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Agent Frameworks

Alternative LLM Inference Providers and GPU Cloud Cost Comparison (2026)

1. The Alternative Inference Landscape · March 2026

The market has consolidated around four categories:

Audience: Platform engineers and FinOps leads evaluating workload routing away from AWS Bedrock and Azure OpenAI.

Bottom line up front: For open-weight models (Llama 4, Llama 3.3 70B, Mixtral, DeepSeek), alternative inference providers are consistently 40–200% cheaper than Bedrock on a per-token basis, with materially faster generation speeds. The tradeoff is compliance certification coverage — Bedrock is the only option with FedRAMP authorization and a comprehensive enterprise compliance posture that matches AWS’s shared responsibility model. The architecture question for most platform teams in 2026 is not “Bedrock or alternatives” but “which workloads route where, and how does the gateway manage it.”

The market has consolidated around four categories:

Specialized hardware inference providers — Groq (LPU), Cerebras (wafer-scale CSP), Sambanova (RDU): purpose-built silicon that cannot run arbitrary workloads but achieves generation speeds 5–50x faster than GPU-based inference at competitive per-token prices. Differentiated by latency, not breadth.

Multi-model open inference platforms — Together AI, Fireworks AI, DeepInfra: managed GPU clusters running a broad catalog of open-weight models under a serverless pay-per-token model with optional dedicated endpoints. Differentiated by model selection, fine-tuning capabilities, and SLA options.

Commodity inference marketplaces — Replicate, Perplexity API (search-augmented), OpenRouter: lower operational overhead for the provider, wider model variety, but less predictable latency and weaker enterprise SLAs. Suitable for prototyping and lower-criticality workloads.

GPU cloud platforms — CoreWeave, Lambda Labs, RunPod, Vast.ai: raw compute that you deploy on. Not inference-as-a-service; requires running vLLM, SGLang, or similar. Lowest per-token cost at scale but highest operational overhead.

Summary Comparison Table

Provider Llama 3.3 70B (input/output per 1M) Speed (tok/s) Enterprise SLA HIPAA FedRAMP
AWS Bedrock $2.65 / $2.65 ~30–80 99.9% + SLA docs BAA available Authorized
Groq $0.59 / $0.79 280–394 Enterprise tier BAA available No
Together AI $0.88 flat ~120–180 99.9% (Enterprise) Enterprise plan No
Fireworks AI $0.90 flat ~150–200 99.8% uptime Enterprise plan No
Cerebras $0.60 flat 450–2,300 Enterprise contract Contact sales No
Sambanova Custom (contact) ~700–1,400 Enterprise contract Contact sales No
DeepInfra ~$0.35–$0.55 ~100–150 Best-effort No No

Bedrock pricing source: $2.65/1M vs $0.88 on Together AI — a 201% premium on identical Llama 3.3 70B. Bedrock rates include the managed service overhead and AWS compliance infrastructure.

2. Groq — LPU Hardware, Ultra-Low Latency

What it is

Groq operates its own Language Processing Unit (LPU) silicon — a deterministic, systolic-array architecture that eliminates the memory bandwidth bottlenecks of GPU attention computation. The result is dramatically higher tokens-per-second output without the queuing variability of GPU clusters. As of mid-2026, Groq’s Llama 3.3 70B benchmarks at 276–394 tokens/second, the fastest independently verified speed for any hosted provider of this model.

Current pricing (June 2026)

Model Input ($/1M) Output ($/1M)
Llama 3.3 70B $0.59 $0.79
Llama 3.1 8B $0.05 $0.08
Mixtral 8x7B $0.24 $0.24
Gemma 2 9B $0.20 $0.20
Llama 4 Scout (17B) $0.11 $0.34
Llama 4 Maverick (17Bx128E) $0.50 $0.77

Groq offers a free tier with rate limits sufficient for development. Paid tiers have no hard rate-limit ceiling (subject to capacity). A modest price restructuring occurred in Q1 2026; the Llama 3.3 70B rate dropped from ~$0.70/$0.80 to the current $0.59/$0.79 as Groq scaled hardware capacity.

