This is unlikely to be relevant to AI text output, but sometimes, if the AI is used in a way that could cause physical harm (e.g., healthcare advice), ensure any injury or death caused by service failures is on them. This is usually a standard carve-out (for any product, the vendor is liable for personal injury caused by their negligence).
- Third-Party Dependencies Liability: If Google’s service incorporates third-party models or data (for example, perhaps some model from a partner in their Model Garden), clarify who indemnifies for what. Google should take responsibility for any third-party components they provide as part of the service. Don’t let them pass the buck, saying you must go after that third party. Your contract is with Google – they must flow down any necessary indemnities from their providers to you.
- Indemnity Process: Also check the fine print of indemnification clauses—usually, you must notify Google promptly of any claim and allow them to control the defense, etc. Those are standard; just be aware and follow them if needed. The key is the scope of what’s covered, as discussed above.
- Example (Indemnity negotiation): “We appreciate Google’s public statement on AI indemnification. We need our contract to explicitly say that Google will indemnify and defend [Your Company] for any claims of intellectual property infringement arising from our use of the generative AI service, including claims related to the model’s training data or output. Additionally, if any confidential data we provide is leaked due to Google’s breach, Google should cover our costs (like regulatory fines or customer notifications). We cannot proceed without these risk mitigations – this is non-negotiable.” This sets a firm stance.
- Overall Risk Posture: Convey to Google that as an enterprise customer, you will not accept a deal that offloads all risk to you. Google must take responsibility for its technology. If they truly believe in their AI, they should support it with strong commitments. Use analogies: you wouldn’t buy a car that the manufacturer refuses to warranty at all – same logic here.
Finally, be mindful of mutual indemnities: Google might ask you to indemnify them if, say, you use the service to do something illegal or infringe on others (e.g., you fine-tune the model with data you didn’t have rights to). That’s fair – just ensure it’s reasonable and narrow in scope. Don’t agree to indemnify Google for broad things beyond your control.
Integration Costs and Third-Party Dependencies
Adopting Google’s generative AI isn’t just about signing the contract and calling the API – there’s typically a whole ecosystem to integrate and costs associated with that. In negotiations, go beyond the core service and consider the total solution.
Where possible, have Google share some of the integration burden or at least acknowledge it:
- Professional Services and Onboarding: If using generative AI at an enterprise scale, you might require help to get started – integrating the API into your applications, tuning models, migrating data, etc. Ask Google what onboarding assistance they provide. Negotiate for free or discounted professional services hours to accelerate your deployment. For example, large deals often include a “Customer Success” package or some engineering support. If not offered, request a certain number of consultation hours, architectural reviews, or even a pilot project with Google’s AI experts. Get help baked into the deal so you’re not left paying a systems integrator entirely out of pocket.
- Credits for Related Services: Generative AI might not stand alone. You may need to use other Google Cloud services with it – e.g., Cloud Storage for training data, BigQuery for storing prompts/results, or Kubernetes for deploying apps that use the AI. Use this to your advantage: ask for cloud credits or discounts on those ancillary services. They should sweeten the pot on the supporting infrastructure if you’re committing budget to Google AI. This also ensures you can build the end-to-end solution without ballooning costs.
- Third-Party Tools: Identify if any third-party software or tools are needed in the solution. For instance, do you need a separate vector database, an annotation tool, or a specific application framework to fully utilize the AI? If Google has partner solutions (from their Marketplace or recommended partners), see if they will facilitate that. They might bundle a partner product at a discount or coordinate a better rate for you. Make it clear that the overall success requires those pieces, too. As a negotiator, you can indirectly negotiate those through Google (“We also need Product X for this to work – can Google help us get a good deal on that since it’s part of the solution you’re proposing?”).
- APIs and Connectors: Ensure that any integration connectors or APIs you need are included. For example, if you want to integrate generative AI with your internal systems (CRM, databases), do you need to use Google’s integration services or additional APIs (like Apigee, etc.)? If yes, negotiate those licenses or costs now. Don’t let them surprise you later that connecting System A to the AI requires an extra fee. A holistic contract should cover all necessary components.