Bedrock comparison

Bedrock charges $2.65/1M tokens for Llama 3.3 70B (on-demand). At equivalent throughput, Groq costs 78% less on input tokens and 70% less on output. For a workload generating 500M output tokens/month, that is $395K (Groq) vs $1.325M (Bedrock) — a $930K annual delta before considering that Groq generates output 4–5x faster, reducing wall-clock latency for real-time applications.

When Groq wins

  • Streaming chat and copilot interfaces where first-token and inter-token latency are user-visible. Sub-100ms TTFT on 70B models is not achievable on GPU-based platforms at comparable cost.
  • Agentic loops with many short generation calls — the per-call latency penalty compounds across tool-use chains. A 10-step agent loop at 50 tokens/step runs in ~1.3 seconds on Groq vs ~7–15 seconds on GPU inference.
  • Cost-sensitive open-model workloads replacing GPT-4o for tasks that do not require frontier model capability. Llama 3.3 70B at $0.79/M output vs GPT-4o at $15/M output is a 19x cost reduction for acceptable quality loss on structured tasks.

Enterprise considerations

  • Data residency: US-only as of June 2026. EU data residency is on the enterprise roadmap but not GA. This eliminates Groq for GDPR Article 44 restricted-transfer use cases without adequacy decision coverage.
  • Compliance: SOC 2 Type II certified. HIPAA BAA available on enterprise plans. No FedRAMP authorization. Not suitable for US federal agency workloads.
  • SLA: Uptime SLA available on enterprise tier (specific percentage requires direct negotiation). No public SLA on free/pay-as-you-go plans. Historical uptime from community tracking: 99.5–99.7%, lower than Bedrock’s 99.9% SLA commitment.
  • Model breadth: Groq runs a curated set of open-weight models — it does not host proprietary frontier models (GPT-4, Claude). Pure-play for open models.
  • LiteLLM integration: model: groq/llama-3.3-70b-versatile. Full OpenAI-compatible endpoint, drop-in for any application using the OpenAI SDK.

3. Together AI — Open Model Hosting and Fine-Tuning Platform

What it is

Together AI is a managed inference platform running 200+ open-weight models on owned GPU clusters (H100/H200/A100). It is differentiated from Groq by model breadth, fine-tuning infrastructure, dedicated endpoint options, and a more comprehensive enterprise SLA. Together is the default choice when the workload is open-model but does not have the strict latency requirements that justify Groq.

Current pricing (June 2026)

Serverless inference:

Model Input ($/1M) Output ($/1M)
Llama 4 Scout (17B) $0.18 $0.59
Llama 4 Maverick (17Bx128E) $0.27 $0.85
Llama 3.3 70B Instruct $0.88 $0.88
Mixtral 8x22B Instruct $1.20 $1.20
DeepSeek V3.1 $0.60 $1.70
Qwen 2.5 72B $1.20 $1.20
DBRX Instruct $1.20 $1.20
GPT-OSS 20B (cheapest) $0.05 $0.20

Dedicated endpoints: $6.49/hr per H100 GPU. For a single-H100 Llama 3.3 70B deployment at 1,500 tokens/second sustained, this works out to approximately $0.70–$1.20/1M tokens at 70–80% utilization — competitive with serverless and with guaranteed latency SLA.

Fine-tuning: Per-token training cost billed separately (typically $3–6/1M training tokens depending on model size). Hosting the fine-tuned model on serverless costs the same per-token as the base model.