- Customization and Feature Requests: Maybe you need a certain feature (like a higher character limit per request or a specialized model for your industry). Discuss these during the negotiation. If it’s on Google’s roadmap, get a commitment (even if tentative) about timelines or at least a clause that if that feature isn’t delivered by X date, you can adjust the contract. While they may not agree to tie a feature to contract terms, raising it sets expectations. Alternatively, ask for a most-favored-customer clause on new features – e.g., if they roll out a better model or capability, you get access under your current pricing. This prevents a situation where they unveil the “Gemini Ultra model” and charge a lot more – you’d have leverage to say it should be part of your deal or at a fair add-on price.
- Hidden Costs: If your application pulls large amounts of data from Google Cloud, watch out for network egress fees or data transfer costs. If your AI usage will result in a lot of data being moved, consider negotiating flat or waived data transfer fees. Similarly, if you require VPN or dedicated interconnect for secure connectivity to Google, that might be another cost—bring it into the conversation if relevant.
- Support for Third-Party Models: Google’s Vertex AI might allow third-party or open-source models (like Meta’s Llama, etc.). If part of your strategy is to use a mix of models, clarify support. For example, “We may want to use Google’s PaLM for general tasks but a specialized third-party model for medical data. How is it licensed and supported if we do that within Vertex AI?” Ensure that if you use non-Google models through their platform, you aren’t violating anything and’ll still have a unified support channel. If a third-party model fails, you don’t want Google and the other vendor pointing fingers at each other – you want Google to help manage it since it’s on their platform.
- Example (Integration incentives): “To successfully implement this, we anticipate needing help integrating with our internal systems and possibly using BigQuery to analyze the outputs. We’d like Google to provide 100 hours of an AI engineer’s time to assist our team in the first quarter and an upfront $50k in Cloud credits to cover any ancillary services during our pilot phase. This will ensure we hit the ground running. Otherwise, the cost and risk of integration is all on us – we need Google to have some skin in the game for implementation success.” Cloud providers often have funds for this (sometimes called deployment or adoption funds), so it’s very much on the table if you ask.
Remember, a fancy AI API means nothing if you can’t integrate it into your business workflows. Ensure the contract sets you up for success, with technology access and the support and peripheral tools needed. Google should be a partner in making the whole solution work, not just a seller of API calls.
Exit Strategy, Portability, and Lock-In Risks
One of the biggest strategic concerns for CIOs is avoiding vendor lock-in, especially with something as potentially game-changing as AI. You don’t want to be hostage to Google’s platform or pricing forever if alternatives mature.
From day one, negotiate with the end in mind: ensure you have an exit strategy and as much portability as possible.
- Data Portability: This is non-negotiable – your data must remain yours, and you should be able to get it out. The contract should guarantee that you can export all your input data, any stored outputs, and any model artifacts that are yours (for example, fine-tuning weights or embeddings you generated) at any time, particularly upon termination. Ensure the data is in a standard format when exported. For instance, if you logged all prompts and responses, those logs should be available to download (maybe through an API or provided by support). If you fine-tuned a model, ask if you can get a copy of the fine-tuned model (note: with proprietary models, they might not allow the weights to leave their platform, but it’s worth asking). At the very least, get your training dataset and any configuration used so you can replicate the training elsewhere.
- Assistance with Exit: It might sound cheeky to ask a vendor to help you leave, but large enterprises can negotiate termination assistance clauses. If you decide to exit the service, Google will provide reasonable support to transition off. That could include data export help, running the service for a short overlap period, or cooperating with a new vendor. If this contract is big, you can insert such terms. Ensure a grace period after termination where your data is still accessible. For example, a contract ends, but you have 60-90 days to retrieve data before they delete it. Google’s standard might already offer that, but double-check.
- Avoiding Long Lock-in Commitments: Avoid multi-year commitments that lock you in without escape. It’s okay to sign a 3-year deal (common for enterprise discounts), but bake in some escape hatches:
- Performance-based termination: As mentioned earlier, if SLAs are repeatedly missed or if Google materially changes the service negatively, you should be able to terminate without penalty.
- Change of Control: If (hypothetically) Google decided to spin off or discontinue the AI service, or if regulatory changes make it impossible for you to use it, have clauses that let you out. These scenarios are unlikely, but 2025 and beyond can hold surprises.
- Review Points: In a fast-evolving field, consider a mid-term review. For instance, in a 3-year deal, say at 18 months, you will review pricing and terms to ensure they align with market standards. This isn’t an obligation to change, but at least an acknowledgment that AI changes fast. Perhaps tie it to the emergence of significantly better models – you could say if Google launches a new model that’s 10x more powerful, you can switch to that under the same pricing terms or have an option to renegotiate.
- Cross-Cloud or On-Prem Backup Plan: It might be more of an internal strategy, but mention it to Google to keep the pressure: Indicate you plan to architect your solution cloud-agnostic. For example, you might use an abstraction layer to swap out Google’s API for another. Or you might only use models available elsewhere, too (like open-source ones). You’ll get better terms if Google knows you have a credible Plan B. In negotiation, say, “We need the flexibility to run some of these workloads on-prem or in other clouds for compliance or cost reasons. We’d like your commitment to help us do that if required.” Maybe that means Google offers an on-prem appliance or assures you that your data and prompts can be used to fine-tune a model on another platform if you leave. They may not fully agree, but it sets the expectation that you won’t be stuck.
- IP and Model Portability: If you built a custom model with Google’s tools (e.g., you trained a TensorFlow model on Vertex AI), you should be free to take that model and run it elsewhere. Ensure that nothing in the contract prevents that. The only caution is that if you fine-tuned a Google proprietary model (like PaLM/Gemini), you likely can’t take the base model. However, you could negotiate a license to use the fine-tuned model for a transition period outside Google’s cloud. This is tricky, but maybe something like: Google will host the model inference for you for a limited time after termination. At the same time, you find a replacement (that’s another form of exit assistance).
- Lock-in Transparency: Ask direct questions during negotiation: “If we switch to a different AI platform in two years, what support will Google provide? Have other customers done it?” Gauge their response. Often, raising it will make them more likely to accommodate reasonable requests because they know you’re thinking ahead.
- Documentation and Process: Ensure the contract or SLA document contains how you retrieve your data or terminate. It should not be a vague “you can get your data.” Spell out if possible: “Upon request, Google will provide all Customer Data and generated content in JSON format via secure cloud storage. Google will certify deletion of Customer Data from its systems 60 days after contract end.” This level of detail is ideal so there’s no dispute later.
- Example (Portability clause): “We require a clear exit strategy. If we choose to leave, Google must export all our data (prompts, outputs, and fine-tuned model parameters) and assist in transitioning the service. We’d like a 90-day period after termination where the service remains operational (or a subset of it) to ensure a smooth migration. Additionally, any configurations or code we developed on Vertex AI should run on standard open-source platforms so we can port it if needed.” This tells Google you won’t tolerate being trapped.
In essence, hope for the best, but plan for the worst. The worst (from a vendor perspective) is you leaving – and you must ensure that if that day comes, you can do so cleanly. By negotiating these terms up front, you also message Google that they must continue to earn your business, not take it for granted once you’re signed.
Final Advice: Negotiating a generative AI contract with Google is not just a legal exercise but a strategic one. Be firm, do your homework, and bring in all relevant stakeholders (IT, legal, data governance, security) to cover every angle.
Google’s representatives might push back and say, “These terms are our policy” – don’t accept that at face value. Almost everything is negotiable if your business is valuable to them. Use competition as leverage, and don’t be afraid to walk away if the deal doesn’t meet your critical needs.
Practical tip: Document every promise or statement made during negotiations and include it in the contract or an addendum. Verbal assurances like “we don’t usually do X” mean nothing if it’s not in writing. For example, if a Google rep says, “We’ve never had an issue with data leakage,” that’s nice, but we still insist on the contractual data misuse indemnity.