When Together beats Bedrock

  • Llama 4 family: Together hosts Llama 4 Scout at $0.18/$0.59 vs Bedrock’s pricing for equivalent capability models. For Scout-class tasks (reasoning, summarization, coding), this is a material cost reduction with no quality tradeoff.
  • Fine-tuning on open models: Bedrock supports fine-tuning for a limited subset of models at higher cost and with AWS-specific tooling. Together’s fine-tuning pipeline is simpler (upload JSONL, configure, deploy to serverless endpoint) and the per-token hosting cost post-fine-tune is unchanged.
  • Model experimentation: 200+ model catalog including niche academic models not on Bedrock. Useful for evaluation runs comparing model families.
  • High-volume batch inference: The 99.9% SLA enterprise tier with unlimited tokens and priority GPU access makes Together viable for batch workloads that currently use SageMaker batch transform.

Enterprise features

  • Enterprise tier: Geo-redundant deployment, private VPC, unlimited tokens, priority GPU access, 99.9% uptime SLA, long-term log retention. Requires annual contract.
  • Compliance: SOC 2 Type II. HIPAA coverage on enterprise plan. No FedRAMP. Similar compliance posture to Groq.
  • Data handling: Serverless inference inputs/outputs are not used for model training by default. Enterprise customers get zero-retention data agreements.
  • LiteLLM integration: model: together_ai/meta-llama/Llama-3.3-70B-Instruct-Turbo. Full OpenAI-compatible API.

Dedicated vs serverless tradeoff

Serverless has no fixed cost and scales to zero, but cold-start latency can be 200–800ms on infrequently accessed models. Dedicated endpoints eliminate cold starts and provide predictable P99 latency but cost ~$4,700/month per H100. The break-even is roughly 200M tokens/month for a 70B model at typical request patterns — above that, dedicated is cheaper per token and has better latency guarantees.

4. Fireworks AI — Fast Serverless Inference and FireOptimizer

What it is

Fireworks AI positions as the “fastest serverless inference” platform, with particular strength in function calling accuracy and an optimization pipeline (FireOptimizer) that can reduce inference costs for custom workloads. Its differentiated feature is 99.8% uptime — the highest published uptime in the inference provider market — combined with strong structured output capabilities.

Current pricing (June 2026)

Serverless inference (per 1M tokens, blended):

Model tier Price
Small (≤8B params) $0.20/1M
Medium (8–70B) $0.90/1M
Large (>70B, MoE) $3.00–$4.50/1M

Specific models:

Model Input ($/1M) Output ($/1M)
Llama 3.3 70B $0.90 $0.90
DeepSeek V4 Flash $0.14 $0.28
DeepSeek V4 Pro $1.74 ($0.145 cached) $3.48
Kimi K2 $0.95 ($0.16 cached) $4.00

Batch inference: 50% discount on all serverless rates. A 70B batch job at $0.45/1M token is competitive with any managed option.

On-demand dedicated GPU (for custom deployments):

  • H100 80GB: $7.00/hr
  • H200 141GB: $7.00/hr
  • B200 180GB: $10.00/hr
  • B300 288GB: $12.00/hr

Cache discount: 50% off input tokens on cache hit — significant for RAG and prompt-heavy workloads with stable system prompts.

FireOptimizer

FireOptimizer is Fireworks’s post-training optimization service covering SFT, LoRA adapters (hundreds served concurrently on a single base model), reinforcement fine-tuning, and RL pipelines — all billed at standard per-token rates with no optimization surcharge. For teams that need fine-tuned model variants without the overhead of managing a training pipeline, FireOptimizer provides a lower-friction path than either Together AI’s fine-tuning or Bedrock’s fine-tuning service.

LoRA adapters hosted on Fireworks are particularly cost-effective: a custom adapter is served at the same per-token price as the base model, meaning customization cost is front-loaded to training, not to inference. This contrasts with Bedrock’s fine-tuned model hosting, which carries an additional inference surcharge.

Function calling

Fireworks’s FireFunction model achieves 92.1% multi-tool accuracy — within 2–3 points of GPT-4o and significantly better than raw open-weight models. No additional charge for function calling. For agentic workloads requiring reliable structured output and tool invocation at open-model prices, Fireworks is the leading option.

Enterprise features

  • Enterprise Reserved: Custom-priced dedicated infrastructure with SLAs, priority support, and bring-your-own-cloud (BYOC) deployment in customer’s VPC.
  • Uptime: 99.8% reported uptime, higher than Together AI’s serverless tier.
  • Compliance: SOC 2. HIPAA available on enterprise plan. No FedRAMP.

5. Cerebras and Sambanova — Niche Hardware for Maximum Speed

Cerebras: Wafer-Scale for Generation-Speed-Constrained Workloads

Architecture: Cerebras’s Wafer Scale Engine (WSE-3) integrates an entire silicon wafer as a single chip — 900,000 AI cores and 44GB of on-chip SRAM. Because SRAM has 100x the bandwidth of HBM, Cerebras can serve 70B parameter models without the memory bandwidth bottleneck that limits GPU-based inference. The result: generation speeds that are an order of magnitude faster than GPU inference.

Benchmarks (Llama family):

Model Cerebras tok/s Groq tok/s H100 (vLLM) tok/s
Llama 3.1 8B 2,000–2,500 ~1,250 200–400
Llama 3.1 70B 450–569 280–394 50–120
Llama 3.3 70B ~2,300 (Cerebras-reported) 276–394 50–120

The 2,300 tok/s figure for Llama 3.3 70B represents 6–8x faster generation than Groq and 20–40x faster than H100 GPU inference.

Pricing: $0.60/1M tokens for Llama 3.1 70B on pay-per-token. Enterprise customers negotiate flat monthly contracts (3, 6, or 12-month terms) with dedicated throughput, SLA guarantees, and volume discounts. Enterprise pricing is not published; requires direct engagement.

When Cerebras is the right choice:

  • Voice agents: Applications where generation latency is the audible bottleneck. At 2,300 tok/s, a 200-token response generates in under 100ms — below the threshold of perceivable delay in conversational interfaces.
  • Real-time reasoning: Chain-of-thought workflows where the model reasons through 2,000–5,000 tokens before producing an output. At Groq speeds (280 tok/s), a 3,000-token reasoning chain takes ~10 seconds. At Cerebras speeds (~2,000 tok/s), it takes ~1.5 seconds — enabling synchronous use in user-facing flows that would otherwise require async.
  • High-frequency agentic pipelines: Multi-step agents that generate 50–200 tokens per tool call. At 10 tool calls per request and 150 tokens per call, the difference between 80 tok/s (GPU) and 2,300 tok/s (Cerebras) is 18.75 seconds vs 0.65 seconds.

Limitations:

  • Model catalog is narrow: Cerebras runs specific Llama variants and select open-weight models. No proprietary models. No fine-tuning as a service.
  • No public SLA on API tier. Enterprise contracts include SLA but terms are negotiated.
  • No FedRAMP. HIPAA compliance posture: contact sales.
  • US-only infrastructure as of June 2026.

Sambanova: RDU for Batch and Agentic Inference

Architecture: Sambanova’s Reconfigurable Dataflow Unit (RDU) uses a dataflow architecture that maps model execution directly onto processor topology, minimizing data movement — the primary cost in large-model inference. The SN50 (released Q2 2026) adds higher network bandwidth and improved multi-chip scalability over the SN40L.

A notable 2026 development: Sambanova and Intel announced a heterogeneous inference architecture combining Intel Xeon 6 (CPU, prefill stage), GPU (optional), and RDU (decode stage). This pipeline targets agentic workloads where the prefill:decode ratio is uneven — RDU handles the decode bottleneck while Xeon handles prefill at lower cost. GA expected H2 2026.

Performance (SN40L, Llama family):

  • Llama 3.1 70B: ~700–900 tok/s generation
  • Llama 3.1 405B: ~130 tok/s — competitive for a 405B model that would require multi-node GPU setups

Pricing model: Enterprise contracts only. No public pay-per-token pricing. Sambanova targets Fortune 500 deployments with annual contracts that include dedicated inference capacity. Pricing is quoted as cost-per-inference-unit normalized to throughput. For comparison shopping, treat Sambanova as a GPT-4o or Claude Sonnet alternative for enterprise scale with open-weight model quality — the value proposition is throughput SLA at scale, not per-token spot pricing.

When Sambanova makes sense:

  • Large-batch document processing pipelines at enterprise scale (millions of documents/day) where consistent throughput SLA matters more than per-token spot price.
  • Organizations with existing Sambanova hardware contracts or Intel partnership relationships.
  • 405B model workloads where multi-node GPU setups would otherwise be required — Sambanova can serve Llama 3.1 405B on a single RDU chassis.

6. GPU Cloud Platforms for Self-Hosting

Self-hosting with vLLM or SGLang on rented GPU cloud is the lowest per-token cost option at scale but requires DevOps investment. The economics only close above ~200M tokens/month with >50% GPU utilization.

Current H100 pricing (June 2026)

Provider H100 SXM On-Demand H100 SXM Spot A100 80GB On-Demand Notes
CoreWeave $4.25/hr (PCIe) / ~$6.16/hr (SXM HGX node avg) Not widely available $2.00–$2.50/hr Enterprise SLA, Kubernetes-native, lowest spot variability
Lambda Labs $2.99–$3.99/hr Limited $1.99/hr Simple API, less Kubernetes tooling
RunPod $3.44/hr $1.50/hr $1.19–$1.89/hr Marketplace model, high availability variance
Vast.ai $2.49–$3.29/hr $1.55/hr $0.67–$1.20/hr True marketplace (peer-hosted), cheapest but unreliable for production
AWS SageMaker (ml.p4d.24xlarge, 8x A100) ~$6.88/hr normalized per GPU N/A $6.88/hr GPU-normalized Includes managed infra, but 2–3x CoreWeave/Lambda price

H200 and B200 availability: CoreWeave and Lambda Labs offer H200 (141GB HBM3e) and B200 (180GB) in 2026. H200 runs at $5.00–$7.00/hr on-demand; B200 at $10–$12/hr. B200 is significantly faster for large-context inference due to memory bandwidth gains.

Effective cost per million tokens (self-hosted vLLM, Llama 3.3 70B)

At H100 on-demand pricing of $3.50/hr and vLLM throughput of ~1,500 tokens/second (70B model, standard batch):

  • 1,500 tok/s = 5.4B tokens/hour
  • $3.50/hr ÷ 5.4B tokens = $0.65/1M tokens at full utilization

At 70% utilization (realistic sustained load): $0.93/1M tokens At 40% utilization (variable load): $1.62/1M tokens At 20% utilization (dev/staging): $3.25/1M tokens

This means self-hosting breaks even with Together AI ($0.88/1M) only above ~65–70% sustained utilization. Below that threshold, serverless alternatives are cheaper on a per-token basis and have zero operational overhead.

Hidden costs for self-hosting:

  • vLLM/SGLang deployment and tuning: 1–3 engineer-weeks initial, 0.5 engineer/month ongoing
  • Model updates: manual (no automatic updates from model providers)
  • Observability stack: Prometheus + Grafana or equivalent, not included
  • Ingress/egress costs: significant if traffic routes through cloud load balancers
  • On-call burden: GPU health, OOM crashes, CUDA version management

Reserved/committed pricing: CoreWeave and Lambda Labs offer 1-year and 3-year reserved rates. CoreWeave 1-year H100 SXM: ~$2.10/hr (vs $4.25/hr on-demand). At 1-year reserved pricing and 70% utilization, self-hosting Llama 3.3 70B runs at approximately $0.38/1M tokens — materially cheaper than any inference API.

When GPU cloud beats SageMaker

SageMaker charges $6.88/hr per GPU (ml.p4d.24xlarge, 8x A100) — approximately 3x CoreWeave’s on-demand H100 rate and 4x Lambda Labs. The only reasons to stay on SageMaker for inference workloads:

  • Deep integration with existing SageMaker training pipelines and MLflow tracking
  • AWS IAM/VPC network topology requirements
  • SageMaker’s managed model registry and endpoint versioning for regulated deployments

For net-new inference deployments without those constraints, CoreWeave or Lambda Labs with vLLM is materially cheaper. The operational overhead is similar — SageMaker’s “managed” benefit diminishes for teams already running Kubernetes.

7. Multi-Provider Routing with LiteLLM

Architecture

LiteLLM is the standard solution for abstracting multi-provider inference into a single OpenAI-compatible endpoint. It runs as a proxy (self-hosted or via LiteLLM’s managed tier) and handles:

  • Provider abstraction: 100+ providers (OpenAI, Anthropic, Bedrock, Groq, Together, Fireworks, Cerebras, and more) behind a single API
  • Routing: round-robin, least-latency, cost-optimized, or custom rules per model alias
  • Fallback chains: automatic failover when a provider rate-limits or errors
  • Cost tracking: per-request spend logs with provider, model, and team attribution
  • Budget enforcement: per-team or per-model spend limits with automatic fallback or blocking

Sample routing configuration

model_list:
  # Fast/cheap path: Groq for latency-sensitive open-model calls
  - model_name: llama-3.3-70b-fast
    litellm_params:
      model: groq/llama-3.3-70b-versatile
      api_key: os.environ/GROQ_API_KEY
    model_info:
      max_tokens: 32768

  # Standard path: Together AI for open models
  - model_name: llama-3.3-70b-standard
    litellm_params:
      model: together_ai/meta-llama/Llama-3.3-70B-Instruct-Turbo
      api_key: os.environ/TOGETHER_API_KEY

  # Fallback: Bedrock when alternatives fail or for compliance-required workloads
  - model_name: llama-3.3-70b-bedrock
    litellm_params:
      model: bedrock/meta.llama3-3-70b-instruct-v1:0
      aws_region_name: us-east-1

router_settings:
  routing_strategy: latency-based-routing
  num_retries: 2
  fallbacks:
    - llama-3.3-70b-fast:
        - llama-3.3-70b-standard
        - llama-3.3-70b-bedrock
  allowed_fails: 1
  cooldown_time: 30  # seconds before retrying a failed provider

Cost tracking across providers

LiteLLM’s SpendLogs database records per-request cost, provider, model, latency, and team tag. For FinOps reporting:

  • LiteLLM SpendLogs → aggregate by provider to see actual spend vs Bedrock baseline
  • AWS CUR (Cost and Usage Report) → captures Bedrock spend independently; reconcile against LiteLLM logs for Bedrock-routed calls to detect discrepancies
  • LiteLLM’s admin dashboard provides team-level spend breakdowns without requiring CUR integration

Latency-based routing logic

For the Groq/Together/Fireworks tier:

  • Groq first for requests where P50 latency target < 500ms and output < 500 tokens (real-time chat, agent tool calls)
  • Together AI or Fireworks for requests with relaxed latency targets or where Groq doesn’t host the required model variant
  • Bedrock fallback for requests that fail on both alternative providers, or where the request explicitly requires a compliance-covered path (tagged via metadata header)

Compliance routing

Alternative providers (Groq, Together, Fireworks) do not hold FedRAMP authorization as of June 2026. For workloads subject to:

  • FedRAMP: Bedrock only (or Azure Government for Azure OpenAI). No alternative provider is authorized.
  • HIPAA: Groq, Together, and Fireworks all offer BAA on enterprise plans, but the BAA quality and audit regime is less mature than Bedrock’s. For regulated healthcare workloads, validate BAA scope before routing production PHI to alternative providers.
  • SOC 2: All major alternative providers have SOC 2 Type II. Acceptable for most enterprise security reviews.
  • GDPR/data residency: Groq is US-only. Together AI and Fireworks AI have US-primary infrastructure with limited EU presence. For EU data subjects, Bedrock (EU regions) or Azure OpenAI (EU regions) remain the compliant options.
  • PCI DSS / financial services: No alternative provider has PCI DSS certification at infrastructure level. Bedrock inherits AWS’s PCI DSS scope.

Recommended compliance routing rule: Tag requests at ingestion with a compliance_tier metadata field (values: standard, hipaa, fedramp, eu-gdpr). Route fedramp and eu-gdpr directly to Bedrock, bypassing the alternative provider tier. Route hipaa to Bedrock or to alternative providers with signed BAA and no US-EU data transfer. Route standard through the cost-optimized path.

8. Decision Framework

Workload routing decision tree

Is the workload FedRAMP or EU-GDPR regulated?
  YES → Bedrock (or Azure OpenAI for EU)
  NO → continue

Is the model a frontier proprietary model (Claude Sonnet/Opus, GPT-4)?
  YES → Bedrock / Anthropic API / Azure OpenAI
  NO (open model) → continue

Is sub-500ms TTFT or >200 tok/s generation critical?
  YES → Groq (first choice) or Cerebras (for >1,000 tok/s requirement)
  NO → continue

Is fine-tuning required or expected?
  YES → Together AI (best fine-tune-to-inference pipeline) or Fireworks AI (FireOptimizer/LoRA)
  NO → continue

Is function calling accuracy critical (>90%)?
  YES → Fireworks AI (FireFunction, 92.1% multi-tool) or Bedrock (Claude Sonnet)
  NO → continue

Is monthly volume >200M tokens with sustained load?
  YES, with DevOps capacity → GPU cloud (CoreWeave/Lambda + vLLM) at $0.40–$0.90/1M
  YES, no DevOps capacity → Together AI or Fireworks AI serverless
  NO → Together AI or Groq serverless (pay-per-token, zero overhead)

Per-workload recommendations (summary)

Workload Recommended Provider Rationale
Real-time chat / copilot (open model) Groq Lowest TTFT, cheapest 70B output
Batch summarization / extraction Together AI or Fireworks (batch -50%) Low cost, high throughput, no latency SLA needed
Function-calling agentic pipeline Fireworks AI (FireFunction) Best multi-tool accuracy at open-model prices
Fine-tuned model serving Together AI Seamless fine-tune → serverless deploy pipeline
Voice agent / real-time reasoning Cerebras 2,000+ tok/s enables synchronous reasoning
Enterprise batch at 405B scale Sambanova Single-chassis 405B, enterprise throughput SLA
Regulated / FedRAMP / EU workload AWS Bedrock Only compliant option
Frontier model (Claude, GPT-4) Bedrock / Anthropic API No alternative provider hosts these
High-volume open model (>500M tok/mo) GPU cloud (CoreWeave + vLLM) Break-even economics at scale with reserved pricing

9. Cost Modeling Reference

Monthly spend comparison: 1B tokens (500M input, 500M output), Llama 3.3 70B

Provider Input cost Output cost Total vs Bedrock
AWS Bedrock $1,325 $1,325 $2,650 baseline
Together AI $440 $440 $880 -67%
Groq $295 $395 $690 -74%
Fireworks AI $450 $450 $900 -66%
GPU cloud (CoreWeave, 70% utilization) — — ~$930 -65%
GPU cloud (CoreWeave, 1yr reserved, 70% util) — — ~$380 -86%

GPU cloud estimate: H100 1-yr reserved at $2.10/hr, 1,500 tok/s at 70% utilization = $0.38/1M tokens. Does not include DevOps overhead.

When Bedrock’s premium is justified

  • Full AWS IAM/VPC network topology with no external API calls permitted
  • FedRAMP or equivalent federal compliance requirement
  • Frontier model access (Claude Sonnet 4.6, Claude Opus 4.6) — no alternative
  • Multi-modal workloads using Bedrock’s Nova or Titan models
  • Organizations with AWS Enterprise Discount Program (EDP) credits that make Bedrock effectively discounted

10. Sources and Data Currency

Pricing data reflects June 2026 published rates. LLM pricing is volatile — verify before budget commitments.